how to prepare for data science case study interview

2024 Guide: 20+ Essential Data Science Case Study Interview Questions

Case studies are often the most challenging aspect of data science interview processes. They are crafted to resemble a company’s existing or previous projects, assessing a candidate’s ability to tackle prompts, convey their insights, and navigate obstacles.

To excel in data science case study interviews, practice is crucial. It will enable you to develop strategies for approaching case studies, asking the right questions to your interviewer, and providing responses that showcase your skills while adhering to time constraints.

The best way of doing this is by using a framework for answering case studies. For example, you could use the product metrics framework and the A/B testing framework to answer most case studies that come up in data science interviews.

There are four main types of data science case studies:

• Product Case Studies - This type of case study tackles a specific product or feature offering, often tied to the interviewing company. Interviewers are generally looking for a sense of business sense geared towards product metrics.
• Data Analytics Case Study Questions - Data analytics case studies ask you to propose possible metrics in order to investigate an analytics problem. Additionally, you must write a SQL query to pull your proposed metrics, and then perform analysis using the data you queried, just as you would do in the role.
• Modeling and Machine Learning Case Studies - Modeling case studies are more varied and focus on assessing your intuition for building models around business problems.
• Business Case Questions - Similar to product questions, business cases tackle issues or opportunities specific to the organization that is interviewing you. Often, candidates must assess the best option for a certain business plan being proposed, and formulate a process for solving the specific problem.

How Case Study Interviews Are Conducted

Oftentimes as an interviewee, you want to know the setting and format in which to expect the above questions to be asked. Unfortunately, this is company-specific: Some prefer real-time settings, where candidates actively work through a prompt after receiving it, while others offer some period of days (say, a week) before settling in for a presentation of your findings.

It is therefore important to have a system for answering these questions that will accommodate all possible formats, such that you are prepared for any set of circumstances (we provide such a framework below).

Why Are Case Study Questions Asked?

Case studies assess your thought process in answering data science questions. Specifically, interviewers want to see that you have the ability to think on your feet, and to work through real-world problems that likely do not have a right or wrong answer. Real-world case studies that are affecting businesses are not binary; there is no black-and-white, yes-or-no answer. This is why it is important that you can demonstrate decisiveness in your investigations, as well as show your capacity to consider impacts and topics from a variety of angles. Once you are in the role, you will be dealing directly with the ambiguity at the heart of decision-making.

Perhaps most importantly, case interviews assess your ability to effectively communicate your conclusions. On the job, data scientists exchange information across teams and divisions, so a significant part of the interviewer’s focus will be on how you process and explain your answer.

Quick tip: Because case questions in data science interviews tend to be product- and company-focused, it is extremely beneficial to research current projects and developments across different divisions , as these initiatives might end up as the case study topic.

How to Answer Data Science Case Study Questions (The Framework)

There are four main steps to tackling case questions in Data Science interviews, regardless of the type: clarify, make assumptions, gather context, and provide data points and analysis.

Step 1: Clarify

Clarifying is used to gather more information . More often than not, these case studies are designed to be confusing and vague. There will be unorganized data intentionally supplemented with extraneous or omitted information, so it is the candidate’s responsibility to dig deeper, filter out bad information, and fill gaps. Interviewers will be observing how an applicant asks questions and reach their solution.

For example, with a product question, you might take into consideration:

• What is the product?
• How does the product work?
• How does the product align with the business itself?

Step 2: Make Assumptions

When you have made sure that you have evaluated and understand the dataset, start investigating and discarding possible hypotheses. Developing insights on the product at this stage complements your ability to glean information from the dataset, and the exploration of your ideas is paramount to forming a successful hypothesis. You should be communicating your hypotheses with the interviewer, such that they can provide clarifying remarks on how the business views the product, and to help you discard unworkable lines of inquiry. If we continue to think about a product question, some important questions to evaluate and draw conclusions from include:

• Who uses the product? Why?
• What are the goals of the product?
• How does the product interact with other services or goods the company offers?

The goal of this is to reduce the scope of the problem at hand, and ask the interviewer questions upfront that allow you to tackle the meat of the problem instead of focusing on less consequential edge cases.

Step 3: Propose a Solution

Now that a hypothesis is formed that has incorporated the dataset and an understanding of the business-related context, it is time to apply that knowledge in forming a solution. Remember, the hypothesis is simply a refined version of the problem that uses the data on hand as its basis to being solved. The solution you create can target this narrow problem, and you can have full faith that it is addressing the core of the case study question.

Keep in mind that there isn’t a single expected solution, and as such, there is a certain freedom here to determine the exact path for investigation.

Step 4: Provide Data Points and Analysis

Finally, providing data points and analysis in support of your solution involves choosing and prioritizing a main metric. As with all prior factors, this step must be tied back to the hypothesis and the main goal of the problem. From that foundation, it is important to trace through and analyze different examples– from the main metric–in order to validate the hypothesis.

Quick tip: Every case question tends to have multiple solutions. Therefore, you should absolutely consider and communicate any potential trade-offs of your chosen method. Be sure you are communicating the pros and cons of your approach.

Note: In some special cases, solutions will also be assessed on the ability to convey information in layman’s terms. Regardless of the structure, applicants should always be prepared to solve through the framework outlined above in order to answer the prompt.

The Role of Effective Communication

There have been multiple articles and discussions conducted by interviewers behind the Data Science Case Study portion, and they all boil down success in case studies to one main factor: effective communication.

All the analysis in the world will not help if interviewees cannot verbally work through and highlight their thought process within the case study. Again, interviewers are keyed at this stage of the hiring process to look for well-developed “soft-skills” and problem-solving capabilities. Demonstrating those traits is key to succeeding in this round.

To this end, the best advice possible would be to practice actively going through example case studies, such as those available in the Interview Query questions bank . Exploring different topics with a friend in an interview-like setting with cold recall (no Googling in between!) will be uncomfortable and awkward, but it will also help reveal weaknesses in fleshing out the investigation.

Don’t worry if the first few times are terrible! Developing a rhythm will help with gaining self-confidence as you become better at assessing and learning through these sessions.

Finding the right data science talent for case studies? OutSearch.ai ’s AI-driven platform streamlines this by pinpointing candidates who excel in real-world scenarios. Discover how they can help you match with top problem-solvers.

Product Case Study Questions

With product data science case questions , the interviewer wants to get an idea of your product sense intuition. Specifically, these questions assess your ability to identify which metrics should be proposed in order to understand a product.

1. How would you measure the success of private stories on Instagram, where only certain close friends can see the story?

Start by answering: What is the goal of the private story feature on Instagram? You can’t evaluate “success” without knowing what the initial objective of the product was, to begin with.

One specific goal of this feature would be to drive engagement. A private story could potentially increase interactions between users, and grow awareness of the feature.

Now, what types of metrics might you propose to assess user engagement? For a high-level overview, we could look at:

• Average stories per user per day
• Average Close Friends stories per user per day

However, we would also want to further bucket our users to see the effect that Close Friends stories have on user engagement. By bucketing users by age, date joined, or another metric, we could see how engagement is affected within certain populations, giving us insight on success that could be lost if looking at the overall population.

2. How would you measure the success of acquiring new users through a 30-day free trial at Netflix?

More context: Netflix is offering a promotion where users can enroll in a 30-day free trial. After 30 days, customers will automatically be charged based on their selected package. How would you measure acquisition success, and what metrics would you propose to measure the success of the free trial?

One way we can frame the concept specifically to this problem is to think about controllable inputs, external drivers, and then the observable output . Start with the major goals of Netflix:

• Acquiring new users to their subscription plan.
• Decreasing churn and increasing retention.

Looking at acquisition output metrics specifically, there are several top-level stats that we can look at, including:

• Conversion rate percentage
• Cost per free trial acquisition
• Daily conversion rate

With these conversion metrics, we would also want to bucket users by cohort. This would help us see the percentage of free users who were acquired, as well as retention by cohort.

3. How would you measure the success of Facebook Groups?

Start by considering the key function of Facebook Groups . You could say that Groups are a way for users to connect with other users through a shared interest or real-life relationship. Therefore, the user’s goal is to experience a sense of community, which will also drive our business goal of increasing user engagement.

What general engagement metrics can we associate with this value? An objective metric like Groups monthly active users would help us see if Facebook Groups user base is increasing or decreasing. Plus, we could monitor metrics like posting, commenting, and sharing rates.

There are other products that Groups impact, however, specifically the Newsfeed. We need to consider Newsfeed quality and examine if updates from Groups clog up the content pipeline and if users prioritize those updates over other Newsfeed items. This evaluation will give us a better sense of if Groups actually contribute to higher engagement levels.

4. How would you analyze the effectiveness of a new LinkedIn chat feature that shows a “green dot” for active users?

Note: Given engineering constraints, the new feature is impossible to A/B test before release. When you approach case study questions, remember always to clarify any vague terms. In this case, “effectiveness” is very vague. To help you define that term, you would want first to consider what the goal is of adding a green dot to LinkedIn chat.

5. How would you diagnose why weekly active users are up 5%, but email notification open rates are down 2%?

What assumptions can you make about the relationship between weekly active users and email open rates? With a case question like this, you would want to first answer that line of inquiry before proceeding.

Hint: Open rate can decrease when its numerator decreases (fewer people open emails) or its denominator increases (more emails are sent overall). Taking these two factors into account, what are some hypotheses we can make about our decrease in the open rate compared to our increase in weekly active users?

Data Analytics Case Study Questions

Data analytics case studies ask you to dive into analytics problems. Typically these questions ask you to examine metrics trade-offs or investigate changes in metrics. In addition to proposing metrics, you also have to write SQL queries to generate the metrics, which is why they are sometimes referred to as SQL case study questions .

6. Using the provided data, generate some specific recommendations on how DoorDash can improve.

In this DoorDash analytics case study take-home question you are provided with the following dataset:

• Customer order time
• Restaurant order time
• Driver arrives at restaurant time
• Order delivered time
• Customer ID
• Amount of discount
• Amount of tip

With a dataset like this, there are numerous recommendations you can make. A good place to start is by thinking about the DoorDash marketplace, which includes drivers, riders and merchants. How could you analyze the data to increase revenue, driver/user retention and engagement in that marketplace?

7. After implementing a notification change, the total number of unsubscribes increases. Write a SQL query to show how unsubscribes are affecting login rates over time.

This is a Twitter data science interview question , and let’s say you implemented this new feature using an A/B test. You are provided with two tables: events (which includes login, nologin and unsubscribe ) and variants (which includes control or variant ).

We are tasked with comparing multiple different variables at play here. There is the new notification system, along with its effect of creating more unsubscribes. We can also see how login rates compare for unsubscribes for each bucket of the A/B test.

Given that we want to measure two different changes, we know we have to use GROUP BY for the two variables: date and bucket variant. What comes next?

8. Write a query to disprove the hypothesis: Data scientists who switch jobs more often end up getting promoted faster.

More context: You are provided with a table of user experiences representing each person’s past work experiences and timelines.

This question requires a bit of creative problem-solving to understand how we can prove or disprove the hypothesis. The hypothesis is that a data scientist that ends up switching jobs more often gets promoted faster.

Therefore, in analyzing this dataset, we can prove this hypothesis by separating the data scientists into specific segments on how often they jump in their careers.

For example, if we looked at the number of job switches for data scientists that have been in their field for five years, we could prove the hypothesis that the number of data science managers increased as the number of career jumps also rose.

• Never switched jobs: 10% are managers
• Switched jobs once: 20% are managers
• Switched jobs twice: 30% are managers
• Switched jobs three times: 40% are managers

9. Write a SQL query to investigate the hypothesis: Click-through rate is dependent on search result rating.

More context: You are given a table with search results on Facebook, which includes query (search term), position (the search position), and rating (human rating from 1 to 5). Each row represents a single search and includes a column has_clicked that represents whether a user clicked or not.

This question requires us to formulaically do two things: create a metric that can analyze a problem that we face and then actually compute that metric.

Think about the data we want to display to prove or disprove the hypothesis. Our output metric is CTR (clickthrough rate). If CTR is high when search result ratings are high and CTR is low when the search result ratings are low, then our hypothesis is proven. However, if the opposite is true, CTR is low when the search result ratings are high, or there is no proven correlation between the two, then our hypothesis is not proven.

With that structure in mind, we can then look at the results split into different search rating buckets. If we measure the CTR for queries that all have results rated at 1 and then measure CTR for queries that have results rated at lower than 2, etc., we can measure to see if the increase in rating is correlated with an increase in CTR.

10. How would you help a supermarket chain determine which product categories should be prioritized in their inventory restructuring efforts?

You’re working as a Data Scientist in a local grocery chain’s data science team. The business team has decided to allocate store floor space by product category (e.g., electronics, sports and travel, food and beverages). Help the team understand which product categories to prioritize as well as answering questions such as how customer demographics affect sales, and how each city’s sales per product category differs.

Check out our Data Analytics Learning Path .

Modeling and Machine Learning Case Questions

Machine learning case questions assess your ability to build models to solve business problems. These questions can range from applying machine learning to solve a specific case scenario to assessing the validity of a hypothetical existing model . The modeling case study requires a candidate to evaluate and explain any certain part of the model building process.

11. Describe how you would build a model to predict Uber ETAs after a rider requests a ride.

Common machine learning case study problems like this are designed to explain how you would build a model. Many times this can be scoped down to specific parts of the model building process. Examining the example above, we could break it up into:

How would you evaluate the predictions of an Uber ETA model?

What features would you use to predict the Uber ETA for ride requests?

Our recommended framework breaks down a modeling and machine learning case study to individual steps in order to tackle each one thoroughly. In each full modeling case study, you will want to go over:

• Data processing
• Feature Selection
• Model Selection
• Cross Validation
• Evaluation Metrics
• Testing and Roll Out

12. How would you build a model that sends bank customers a text message when fraudulent transactions are detected?

Additionally, the customer can approve or deny the transaction via text response.

Let’s start out by understanding what kind of model would need to be built. We know that since we are working with fraud, there has to be a case where either a fraudulent transaction is or is not present .

Hint: This problem is a binary classification problem. Given the problem scenario, what considerations do we have to think about when first building this model? What would the bank fraud data look like?

13. How would you design the inputs and outputs for a model that detects potential bombs at a border crossing?

Additional questions. How would you test the model and measure its accuracy? Remember the equation for precision:

Because we can not have high TrueNegatives, recall should be high when assessing the model.

14. Which model would you choose to predict Airbnb booking prices: Linear regression or random forest regression?

Start by answering this question: What are the main differences between linear regression and random forest?

Random forest regression is based on the ensemble machine learning technique of bagging . The two key concepts of random forests are:

• Random sampling of training observations when building trees.
• Random subsets of features for splitting nodes.

Random forest regressions also discretize continuous variables, since they are based on decision trees and can split categorical and continuous variables.

Linear regression, on the other hand, is the standard regression technique in which relationships are modeled using a linear predictor function, the most common example represented as y = Ax + B.

Let’s see how each model is applicable to Airbnb’s bookings. One thing we need to do in the interview is to understand more context around the problem of predicting bookings. To do so, we need to understand which features are present in our dataset.

We can assume the dataset will have features like:

• Location features.
• Seasonality.
• Number of bedrooms and bathrooms.
• Private room, shared, entire home, etc.
• External demand (conferences, festivals, sporting events).

Which model would be the best fit for this feature set?

15. Using a binary classification model that pre-approves candidates for a loan, how would you give each rejected application a rejection reason?

More context: You do not have access to the feature weights. Start by thinking about the problem like this: How would the problem change if we had ten, one thousand, or ten thousand applicants that had gone through the loan qualification program?

Pretend that we have three people: Alice, Bob, and Candace that have all applied for a loan. Simplifying the financial lending loan model, let us assume the only features are the total number of credit cards , the dollar amount of current debt , and credit age . Here is a scenario:

Alice: 10 credit cards, 5 years of credit age, $\$20K$ in debt

Bob: 10 credit cards, 5 years of credit age, $\$15K$ in debt

Candace: 10 credit cards, 5 years of credit age, $\$10K$ in debt

If Candace is approved, we can logically point to the fact that Candace’s $\$10K$ in debt swung the model to approve her for a loan. How did we reason this out?

If the sample size analyzed was instead thousands of people who had the same number of credit cards and credit age with varying levels of debt, we could figure out the model’s average loan acceptance rate for each numerical amount of current debt. Then we could plot these on a graph to model the y-value (average loan acceptance) versus the x-value (dollar amount of current debt). These graphs are called partial dependence plots.

Business Case Questions

In data science interviews, business case study questions task you with addressing problems as they relate to the business. You might be asked about topics like estimation and calculation, as well as applying problem-solving to a larger case. One tip: Be sure to read up on the company’s products and ventures before your interview to expose yourself to possible topics.

16. How would you estimate the average lifetime value of customers at a business that has existed for just over one year?

More context: You know that the product costs $\$100$ per month, averages 10% in monthly churn, and the average customer stays for 3.5 months.

Remember that lifetime value is defined by the prediction of the net revenue attributed to the entire future relationship with all customers averaged. Therefore, $\$100$ * 3.5 = $\$350$… But is it that simple?

Because this company is so new, our average customer length (3.5 months) is biased from the short possible length of time that anyone could have been a customer (one year maximum). How would you then model out LTV knowing the churn rate and product cost?

17. How would you go about removing duplicate product names (e.g. iPhone X vs. Apple iPhone 10) in a massive database?

See the full solution for this Amazon business case question on YouTube:

18. What metrics would you monitor to know if a 50% discount promotion is a good idea for a ride-sharing company?

This question has no correct answer and is rather designed to test your reasoning and communication skills related to product/business cases. First, start by stating your assumptions. What are the goals of this promotion? It is likely that the goal of the discount is to grow revenue and increase retention. A few other assumptions you might make include:

• The promotion will be applied uniformly across all users.
• The 50% discount can only be used for a single ride.

How would we be able to evaluate this pricing strategy? An A/B test between the control group (no discount) and test group (discount) would allow us to evaluate Long-term revenue vs average cost of the promotion. Using these two metrics how could we measure if the promotion is a good idea?

19. A bank wants to create a new partner card, e.g. Whole Foods Chase credit card). How would you determine what the next partner card should be?

More context: Say you have access to all customer spending data. With this question, there are several approaches you can take. As your first step, think about the business reason for credit card partnerships: they help increase acquisition and customer retention.

One of the simplest solutions would be to sum all transactions grouped by merchants. This would identify the merchants who see the highest spending amounts. However, the one issue might be that some merchants have a high-spend value but low volume. How could we counteract this potential pitfall? Is the volume of transactions even an important factor in our credit card business? The more questions you ask, the more may spring to mind.

20. How would you assess the value of keeping a TV show on a streaming platform like Netflix?

Say that Netflix is working on a deal to renew the streaming rights for a show like The Office , which has been on Netflix for one year. Your job is to value the benefit of keeping the show on Netflix.

Start by trying to understand the reasons why Netflix would want to renew the show. Netflix mainly has three goals for what their content should help achieve:

• Acquisition: To increase the number of subscribers.
• Retention: To increase the retention of active subscribers and keep them on as paying members.
• Revenue: To increase overall revenue.

One solution to value the benefit would be to estimate a lower and upper bound to understand the percentage of users that would be affected by The Office being removed. You could then run these percentages against your known acquisition and retention rates.

21. How would you determine which products are to be put on sale?

Let’s say you work at Amazon. It’s nearing Black Friday, and you are tasked with determining which products should be put on sale. You have access to historical pricing and purchasing data from items that have been on sale before. How would you determine what products should go on sale to best maximize profit during Black Friday?

To start with this question, aggregate data from previous years for products that have been on sale during Black Friday or similar events. You can then compare elements such as historical sales volume, inventory levels, and profit margins.

Learn More About Feature Changes

This course is designed teach you everything you need to know about feature changes:

More Data Science Interview Resources

Case studies are one of the most common types of data science interview questions . Practice with the data science course from Interview Query, which includes product and machine learning modules.

Data science case interviews (what to expect & how to prepare)

Data science case studies are tough to crack: they’re open-ended, technical, and specific to the company. Interviewers use them to test your ability to break down complex problems and your use of analytical thinking to address business concerns.

So we’ve put together this guide to help you familiarize yourself with case studies at companies like Amazon, Google, and Meta (Facebook), as well as how to prepare for them, using practice questions and a repeatable answer framework.

Here’s the first thing you need to know about tackling data science case studies: always start by asking clarifying questions, before jumping in to your plan.

Let’s get started.

• What to expect in data science case study interviews
• How to approach data science case studies
• Sample cases from FAANG data science interviews
• How to prepare for data science case interviews

Click here to practice 1-on-1 with ex-FAANG interviewers

1. what to expect in data science case study interviews.

Before we get into an answer method and practice questions for data science case studies, let’s take a look at what you can expect in this type of interview.

Of course, the exact interview process for data scientist candidates will depend on the company you’re applying to, but case studies generally appear in both the pre-onsite phone screens and during the final onsite or virtual loop.

These questions may take anywhere from 10 to 40 minutes to answer, depending on the depth and complexity that the interviewer is looking for. During the initial phone screens, the case studies are typically shorter and interspersed with other technical and/or behavioral questions. During the final rounds, they will likely take longer to answer and require a more detailed analysis.

While some candidates may have the opportunity to prepare in advance and present their conclusions during an interview round, most candidates work with the information the interviewer offers on the spot.

1.1 The types of data science case studies

Generally, there are two types of case studies:

• Analysis cases , which focus on how you translate user behavior into ideas and insights using data. These typically center around a product, feature, or business concern that’s unique to the company you’re interviewing with.
• Modeling cases , which are more overtly technical and focus on how you build and use machine learning and statistical models to address business problems.

The number of case studies that you’ll receive in each category will depend on the company and the position that you’ve applied for. Facebook , for instance, typically doesn’t give many machine learning modeling cases, whereas Amazon does.

Also, some companies break these larger groups into smaller subcategories. For example, Facebook divides its analysis cases into two types: product interpretation and applied data . 

You may also receive in-depth questions similar to case studies, which test your technical capabilities (e.g. coding, SQL), so if you’d like to learn more about how to answer coding interview questions, take a look here .

We’ll give you a step-by-step method that can be used to answer analysis and modeling cases in section 2 . But first, let’s look at how interviewers will assess your answers.

1.2 What interviewers are looking for

We’ve researched accounts from ex-interviewers and data scientists to pinpoint the main criteria that interviewers look for in your answers. While the exact grading rubric will vary per company, this list from an ex-Google data scientist is a good overview of the biggest assessment areas:

• Structure : candidate can break down an ambiguous problem into clear steps
• Completeness : candidate is able to fully answer the question
• Soundness : candidate’s solution is feasible and logical
• Clarity : candidate’s explanations and methodology are easy to understand
• Speed : candidate manages time well and is able to come up with solutions quickly

You’ll be able to improve your skills in each of these categories by practicing data science case studies on your own, and by working with an answer framework. We’ll get into that next.

2. How to approach data science case studies

Approaching data science cases with a repeatable framework will not only add structure to your answer, but also help you manage your time and think clearly under the stress of interview conditions.

Let’s go over a framework that you can use in your interviews, then break it down with an example answer.

2.1 Data science case framework: CAPER

We've researched popular frameworks used by real data scientists, and consolidated them to be as memorable and useful in an interview setting as possible.

Try using the framework below to structure your thinking during the interview. 

• Clarify : Start by asking questions. Case questions are ambiguous, so you’ll need to gather more information from the interviewer, while eliminating irrelevant data. The types of questions you’ll ask will depend on the case, but consider: what is the business objective? What data can I access? Should I focus on all customers or just in X region?
• Assume : Narrow the problem down by making assumptions and stating them to the interviewer for confirmation. (E.g. the statistical significance is X%, users are segmented based on XYZ, etc.) By the end of this step you should have constrained the problem into a clear goal.
• Plan : Now, begin to craft your solution. Take time to outline a plan, breaking it into manageable tasks. Once you’ve made your plan, explain each step that you will take to the interviewer, and ask if it sounds good to them.
• Execute : Carry out your plan, walking through each step with the interviewer. Depending on the type of case, you may have to prepare and engineer data, code, apply statistical algorithms, build a model, etc. In the majority of cases, you will need to end with business analysis.
• Review : Finally, tie your final solution back to the business objectives you and the interviewer had initially identified. Evaluate your solution, and whether there are any steps you could have added or removed to improve it. 

Now that you’ve seen the framework, let’s take a look at how to implement it.

2.2 Sample answer using the CAPER framework

Below you’ll find an answer to a Facebook data science interview question from the Applied Data loop. This is an example that comes from Facebook’s data science interview prep materials, which you can find here .

Try this question:

Imagine that Facebook is building a product around high schools, starting with about 300 million users who have filled out a field with the name of their current high school. How would you find out how much of this data is real?

First, we need to clarify the question, eliminating irrelevant data and pinpointing what is the most important. For example:

• What exactly does “real” mean in this context?
• Should we focus on whether the high school itself is real, or whether the user actually attended the high school they’ve named?

After discussing with the interviewer, we’ve decided to focus on whether the high school itself is real first, followed by whether the user actually attended the high school they’ve named.

Next, we’ll narrow the problem down and state our assumptions to the interviewer for confirmation. Here are some assumptions we could make in the context of this problem:

• The 300 million users are likely teenagers, given that they’re listing their current high school
• We can assume that a high school that is listed too few times is likely fake
• We can assume that a high school that is listed too many times (e.g. 10,000+ students) is likely fake

The interviewer has agreed with each of these assumptions, so we can now move on to the plan.

Next, it’s time to make a list of actionable steps and lay them out for the interviewer before moving on.

First, there are two approaches that we can identify:

• A high precision approach, which provides a list of people who definitely went to a confirmed high school
• A high recall approach, more similar to market sizing, which would provide a ballpark figure of people who went to a confirmed high school

As this is for a product that Facebook is currently building, the product use case likely calls for an estimate that is as accurate as possible. So we can go for the first approach, which will provide a more precise estimate of confirmed users listing a real high school. 

Now, we list the steps that make up this approach:

• To find whether a high school is real: Draw a distribution with the number of students on the X axis, and the number of high schools on the Y axis, in order to find and eliminate the lower and upper bounds
• To find whether a student really went to a high school: use a user’s friend graph and location to determine the plausibility of the high school they’ve named

The interviewer has approved the plan, which means that it’s time to execute.

4. Execute 

Step 1: Determining whether a high school is real

Going off of our plan, we’ll first start with the distribution.

We can use x1 to denote the lower bound, below which the number of times a high school is listed would be too small for a plausible school. x2 then denotes the upper bound, above which the high school has been listed too many times for a plausible school.

Here is what that would look like:

Be prepared to answer follow up questions. In this case, the interviewer may ask, “looking at this graph, what do you think x1 and x2 would be?”

Based on this distribution, we could say that x1 is approximately the 5th percentile, or somewhere around 100 students. So, out of 300 million students, if fewer than 100 students list “Applebee” high school, then this is most likely not a real high school.

x2 is likely around the 95th percentile, or potentially as high as the 99th percentile. Based on intuition, we could estimate that number around 10,000. So, if more than 10,000 students list “Applebee” high school, then this is most likely not real. Here is how that looks on the distribution:

At this point, the interviewer may ask more follow-up questions, such as “how do we account for different high schools that share the same name?”

In this case, we could group by the schools’ name and location, rather than name alone. If the high school does not have a dedicated page that lists its location, we could deduce its location based on the city of the user that lists it. 

Step 2: Determining whether a user went to the high school

A strong signal as to whether a user attended a specific high school would be their friend graph: a set number of friends would have to have listed the same current high school. For now, we’ll set that number at five friends.

Don’t forget to call out trade-offs and edge cases as you go. In this case, there could be a student who has recently moved, and so the high school they’ve listed does not reflect their actual current high school. 

To solve this, we could rely on users to update their location to reflect the change. If users do not update their location and high school, this would present an edge case that we would need to work out later.

To conclude, we could use the data from both the friend graph and the initial distribution to confirm the two signifiers: a high school is real, and the user really went there.

If enough users in the same location list the same high school, then it is likely that the high school is real, and that the users really attend it. If there are not enough users in the same location that list the same high school, then it is likely that the high school is not real, and the users do not actually attend it.

3. Sample cases from FAANG data science interviews

Having worked through the sample problem above, try out the different kinds of case studies that have been asked in data science interviews at FAANG companies. We’ve divided the questions into types of cases, as well as by company.

For more information about each of these companies’ data science interviews, take a look at these guides:

• Facebook data scientist interview guide
• Amazon data scientist interview guide
• Google data scientist interview guide

Now let’s get into the questions. This is a selection of real data scientist interview questions, according to data from Glassdoor.

Data science case studies

Facebook - Analysis (product interpretation)

• How would you measure the success of a product?
• What KPIs would you use to measure the success of the newsfeed?
• Friends acceptance rate decreases 15% after a new notifications system is launched - how would you investigate?

Facebook - Analysis (applied data)

• How would you evaluate the impact for teenagers when their parents join Facebook?
• How would you decide to launch or not if engagement within a specific cohort decreased while all the rest increased?
• How would you set up an experiment to understand feature change in Instagram stories?

Amazon - modeling

• How would you improve a classification model that suffers from low precision?
• When you have time series data by month, and it has large data records, how will you find significant differences between this month and previous month?

Google - Analysis

• You have a google app and you make a change. How do you test if a metric has increased or not?
• How do you detect viruses or inappropriate content on YouTube?
• How would you compare if upgrading the android system produces more searches?

4. How to prepare for data science case interviews

Understanding the process and learning a method for data science cases will go a long way in helping you prepare. But this information is not enough to land you a data science job offer. 

To succeed in your data scientist case interviews, you're also going to need to practice under realistic interview conditions so that you'll be ready to perform when it counts. 

For more information on how to prepare for data science interviews as a whole, take a look at our guide on data science interview prep .

4.1 Practice on your own

Start by answering practice questions alone. You can use the list in section 3 , and interview yourself out loud. This may sound strange, but it will significantly improve the way you communicate your answers during an interview. 

Play the role of both the candidate and the interviewer, asking questions and answering them, just like two people would in an interview. This will help you get used to the answer framework and get used to answering data science cases in a structured way.

4.2 Practice with peers

Once you’re used to answering questions on your own , then a great next step is to do mock interviews with friends or peers. This will help you adapt your approach to accommodate for follow-ups and answer questions you haven’t already worked through.

This can be especially helpful if your friend has experience with data scientist interviews, or is at least familiar with the process.

4.3 Practice with ex-interviewers

Finally, you should also try to practice data science mock interviews with expert ex-interviewers, as they’ll be able to give you much more accurate feedback than friends and peers.

If you know a data scientist or someone who has experience running interviews at a big tech company, then that's fantastic. But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

Here's the good news. We've already made the connections for you. We’ve created a coaching service where you can practice 1-on-1 with ex-interviewers from leading tech companies. Learn more and start scheduling sessions today .

Related articles:

Data Science Case Study Interview: Your Guide to Success

by Sam McKay, CFA | Careers

Ready to crush your next data science interview? Well, you’re in the right place.

This type of interview is designed to assess your problem-solving skills, technical knowledge, and ability to apply data-driven solutions to real-world challenges.

So, how can you master these interviews and secure your next job?

To master your data science case study interview:

Practice Case Studies: Engage in mock scenarios to sharpen problem-solving skills.

Review Core Concepts: Brush up on algorithms, statistical analysis, and key programming languages.

Contextualize Solutions: Connect findings to business objectives for meaningful insights.

Clear Communication: Present results logically and effectively using visuals and simple language.

Adaptability and Clarity: Stay flexible and articulate your thought process during problem-solving.

This article will delve into each of these points and give you additional tips and practice questions to get you ready to crush your upcoming interview!

After you’ve read this article, you can enter the interview ready to showcase your expertise and win your dream role.

Let’s dive in!

Table of Contents

What to Expect in the Interview?

Data science case study interviews are an essential part of the hiring process. They give interviewers a glimpse of how you, approach real-world business problems and demonstrate your analytical thinking, problem-solving, and technical skills.

Furthermore, case study interviews are typically open-ended , which means you’ll be presented with a problem that doesn’t have a right or wrong answer.

Instead, you are expected to demonstrate your ability to:

Break down complex problems

Make assumptions

Gather context

Provide data points and analysis

This type of interview allows your potential employer to evaluate your creativity, technical knowledge, and attention to detail.

But what topics will the interview touch on?

Topics Covered in Data Science Case Study Interviews

In a case study interview , you can expect inquiries that cover a spectrum of topics crucial to evaluating your skill set:

Topic 1: Problem-Solving Scenarios

In these interviews, your ability to resolve genuine business dilemmas using data-driven methods is essential.

These scenarios reflect authentic challenges, demanding analytical insight, decision-making, and problem-solving skills.

Real-world Challenges: Expect scenarios like optimizing marketing strategies, predicting customer behavior, or enhancing operational efficiency through data-driven solutions.

Analytical Thinking: Demonstrate your capacity to break down complex problems systematically, extracting actionable insights from intricate issues.

Decision-making Skills: Showcase your ability to make informed decisions, emphasizing instances where your data-driven choices optimized processes or led to strategic recommendations.

Your adeptness at leveraging data for insights, analytical thinking, and informed decision-making defines your capability to provide practical solutions in real-world business contexts.

Topic 2: Data Handling and Analysis

Data science case studies assess your proficiency in data preprocessing, cleaning, and deriving insights from raw data.

Data Collection and Manipulation: Prepare for data engineering questions involving data collection, handling missing values, cleaning inaccuracies, and transforming data for analysis.

Handling Missing Values and Cleaning Data: Showcase your skills in managing missing values and ensuring data quality through cleaning techniques.

Data Transformation and Feature Engineering: Highlight your expertise in transforming raw data into usable formats and creating meaningful features for analysis.

Mastering data preprocessing—managing, cleaning, and transforming raw data—is fundamental. Your proficiency in these techniques showcases your ability to derive valuable insights essential for data-driven solutions.

Topic 3: Modeling and Feature Selection

Data science case interviews prioritize your understanding of modeling and feature selection strategies.

Model Selection and Application: Highlight your prowess in choosing appropriate models, explaining your rationale, and showcasing implementation skills.

Feature Selection Techniques: Understand the importance of selecting relevant variables and methods, such as correlation coefficients, to enhance model accuracy.

Ensuring Robustness through Random Sampling: Consider techniques like random sampling to bolster model robustness and generalization abilities.

Excel in modeling and feature selection by understanding contexts, optimizing model performance, and employing robust evaluation strategies.

Become a master at data modeling using these best practices:

Topic 4: Statistical and Machine Learning Approach

These interviews require proficiency in statistical and machine learning methods for diverse problem-solving. This topic is significant for anyone applying for a machine learning engineer position.

Using Statistical Models: Utilize logistic and linear regression models for effective classification and prediction tasks.

Leveraging Machine Learning Algorithms: Employ models such as support vector machines (SVM), k-nearest neighbors (k-NN), and decision trees for complex pattern recognition and classification.

Exploring Deep Learning Techniques: Consider neural networks, convolutional neural networks (CNN), and recurrent neural networks (RNN) for intricate data patterns.

Experimentation and Model Selection: Experiment with various algorithms to identify the most suitable approach for specific contexts.

Combining statistical and machine learning expertise equips you to systematically tackle varied data challenges, ensuring readiness for case studies and beyond.

Topic 5: Evaluation Metrics and Validation

In data science interviews, understanding evaluation metrics and validation techniques is critical to measuring how well machine learning models perform.

Choosing the Right Metrics: Select metrics like precision, recall (for classification), or R² (for regression) based on the problem type. Picking the right metric defines how you interpret your model’s performance.

Validating Model Accuracy: Use methods like cross-validation and holdout validation to test your model across different data portions. These methods prevent errors from overfitting and provide a more accurate performance measure.

Importance of Statistical Significance: Evaluate if your model’s performance is due to actual prediction or random chance. Techniques like hypothesis testing and confidence intervals help determine this probability accurately.

Interpreting Results: Be ready to explain model outcomes, spot patterns, and suggest actions based on your analysis. Translating data insights into actionable strategies showcases your skill.

Finally, focusing on suitable metrics, using validation methods, understanding statistical significance, and deriving actionable insights from data underline your ability to evaluate model performance.

Also, being well-versed in these topics and having hands-on experience through practice scenarios can significantly enhance your performance in these case study interviews.

Prepare to demonstrate technical expertise and adaptability, problem-solving, and communication skills to excel in these assessments.

Now, let’s talk about how to navigate the interview.

Here is a step-by-step guide to get you through the process.

Steps by Step Guide Through the Interview

This section’ll discuss what you can expect during the interview process and how to approach case study questions.

Step 1: Problem Statement: You’ll be presented with a problem or scenario—either a hypothetical situation or a real-world challenge—emphasizing the need for data-driven solutions within data science.

Step 2: Clarification and Context: Seek more profound clarity by actively engaging with the interviewer. Ask pertinent questions to thoroughly understand the objectives, constraints, and nuanced aspects of the problem statement.

Step 3: State your Assumptions: When crucial information is lacking, make reasonable assumptions to proceed with your final solution. Explain these assumptions to your interviewer to ensure transparency in your decision-making process.

Step 4: Gather Context: Consider the broader business landscape surrounding the problem. Factor in external influences such as market trends, customer behaviors, or competitor actions that might impact your solution.

Step 5: Data Exploration: Delve into the provided datasets meticulously. Cleanse, visualize, and analyze the data to derive meaningful and actionable insights crucial for problem-solving.

Step 6: Modeling and Analysis: Leverage statistical or machine learning techniques to address the problem effectively. Implement suitable models to derive insights and solutions aligning with the identified objectives.

Step 7: Results Interpretation: Interpret your findings thoughtfully. Identify patterns, trends, or correlations within the data and present clear, data-backed recommendations relevant to the problem statement.

Step 8: Results Presentation: Effectively articulate your approach, methodologies, and choices coherently. This step is vital, especially when conveying complex technical concepts to non-technical stakeholders.

Remember to remain adaptable and flexible throughout the process and be prepared to adapt your approach to each situation.

Now that you have a guide on navigating the interview, let us give you some tips to help you stand out from the crowd.

Top 3 Tips to Master Your Data Science Case Study Interview

Approaching case study interviews in data science requires a blend of technical proficiency and a holistic understanding of business implications.

Here are practical strategies and structured approaches to prepare effectively for these interviews:

1. Comprehensive Preparation Tips

To excel in case study interviews, a blend of technical competence and strategic preparation is key.

Here are concise yet powerful tips to equip yourself for success:

Practice with Mock Case Studies : Familiarize yourself with the process through practice. Online resources offer example questions and solutions, enhancing familiarity and boosting confidence.

Review Your Data Science Toolbox: Ensure a strong foundation in fundamentals like data wrangling, visualization, and machine learning algorithms. Comfort with relevant programming languages is essential.

Simplicity in Problem-solving: Opt for clear and straightforward problem-solving approaches. While advanced techniques can be impressive, interviewers value efficiency and clarity.

Interviewers also highly value someone with great communication skills. Here are some tips to highlight your skills in this area.

2. Communication and Presentation of Results

In case study interviews, communication is vital. Present your findings in a clear, engaging way that connects with the business context. Tips include:

Contextualize results: Relate findings to the initial problem, highlighting key insights for business strategy.

Use visuals: Charts, graphs, or diagrams help convey findings more effectively.

Logical sequence: Structure your presentation for easy understanding, starting with an overview and progressing to specifics.

Simplify ideas: Break down complex concepts into simpler segments using examples or analogies.

Mastering these techniques helps you communicate insights clearly and confidently, setting you apart in interviews.

Lastly here are some preparation strategies to employ before you walk into the interview room.

3. Structured Preparation Strategy

Prepare meticulously for data science case study interviews by following a structured strategy.

Here’s how:

Practice Regularly: Engage in mock interviews and case studies to enhance critical thinking and familiarity with the interview process. This builds confidence and sharpens problem-solving skills under pressure.

Thorough Review of Concepts: Revisit essential data science concepts and tools, focusing on machine learning algorithms, statistical analysis, and relevant programming languages (Python, R, SQL) for confident handling of technical questions.

Strategic Planning: Develop a structured framework for approaching case study problems. Outline the steps and tools/techniques to deploy, ensuring an organized and systematic interview approach.

Understanding the Context: Analyze business scenarios to identify objectives, variables, and data sources essential for insightful analysis.

Ask for Clarification: Engage with interviewers to clarify any unclear aspects of the case study questions. For example, you may ask ‘What is the business objective?’ This exhibits thoughtfulness and aids in better understanding the problem.

Transparent Problem-solving: Clearly communicate your thought process and reasoning during problem-solving. This showcases analytical skills and approaches to data-driven solutions.

Blend technical skills with business context, communicate clearly, and prepare to systematically ace your case study interviews.

Now, let’s really make this specific.

Each company is different and may need slightly different skills and specializations from data scientists.

However, here is some of what you can expect in a case study interview with some industry giants.

Case Interviews at Top Tech Companies

As you prepare for data science interviews, it’s essential to be aware of the case study interview format utilized by top tech companies.

In this section, we’ll explore case interviews at Facebook, Twitter, and Amazon, and provide insight into what they expect from their data scientists.

Facebook predominantly looks for candidates with strong analytical and problem-solving skills. The case study interviews here usually revolve around assessing the impact of a new feature, analyzing monthly active users, or measuring the effectiveness of a product change.

To excel during a Facebook case interview, you should break down complex problems, formulate a structured approach, and communicate your thought process clearly.

Twitter , similar to Facebook, evaluates your ability to analyze and interpret large datasets to solve business problems. During a Twitter case study interview, you might be asked to analyze user engagement, develop recommendations for increasing ad revenue, or identify trends in user growth.

Be prepared to work with different analytics tools and showcase your knowledge of relevant statistical concepts.

Amazon is known for its customer-centric approach and data-driven decision-making. In Amazon’s case interviews, you may be tasked with optimizing customer experience, analyzing sales trends, or improving the efficiency of a certain process.

Keep in mind Amazon’s leadership principles, especially “Customer Obsession” and “Dive Deep,” as you navigate through the case study.

Remember, practice is key. Familiarize yourself with various case study scenarios and hone your data science skills.

With all this knowledge, it’s time to practice with the following practice questions.

Mockup Case Studies and Practice Questions

To better prepare for your data science case study interviews, it’s important to practice with some mockup case studies and questions.

One way to practice is by finding typical case study questions.

Here are a few examples to help you get started:

Customer Segmentation: You have access to a dataset containing customer information, such as demographics and purchase behavior. Your task is to segment the customers into groups that share similar characteristics. How would you approach this problem, and what machine-learning techniques would you consider?

Fraud Detection: Imagine your company processes online transactions. You are asked to develop a model that can identify potentially fraudulent activities. How would you approach the problem and which features would you consider using to build your model? What are the trade-offs between false positives and false negatives?

Demand Forecasting: Your company needs to predict future demand for a particular product. What factors should be taken into account, and how would you build a model to forecast demand? How can you ensure that your model remains up-to-date and accurate as new data becomes available?

By practicing case study interview questions , you can sharpen problem-solving skills, and walk into future data science interviews more confidently.

Remember to practice consistently and stay up-to-date with relevant industry trends and techniques.

Final Thoughts

Data science case study interviews are more than just technical assessments; they’re opportunities to showcase your problem-solving skills and practical knowledge.

Furthermore, these interviews demand a blend of technical expertise, clear communication, and adaptability.

Remember, understanding the problem, exploring insights, and presenting coherent potential solutions are key.

By honing these skills, you can demonstrate your capability to solve real-world challenges using data-driven approaches. Good luck on your data science journey!

Frequently Asked Questions

How would you approach identifying and solving a specific business problem using data.

To identify and solve a business problem using data, you should start by clearly defining the problem and identifying the key metrics that will be used to evaluate success.

Next, gather relevant data from various sources and clean, preprocess, and transform it for analysis. Explore the data using descriptive statistics, visualizations, and exploratory data analysis.

Based on your understanding, build appropriate models or algorithms to address the problem, and then evaluate their performance using appropriate metrics. Iterate and refine your models as necessary, and finally, communicate your findings effectively to stakeholders.

Can you describe a time when you used data to make recommendations for optimization or improvement?

Recall a specific data-driven project you have worked on that led to optimization or improvement recommendations. Explain the problem you were trying to solve, the data you used for analysis, the methods and techniques you employed, and the conclusions you drew.

Share the results and how your recommendations were implemented, describing the impact it had on the targeted area of the business.

How would you deal with missing or inconsistent data during a case study?

When dealing with missing or inconsistent data, start by assessing the extent and nature of the problem. Consider applying imputation methods, such as mean, median, or mode imputation, or more advanced techniques like k-NN imputation or regression-based imputation, depending on the type of data and the pattern of missingness.

For inconsistent data, diagnose the issues by checking for typos, duplicates, or erroneous entries, and take appropriate corrective measures. Document your handling process so that stakeholders can understand your approach and the limitations it might impose on the analysis.

What techniques would you use to validate the results and accuracy of your analysis?

To validate the results and accuracy of your analysis, use techniques like cross-validation or bootstrapping, which can help gauge model performance on unseen data. Employ metrics relevant to your specific problem, such as accuracy, precision, recall, F1-score, or RMSE, to measure performance.

Additionally, validate your findings by conducting sensitivity analyses, sanity checks, and comparing results with existing benchmarks or domain knowledge.

How would you communicate your findings to both technical and non-technical stakeholders?

To effectively communicate your findings to technical stakeholders, focus on the methodology, algorithms, performance metrics, and potential improvements. For non-technical stakeholders, simplify complex concepts and explain the relevance of your findings, the impact on the business, and actionable insights in plain language.

Use visual aids, like charts and graphs, to illustrate your results and highlight key takeaways. Tailor your communication style to the audience, and be prepared to answer questions and address concerns that may arise.

How do you choose between different machine learning models to solve a particular problem?

When choosing between different machine learning models, first assess the nature of the problem and the data available to identify suitable candidate models. Evaluate models based on their performance, interpretability, complexity, and scalability, using relevant metrics and techniques such as cross-validation, AIC, BIC, or learning curves.

Consider the trade-offs between model accuracy, interpretability, and computation time, and choose a model that best aligns with the problem requirements, project constraints, and stakeholders’ expectations.

Keep in mind that it’s often beneficial to try several models and ensemble methods to see which one performs best for the specific problem at hand.

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Data Science Interview Case Studies: How to Prepare and Excel

In the realm of Data Science Interviews , case studies play a crucial role in assessing a candidate's problem-solving skills and analytical mindset . To stand out and excel in these scenarios, thorough preparation is key. Here's a comprehensive guide on how to prepare and shine in data science interview case studies.

Understanding the Basics

Before delving into case studies, it's essential to have a solid grasp of fundamental data science concepts. Review key topics such as statistical analysis, machine learning algorithms, data manipulation, and data visualization. This foundational knowledge will form the basis of your approach to solving case study problems.

Deconstructing the Case Study

When presented with a case study during the interview, take a structured approach to deconstructing the problem. Begin by defining the business problem or question at hand. Break down the problem into manageable components and identify the key variables involved. This analytical framework will guide your problem-solving process.

🚀 Read more on: "Ultimate Guide: Crafting an Impressive UI/UX Design Portfolio for Success"

Utilizing Data Science Techniques

Apply your data science skills to analyze the provided data and derive meaningful insights. Utilize statistical methods, predictive modeling, and data visualization techniques to explore patterns and trends within the dataset. Clearly communicate your methodology and reasoning to demonstrate your analytical capabilities.

Problem-Solving Strategy

Develop a systematic problem-solving strategy to tackle case study challenges effectively. Start by outlining your approach and assumptions before proceeding to data analysis and interpretation. Implement a logical and structured process to arrive at well-supported conclusions.

Practice Makes Perfect

Engage in regular practice sessions with mock case studies to hone your problem-solving skills. Participate in data science forums and communities to discuss case studies with peers and gain diverse perspectives. The more you practice, the more confident and proficient you will become in tackling complex data science challenges.

Communicating Your Findings

Effectively communicating your findings and insights is crucial in a data science interview case study. Present your analysis in a clear and concise manner, highlighting key takeaways and recommendations. Demonstrate your storytelling ability by structuring your presentation in a logical and engaging manner.

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Exceling in data science interview case studies requires a combination of technical proficiency, analytical thinking, and effective communication . By mastering the art of case study preparation and problem-solving, you can showcase your data science skills and secure coveted job opportunities in the field.

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Data science case study interview

Many accomplished students and newly minted AI professionals ask us$:$ How can I prepare for interviews? Good recruiters try setting up job applicants for success in interviews, but it may not be obvious how to prepare for them. We interviewed over 100 leaders in machine learning and data science to understand what AI interviews are and how to prepare for them.

TABLE OF CONTENTS

• I What to expect in the data science case study interview
• II Recommended framework
• III Interview tips
• IV Resources

AI organizations divide their work into data engineering, modeling, deployment, business analysis, and AI infrastructure. The necessary skills to carry out these tasks are a combination of technical, behavioral, and decision making skills. The data science case study interview focuses on technical and decision making skills, and you’ll encounter it during an onsite round for a Data Scientist (DS), Data Analyst (DA), Machine Learning Engineer (MLE) or Machine Learning Researcher (MLR). You can learn more about these roles in our AI Career Pathways report and about other types of interviews in The Skills Boost .

I   What to expect in the data science case study interview

The interviewer is evaluating your approach to a real-world data science problem. The interview revolves around a technical question which can be open-ended. There is no exact solution to the question; it’s your thought process that the interviewer is evaluating. Here’s a list of interview questions you might be asked:

• How many cashiers should be at a Walmart store at a given time?
• You notice a spike in the number of user-uploaded videos on your platform in June. What do you think is the cause, and how would you test it?
• Your company is thinking of changing its logo. Is it a good idea? How would you test it?
• Could you tell if a coin is biased?
• In a given day, how many birthday posts occur on Facebook?
• What are the different performance metrics for evaluating ride sharing services?
• How will you test if a chosen credit scoring model works or not? What dataset(s) do you need?
• Given a user’s history of purchases, how do you predict their next purchase?

II   Recommended framework

All interviews are different, but the ASPER framework is applicable to a variety of case studies:

• Ask . Ask questions to uncover details that were kept hidden by the interviewer. Specifically, you want to answer the following questions: “what are the product requirements and evaluation metrics?”, “what data do I have access to?”, ”how much time and computational resources do I have to run experiments?”.
• Suppose . Make justified assumptions to simplify the problem. Examples of assumptions are: “we are in small data regime”, “events are independent”, “the statistical significance level is 5%”, “the data distribution won’t change over time”, “we have three weeks”, etc.
• Plan . Break down the problem into tasks. A common task sequence in the data science case study interview is: (i) data engineering, (ii) modeling, and (iii) business analysis.
• Execute . Announce your plan, and tackle the tasks one by one. In this step, the interviewer might ask you to write code or explain the maths behind your proposed method.
• Recap . At the end of the interview, summarize your answer and mention the tools and frameworks you would use to perform the work. It is also a good time to express your ideas on how the problem can be extended.

III   Interview tips

Every interview is an opportunity to show your skills and motivation for the role. Thus, it is important to prepare in advance. Here are useful rules of thumb to follow:

Articulate your thoughts in a compelling narrative.

Data scientists often need to convert data into actionable business insights, create presentations, and convince business leaders. Thus, their communication skills are evaluated in interviews and can be the reason of a rejection. Your interviewer will judge the clarity of your thought process, your scientific rigor, and how comfortable you are using technical vocabulary.

Example 1: Your interviewer will notice if you say “correlation matrix” when you actually meant “covariance matrix”.

Example 2: Mispronouncing a widely used technical word or acronym such as Poisson, ICA, or AUC can affect your credibility. For instance, ICA is pronounced aɪ-siː-eɪ (i.e., “I see A”) rather than “Ika”.

Example 3: Show your ability to strategize by drawing the AI project development life cycle on the whiteboard.

Tie your task to the business logic.

Example 1: If you are asked to improve Instagram’s news feed, identify what’s the goal of the product. Is it to have users spend more time on the app, users click on more ads, or drive interactions between users?

Example 2: You present graphs to show the number of salesperson needed in a retail store at a given time. It is a good idea to also discuss the savings your insight can lead to.

Alternatively, your interviewer might give you the business goal, such as improving retention, engagement or reducing employee churn, but expect you to come up with a metric to optimize.

Example: If the goal is to improve user engagement, you might use daily active users as a proxy and track it using their clicks (shares, likes, etc.).

Brush up your data science foundations before the interview.

You have to leverage concepts from probability and statistics such as correlation vs. causation or statistical significance. You should also be able to read a test table.

Example: You’re a professor currently evaluating students with a final exam, but considering switching to a project-based evaluation. A rumor says that the majority of your students are opposed to the switch. Before making the switch, what would you like to test? In this question, you should introduce notation to state your hypothesis and leverage tools such as confidence intervals, p-values, distributions, and tables. Your interviewer might then give you more information. For instance, you have polled a random sample of 300 students in your class and observed that 60% of them were against the switch.

Avoid clear-cut statements.

Because case studies are often open-ended and can have multiple valid solutions, avoid making categorical statements such as “the correct approach is …” You might offend the interviewer if the approach they are using is different from what you describe. It’s also better to show your flexibility with and understanding of the pros and cons of different approaches.

Study topics relevant to the company.

Data science case studies are often inspired by in-house projects. If the team is working on a domain-specific application, explore the literature.

Example 1: If the team is working on time series forecasting, you can expect questions about ARIMA, and follow-ups on how to test whether a coefficient of your model should be zero.

Example 2: If the team is building a recommender system, you might want to read about the types of recommender systems such as collaborative filtering or content-based recommendation. You may also learn about evaluation metrics for recommender systems ( Shani and Gunawardana, 2017 ).

Listen to the hints given by your interviewer.

Example: The interviewer gives you a spreadsheet in which one of the columns has more than 20% missing values, and asks you what you would do about it. You say that you’d discard incomplete records. Your interviewer follows up with “Does the dataset size matter?”. In this scenario, the interviewer expects you to request more information about the dataset and adapt your answer. For instance, if the dataset is small, you might want to replace the missing values with a good estimate (such as the mean of the variable).

Show your motivation.

In data science case study interviews, the interviewer will evaluate your excitement for the company’s product. Make sure to show your curiosity, creativity and enthusiasm.

When you are not sure of your answer, be honest and say so.

Interviewers value honesty and penalize bluffing far more than lack of knowledge.

When out of ideas or stuck, think out loud rather than staying silent.

Talking through your thought process will help the interviewer correct you and point you in the right direction.

IV   Resources

You can build decision making skills by reading data science war stories and exposing yourself to projects . Here’s a list of useful resources to prepare for the data science case study interview.

• In Your Client Engagement Program Isn’t Doing What You Think It Is , Stitch Fix scientists (Glynn and Prabhakar) argue that “optimal” client engagement tactics change over time and companies must be fluid and adaptable to accommodate ever-changing client needs and business strategies. They present a contextual bandit framework to personalize an engagement strategy for each individual client.
• For many Airbnb prospective guests, planning a trip starts at the search engine. Search Engine Optimization (SEO) helps make Airbnb painless to find for past guests and easy to discover for new ones. In Experimentation & Measurement for Search Engine Optimization , Airbnb data scientist De Luna explains how you can measure the effectiveness of product changes in terms of search engine rankings.
• Coordinating ad campaigns to acquire new users at scale is time-consuming, leading Lyft’s growth team to take on the challenge of automation. In Building Lyft’s Marketing Automation Platform , Sampat shares how Lyft uses algorithms to make thousands of marketing decisions each day such as choosing bids, budgets, creatives, incentives, and audiences; running tests; and more.
• In this Flower Species Identification Case Study , Olson goes over a basic Python data analysis pipeline from start to finish to illustrate what a typical data science workflow looks like.
• Before producing a movie, producers and executives are tasked with critical decisions such as: do we shoot in Georgia or in Gibraltar? Do we keep a 10-hour workday or a 12-hour workday? In Data Science and the Art of Producing Entertainment at Netflix , Netflix scientists and engineers (Kumar et al.) show how data science can help answer these questions and transform a century-old industry with data science.

• Kian Katanforoosh - Founder at Workera, Lecturer at Stanford University - Department of Computer Science, Founding member at deeplearning.ai

Acknowledgment(s)

• The layout for this article was originally designed and implemented by Jingru Guo , Daniel Kunin , and Kian Katanforoosh for the deeplearning.ai AI Notes , and inspired by Distill .

Footnote(s)

• Job applicants are subject to anywhere from 3 to 8 interviews depending on the company, team, and role. You can learn more about the types of AI interviews in The Skills Boost . This includes the machine learning algorithms interview , the deep learning algorithms interview , the machine learning case study interview , the deep learning case study interview , the data science case study interview , and more coming soon.
• It takes time and effort to acquire acumen in a particular domain. You can develop your acumen by regularly reading research papers, articles, and tutorials. Twitter, Medium, and websites of data science and machine learning conferences (e.g., KDD, NeurIPS, ICML, and the like) are good places to read the latest releases. You can also find a list of hundreds of Stanford students' projects on the Stanford CS230 website .

To reference this article, please use:

Workera, "Data Science Case Study Interview".

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Top 10 Data Science Case Study Interview Questions for 2024

Data Science Case Study Interview Questions and Answers to Crack Your next Data Science Interview.

According to Harvard business review, data scientist jobs have been termed “The Sexist job of the 21st century” by Harvard business review . Data science has gained widespread importance due to the availability of data in abundance. As per the below statistics, worldwide data is expected to reach 181 zettabytes by 2025

Source: statists 2021

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“Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.” — Clive Humby, 2006

Table of Contents

What is a data science case study, why are data scientists tested on case study-based interview questions, research about the company, ask questions, discuss assumptions and hypothesis, explaining the data science workflow, 10 data science case study interview questions and answers.

A data science case study is an in-depth, detailed examination of a particular case (or cases) within a real-world context. A data science case study is a real-world business problem that you would have worked on as a data scientist to build a machine learning or deep learning algorithm and programs to construct an optimal solution to your business problem.This would be a portfolio project for aspiring data professionals where they would have to spend at least 10-16 weeks solving real-world data science problems. Data science use cases can be found in almost every industry out there e-commerce , music streaming, stock market,.etc. The possibilities are endless. 

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A case study evaluation allows the interviewer to understand your thought process. Questions on case studies can be open-ended; hence you should be flexible enough to accept and appreciate approaches you might not have taken to solve the business problem. All interviews are different, but the below framework is applicable for most data science interviews. It can be a good starting point that will allow you to make a solid first impression in your next data science job interview. In a data science interview, you are expected to explain your data science project lifecycle , and you must choose an approach that would broadly cover all the data science lifecycle activities. The below seven steps would help you get started in the right direction. 

Source: mindsbs

Business Understanding — Explain the business problem and the objectives for the problem you solved.

Data Mining — How did you scrape the required data ? Here you can talk about the connections(can be database connections like oracle, SAP…etc.) you set up to source your data.

Data Cleaning — Explaining the data inconsistencies and how did you handle them.

Data Exploration — Talk about the exploratory data analysis you performed for the initial investigation of your data to spot patterns and anomalies.

Feature Engineering — Talk about the approach you took to select the essential features and how you derived new ones by adding more meaning to the dataset flow.

Predictive Modeling — Explain the machine learning model you trained, how did you finalized your machine learning algorithm, and talk about the evaluation techniques you performed on your accuracy score.

Data Visualization — Communicate the findings through visualization and what feedback you received.

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How to Answer Case Study-Based Data Science Interview Questions?

During the interview, you can also be asked to solve and explain open-ended, real-world case studies. This case study can be relevant to the organization you are interviewing for. The key to answering this is to have a well-defined framework in your mind that you can implement in any case study, and we uncover that framework here.

Ensure that you read about the company and its work on its official website before appearing for the data science job interview . Also, research the position you are interviewing for and understand the JD (Job description). Read about the domain and businesses they are associated with. This will give you a good idea of what questions to expect.

As case study interviews are usually open-ended, you can solve the problem in many ways. A general mistake is jumping to the answer straight away.

Try to understand the context of the business case and the key objective. Uncover the details kept intentionally hidden by the interviewer. Here is a list of questions you might ask if you are being interviewed for a financial institution -

Does the dataset include all transactions from Bank or transactions from some specific department like loans, insurance, etc.?

Is the customer data provided pre-processed, or do I need to run a statistical test to check data quality?

Which segment of borrower’s your business is targeting/focusing on? Which parameter can be used to avoid biases during loan dispersion?

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Make informed or well-thought assumptions to simplify the problem. Talk about your assumption with the interviewer and explain why you would want to make such an assumption. Try to narrow down to key objectives which you can solve. Here is a list of a few instances — 

As car sales increase consistently over time with no significant spikes, I assume seasonal changes do not impact your car sales. Hence I would prefer the modeling excluding the seasonality component.

As confirmed by you, the incoming data does not require any preprocessing. Hence I will skip the part of running statistical tests to check data quality and perform feature selection.

As IoT devices are capturing temperature data at every minute, I am required to predict weather daily. I would prefer averaging out the minute data to a day to have data daily.

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Now that you have a clear and focused objective to solve the business case. You can start leveraging the 7-step framework we briefed upon above. Think of the mining and cleaning activities that you are required to perform. Talk about feature selection and why you would prefer some features over others, and lastly, how you would select the right machine learning model for the business problem. Here is an example for car purchase prediction from auctions -

First, Prepare the relevant data by accessing the data available from various auctions. I will selectively choose the data from those auctions which are completed. At the same time, when selecting the data, I need to ensure that the data is not imbalanced.

Now I will implement feature engineering and selection to create and select relevant features like a car manufacturer, year of purchase, automatic or manual transmission…etc. I will continue this process if the results are not good on the test set.

Since this is a classification problem, I will check the prediction using the Decision trees and Random forest as this algorithm tends to do better for classification problems. If the results score is unsatisfactory, I can perform hyper parameterization to fine-tune the model and achieve better accuracy scores.

In the end, summarise the answer and explain how your solution is best suited for this business case. How the team can leverage this solution to gain more customers. For instance, building on the car sales prediction analogy, your response can be

For the car predicted as a good car during an auction, the dealers can purchase those cars and minimize the overall losses they incur upon buying a bad car. 

Often, the company you are being interviewed for would select case study questions based on a business problem they are trying to solve or have already solved. Here we list down a few case study-based data science interview questions and the approach to answering those in the interviews. Note that these case studies are often open-ended, so there is no one specific way to approach the problem statement.

1. How would you improve the bank's existing state-of-the-art credit scoring of borrowers? How will you predict someone can face financial distress in the next couple of years?

Consider the interviewer has given you access to the dataset. As explained earlier, you can think of taking the following approach. 

Ask Questions — 

Q: What parameter does the bank consider the borrowers while calculating the credit scores? Do these parameters vary among borrowers of different categories based on age group, income level, etc.?

Q: How do you define financial distress? What features are taken into consideration?

Q: Banks can lend different types of loans like car loans, personal loans, bike loans, etc.  Do you want me to focus on any one loan category?

Discuss the Assumptions  — 

As debt ratio is proportional to monthly income, we assume that people with a high debt ratio(i.e., their loan value is much higher than the monthly income) will be an outlier.

Monthly income tends to vary (mainly on the upside) over two years. Cases, where the monthly income is constant can be considered data entry issues and should not be considered for analysis. I will choose the regression model to fill up the missing values.

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Firstly, I will carefully select the relevant data for my analysis. I will deselect records with insane values like people with high debt ratios or inconsistent monthly income.

Identifying essential features and ensuring they do not contain missing values. If they do, fill them up. For instance, Age seems to be a necessary feature for accepting or denying a mortgage. Also, ensuring data is not imbalanced as a meager percentage of borrowers will be defaulter when compared to the complete dataset.

As this is a binary classification problem, I will start with logistic regression and slowly progress towards complex models like decision trees and random forests.

Conclude — 

Banks play a crucial role in country economies. They decide who can get finance and on what terms and can make or break investment decisions. Individuals and companies need access to credit for markets and society to function.

You can leverage this credit scoring algorithm to determine whether or not a loan should be granted by predicting the probability that somebody will experience financial distress in the next two years.

2. At an e-commerce platform, how would you classify fruits and vegetables from the image data?

Q: Do the images in the dataset contain multiple fruits and vegetables, or would each image have a single fruit or a vegetable?

Q: Can you help me understand the number of estimated classes for this classification problem?

Q: What would be an ideal dimension of an image? Do the images vary within the dataset? Are these color images or grey images?

Upon asking the above questions, let us assume the interviewer confirms that each image would contain either one fruit or one vegetable. Hence there won't be multiple classes in a single image, and our website has roughly 100 different varieties of fruits and vegetables. For simplicity, the dataset contains 50,000 images each the dimensions are 100 X 100 pixels.

Assumptions and Preprocessing—

I need to evaluate the training and testing sets. Hence I will check for any imbalance within the dataset. The number of training images for each class should be consistent. So, if there are n number of images for class A, then class B should also have n number of training images (or a variance of 5 to 10 %). Hence if we have 100 classes, the number of training images under each class should be consistent. The dataset contains 50,000 images average image per class is close to 500 images.

I will then divide the training and testing sets into 80: 20 ratios (or 70:30, whichever suits you best). I assume that the images provided might not cover all possible angles of fruits and vegetables; hence such a dataset can cause overfitting issues once the training gets completed. I will keep techniques like Data augmentation handy in case I face overfitting issues while training the model.

End to End Data Science Workflow — 

As this is a larger dataset, I would first check the availability of GPUs as processing 50,000 images would require high computation. I will use the Cuda library to move the training set to GPU for training.

I choose to develop a convolution neural network (CNN) as these networks tend to extract better features from the images when compared to the feed-forward neural network. Feature extraction is quite essential while building the deep neural network. Also, CNN requires way less computation requirement when compared to the feed-forward neural networks.

I will also consider techniques like Batch normalization and learning rate scheduling to improve the accuracy of the model and improve the overall performance of the model. If I face the overfitting issue on the validation set, I will choose techniques like dropout and color normalization to over those.

Once the model is trained, I will test it on sample test images to see its behavior. It is quite common to model that doing well on training sets does not perform well on test sets. Hence, testing the test set model is an important part of the evaluation.

The fruit classification model can be helpful to the e-commerce industry as this would help them classify the images and tag the fruit and vegetables belonging to their category.The fruit and vegetable processing industries can use the model to organize the fruits to the correct categories and accordingly instruct the device to place them on the cover belts involved in packaging and shipping to customers.

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3. How would you determine whether Netflix focuses more on TV shows or Movies?

Q: Should I include animation series and movies while doing this analysis?

Q: What is the business objective? Do you want me to analyze a particular genre like action, thriller, etc.?

Q: What is the targeted audience? Is this focus on children below a certain age or for adults?

Let us assume the interview responds by confirming that you must perform the analysis on both movies and animation data. The business intends to perform this analysis over all the genres, and the targeted audience includes both adults and children.

Assumptions — 

It would be convenient to do this analysis over geographies. As US and India are the highest content generator globally, I would prefer to restrict the initial analysis over these countries. Once the initial hypothesis is established, you can scale the model to other countries.

While analyzing movies in India, understanding the movie release over other months can be an important metric. For example, there tend to be many releases in and around the holiday season (Diwali and Christmas) around November and December which should be considered. 

End to End  Data Science Workflow — 

Firstly, we need to select only the relevant data related to movies and TV shows among the entire dataset. I would also need to ensure the completeness of the data like this has a relevant year of release, month-wise release data, Country-wise data, etc.

After preprocessing the dataset, I will do feature engineering to select the data for only those countries/geographies I am interested in. Now you can perform EDA to understand the correlation of Movies and TV shows with ratings, Categories (drama, comedies…etc.), actors…etc.

Lastly, I would focus on Recommendation clicks and revenues to understand which of the two generate the most revenues. The company would likely prefer the categories generating the highest revenue ( TV Shows vs. Movies) over others.

This analysis would help the company invest in the right venture and generate more revenue based on their customer preference. This analysis would also help understand the best or preferred categories, time in the year to release, movie directors, and actors that their customers would like to see.

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4. How would you detect fake news on social media?

Q: When you say social media, does it mean all the apps available on the internet like Facebook, Instagram, Twitter, YouTub, etc.?

Q: Does the analysis include news titles? Does the news description carry significance?

Q: As these platforms contain content from multiple languages? Should the analysis be multilingual?

Let us assume the interviewer responds by confirming that the news feeds are available only from Facebook. The new title and the news details are available in the same block and are not segregated. For simplicity, we would prefer to categorize the news available in the English language.

Assumptions and Data Preprocessing — 

I would first prefer to segregate the news title and description. The news title usually contains the key phrases and the intent behind the news. Also, it would be better to process news titles as that would require low computing than processing the whole text as a data scientist. This will lead to an efficient solution.

Also, I would also check for data imbalance. An imbalanced dataset can cause the model to be biased to a particular class. 

I would also like to take a subset of news that may focus on a specific category like sports, finance , etc. Gradually, I will increase the model scope, and this news subset would help me set up my baseline model, which can be tweaked later based on the requirement.

Firstly, it would be essential to select the data based on the chosen category. I take up sports as a category I want to start my analysis with.

I will first clean the dataset by checking for null records. Once this check is done, data formatting is required before you can feed to a natural network. I will write a function to remove characters like !”#$%&’()*+,-./:;<=>?@[]^_`{|}~ as their character does not add any value for deep neural network learning. I will also implement stopwords to remove words like ‘and’, ‘is”, etc. from the vocabulary. 

Then I will employ the NLP techniques like Bag of words or TFIDF based on the significance. The bag of words can be faster, but TF IDF can be more accurate and slower. Selecting the technique would also depend upon the business inputs.

I will now split the data in training and testing, train a machine learning model, and check the performance. Since the data set is heavy on text models like naive bayes tends to perform better in these situations.

Conclude  — 

Social media and news outlets publish fake news to increase readership or as part of psychological warfare. In general, the goal is profiting through clickbait. Clickbaits lure users and entice curiosity with flashy headlines or designs to click links to increase advertisements revenues. The trained model will help curb such news and add value to the reader's time.

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5. How would you forecast the price of a nifty 50 stock?

Q: Do you want me to forecast the nifty 50 indexes/tracker or stock price of a specific stock within nifty 50?

Q: What do you want me to forecast? Is it the opening price, closing price, VWAP, highest of the day, etc.?

Q: Do you want me to forecast daily prices /weekly/monthly prices?

Q: Can you tell me more about the historical data available? Do we have ten years or 15 years of recorded data?

With all these questions asked to the interviewer, let us assume the interviewer responds by saying that you should pick one stock among nifty 50 stocks and forecast their average price daily. The company has historical data for the last 20 years.

Assumptions and Data preprocessing — 

As we forecast the average price daily, I would consider VWAP my target or predictor value. VWAP stands for Volume Weighted Average Price, and it is a ratio of the cumulative share price to the cumulative volume traded over a given time.

Solving this data science case study requires tracking the average price over a period, and it is a classical time series problem. Hence I would refrain from using the classical regression model on the time series data as we have a separate set of machine learning models (like ARIMA , AUTO ARIMA, SARIMA…etc.) to work with such datasets.

Like any other dataset, I will first check for null and understand the % of null values. If they are significantly less, I would prefer to drop those records.

Now I will perform the exploratory data analysis to understand the average price variation from the last 20 years. This would also help me understand the tread and seasonality component of the time series data. Alternatively, I will use techniques like the Dickey-Fuller test to know if the time series is stationary or not. 

Usually, such time series is not stationary, and then I can now decompose the time series to understand the additive or multiplicative nature of time series. Now I can use the existing techniques like differencing, rolling stats, or transformation to make the time series non-stationary.

Lastly, once the time series is non-stationary, I will separate train and test data based on the dates and implement techniques like ARIMA or Facebook prophet to train the machine learning model .

Some of the major applications of such time series prediction can occur in stocks and financial trading, analyzing online and offline retail sales, and medical records such as heart rate, EKG, MRI, and ECG.

Time series datasets invoke a lot of enthusiasm between data scientists . They are many different ways to approach a Time series problem, and the process mentioned above is only one of the know techniques.

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6. How would you forecast the weekly sales of Walmart? Which department impacted most during the holidays?

Q: Walmart usually operates three different stores - supermarkets, discount stores, and neighborhood stores. Which store data shall I pick to get started with my analysis? Are the sales tracked in US dollars?

Q: How would I identify holidays in the historical data provided? Is the store closed on Black Friday week, super bowl week, or Christmas week?

Q: What are the evaluation or the loss criteria? How many departments are present across all store types?

Let us assume the interviewer responds by saying you must forecast weekly sales department-wise and not store type-wise in US dollars. You would be provided with a flag within the dataset to inform weeks having holidays. There are over 80 departments across three types of stores.

As we predict the weekly sales, I would assume weekly sales to be the target or the predictor for our data model before training.

We are tracking sales price weekly, We will use a regression model to predict our target variable, “Weekly_Sales,” a grouped/hierarchical time series. We will explore the following categories of models, engineer features, and hyper-tune parameters to choose a model with the best fit.

- Linear models

- Tree models

- Ensemble models

I will consider MEA, RMSE, and R2 as evaluation criteria.

End to End Data Science Workflow-

The foremost step is to figure out essential features within the dataset. I would explore store information regarding their size, type, and the total number of stores present within the historical dataset.

The next step would be to perform feature engineering; as we have weekly sales data available, I would prefer to extract features like ‘WeekofYear’, ‘Month’, ‘Year’, and ‘Day’. This would help the model to learn general trends.

Now I will create store and dept rank features as this is one of the end goals of the given problem. I would create these features by calculating the average weekly sales.

Now I will perform the exploratory data analysis (a.k.a EDA) to understand what story does the data has to say? I will analyze the stores and weekly dept sales for the historical data to foresee the seasonality and trends. Weekly sales against the store and weekly sales against the department to understand their significance and whether these features must be retained that will be passed to the machine learning models.

After feature engineering and selection, I will set up a baseline model and run the evaluation considering MAE, RMSE and R2. As this is a regression problem, I will begin with simple models like linear regression and SGD regressor. Later, I will move towards complex models, like Decision Trees Regressor, if the need arises. LGBM Regressor and SGB regressor.

Sales forecasting can play a significant role in the company’s success. Accurate sales forecasts allow salespeople and business leaders to make smarter decisions when setting goals, hiring, budgeting, prospecting, and other revenue-impacting factors. The solution mentioned above is one of the many ways to approach this problem statement.

With this, we come to the end of the post. But let us do a quick summary of the techniques we learned and how they can be implemented. We would also like to provide you with some practice case studies questions to help you build up your thought process for the interview.

7. Considering an organization has a high attrition rate, how would you predict if an employee is likely to leave the organization?

8. How would you identify the best cities and countries for startups in the world?

9. How would you estimate the impact on Air Quality across geographies during Covid 19?

10. A Company often faces machine failures at its factory. How would you develop a model for predictive maintenance?

Do not get intimated by the problem statement; focus on your approach -

Ask questions to get clarity

Discuss assumptions, don't assume things. Let the data tell the story or get it verified by the interviewer.

Build Workflows — Take a few minutes to put together your thoughts; start with a more straightforward approach.

Conclude — Summarize your answer and explain how it best suits the use case provided.

We hope these case study-based data scientist interview questions will give you more confidence to crack your next data science interview.

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Data Scientist Career Guide and Interview Preparation

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Instructor: IBM Skills Network Team

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What you'll learn

Describe the role of a data scientist and some career path options as well as the prospective opportunities in the field.

Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.

Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.

Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.

Skills you'll gain

• Career Development
• Interviewing Skills
• Job Preparation
• Resume Building

Details to know

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There are 3 modules in this course

Data science professionals are in high demand around the world, and the trend shows no sign of slowing. There are lots of great jobs available, but lots of great candidates too. How can you get the edge in such a competitive field?

This course will prepare you to enter the job market as a great candidate for a data scientist position. It provides practical techniques for creating essential job-seeking materials such as a resume and a portfolio, as well as auxiliary tools like a cover letter and an elevator pitch. You will learn how to find and assess prospective job positions, apply to them, and lay the groundwork for interviewing. The course doesn’t stop there, however. You will also get inside tips and steps you can use to perform professionally and effectively at interviews. You will learn how to approach a code challenge and get to practice completing them. Additionally, it provides guidance about the regular functions and tasks of data scientists, as well as the opportunities of the profession and some options for career development. Let seasoned professionals share their experience to help you get ahead and land the job you want!

Building a Foundation

Your job search will be much more effective if you do some primary work before you begin. In Building a Foundation, you’ll learn how to clearly understand the jobs you will be looking for. You’ll learn how to write a basic resume and collect your previous work examples into a portfolio. You’ll also create some other materials that will be useful, such as a cover letter and an elevator pitch

What's included

10 videos 2 readings 2 quizzes 5 plugins

10 videos • Total 63 minutes

• Role of a Data Scientist • 6 minutes • Preview module
• SME Video: Paths in Data Science • 4 minutes
• Opportunities in Data Science  • 7 minutes
• SME Video: Data Science Roles and Required Skills • 6 minutes
• Build Your Portfolio  • 5 minutes
• SME Video: Optimal Portfolios • 4 minutes
• Introduction to Data Science Professional Certificate • 5 minutes
• Draft Your Resume  • 9 minutes
• SME Video: Attention-Getting Resumes • 6 minutes
• SME Video: Standing Out from the Crowd • 4 minutes

2 readings • Total 4 minutes

• Course Introduction • 2 minutes
• Build your skills as a Data Scientist • 2 minutes

2 quizzes • Total 40 minutes

• Building a Foundation • 10 minutes
• Graded Quiz • 30 minutes

5 plugins • Total 120 minutes

• Hands-on Lab: Draft your resume  • 60 minutes
• Draft Your Basic Cover Letter • 15 minutes
• Hands- on Lab: Create your Cover Letter • 15 minutes
• Drafting Other Materials • 15 minutes
• Drafting an Elevator Pitch • 15 minutes

Applying and Preparing to Interview 

Job Seeking and Interview Preparation helps you understand how to put yourself forth as a memorable candidate. You’ll get guidance on researching prospective companies and assessing job leads to sift out the ones you want to focus on. You’ll learn about rehearsing for interviews and why it can make a big difference in your performance. And you’ll learn ways to network and let people you meet help you find your ideal role.

7 videos 2 quizzes 2 plugins

7 videos • Total 38 minutes

• Company and Industry Research in Data Science • 6 minutes • Preview module
• Networking Online and Off • 6 minutes
• SME Video: Building Your Network • 5 minutes
• Assessing Job Listings  • 6 minutes
• SME Video: A Closer Look at Job Listings • 3 minutes
• Interview rehearsal • 5 minutes
• SME Video: Job Interview Preparation • 4 minutes
• Applying and Preparing to Interview • 10 minutes

2 plugins • Total 75 minutes

• Applying for a Job • 15 minutes
• Hands-on Lab: Prepare for an Interview • 60 minutes

Interviewing

After you’ve attracted a company’s attention, it’s important to know how to follow through. The Interviewing module will guide you through the interview process from beginning to end. You’ll learn about common types of interviews and what to expect from them, including code challenges. You’ll also learn some crucial tips for making a great impression in a final interview and how to follow up so that you stand out from the crowd.

14 videos 3 readings 2 quizzes 3 plugins

14 videos • Total 79 minutes

• Overview of the Interview Process  • 3 minutes • Preview module
• SME Video: A Typical Interview Cycle • 4 minutes
• Data Science Mock Interview - Part 1 • 7 minutes
• Data Science Mock Interview - Part 2 • 5 minutes
• Data Science Mock Interview - Part 3 • 5 minutes
• Mock Interview Analysis • 3 minutes
• Best Practices: Getting an Interview • 6 minutes
• Best Practices: Interview Preparation • 6 minutes
• Coding Challenges In Data Science • 7 minutes
• SME Video: Case Study Insights • 3 minutes
• SME Video: Tech Screen Expectations • 5 minutes
• Final Interviewing • 7 minutes
• SME Video: Interviewing • 6 minutes
• SME Video: Negotiations • 5 minutes

3 readings • Total 17 minutes

• Checklist • 5 minutes
• Congratulations and Next Steps • 2 minutes
• Thanks from the Course Team • 10 minutes
• Interviewing • 10 minutes

3 plugins • Total 45 minutes

• Unethical Interviewing • 15 minutes
• Second-Round Screen • 15 minutes
• After the Interview • 15 minutes

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Reviewed on Sep 4, 2022

I like how things were laid out in clear and concise terms and descriptions. This is one of the more thorough interview preparation guides I've read/looked at .

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I have a deep understanding on what it entails getting a Data Scientist job role.

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Excellent course with useful recommendations and most importantly DO NOTs during the overall interview process.

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[2023] Meta Data Science Interview (+ Case Examples)

Aspiring to become a data scientist at Meta? A core pillar within the data science at Meta is the analytics role specialized in data analysis, data visualization, and AB testing. And, you have the opportunity to work across various products in the Meta ecosystem - Facebook, Instagram, Messenger, WhatsApp, Thread, AR/VR Devices .

Let’s look at a detailed guide on how to ACE the data scientist interview at Meta . Here are 7 key aspects to consider as you prepare for the data scientist interview.

• 📝 Job Application
• ⏰ Interview Process - Recruiter Screen/Technical Screen/Onsite Interviews
• ✍️ Example Questions
• 💡 Preparation Tips

1. Job Application

Getting your application spotted by a recruiter at Meta is tricky. There are, however, a number of strategies you can execute to maximize your chance of landing an interview.

1.1 Understand the role expectation

Meta has the following expectations about the role of the data scientist. Understanding their expectations provide clues on how the interviews will be structured in technical and behavioral rounds.

• Explore large data sets to provide actionable insights with data visualizations
• Track product health and conduct experiment design & analysis
• Partner with data engineers on tables, dashboards, metrics, and goals
• Design robust experiments, considering statistical and practical significance and biases

Soft Skills

• Partner with cross-functional teams to inform and influence product roadmap and go-to-market strategies
• Apply expertise in quantitative analysis and data mining to develop data-informed strategies for improving products.
• Drive product decisions via actionable insights and data storytelling

You will see later, based on actual question examples, Meta places a great deal of assessing the candidate’s competencies in data preparation/analysis, product sense, experimentation, and stakeholder communication .

2.2 Tailor your resume

Tailor your resume to highlight the background and skills that recruiters and hiring managers look for:

• Bachelor's degree (Master’s preferred) in Mathematics, Statistics, and a relevant technical field
• Work experience in analytics and data science specialized in product
• Experience with SQL, Python, R, or other programming languages.
• Proven executive-level communication skills to influence product decisions.

2. Interview Process

The interview process at Meta can take 4 to 8 weeks. In some cases the entire process is expedited if you have a competing offer from another FAANG company (e.g. Google). The steps are recruiter screen → technical screen → onsite interview.

2.1 Recruiter Screen

The recruiter screen at Meta is usually formatted the following way:

• 📝 Format: Phone Call
• ⏰ Duration: 20 to 30 minutes
• 💭 Interviewer: Technical Recruiter
• 📚 Questions: Behavioral, Culture-Fit, Logistics

In the meeting expect to discuss the following:

• On Meta’s mission and About the Role
• Your Background - - "Walk me through your resume. Why do you want to work at Meta?”
• Light Technical Questions - In some cases, a recruiter may ask simple statistics or SQL questions like explain the difference between INNER/LEFT/OUTER JOINS.
• Your Logistics - Expect to discuss your visa/citizenship status, remote/hybrid/location preference, scheduling for the next interview.

Pro Tip💡 - Practice explaining your story prior to the interview

2.2 Technical Screen

The technical screen at Meta is usually conducted on Coderpad, or a virtual pad where the interviewer will assess your coding and product sense ability.

• 📝 Format: Video Conference Call
• ⏰ Duration: 45 to 60 minutes
• 💭 Interviewer: Senior/Staff DS
• 📚 Questions: Data Manipulation (using SQL or coding) and Product Case

Pro Tip💡 - Practice 2 to 3 data manipulation problems up front when you are starting preparation with Meta. Aim to crack a problem within the 7 to 8 minute time limit.

2.3 Onsite Interview

1 to 3 weeks after the technical screen, you will be scheduled for the onsite stage. This is the most challenging aspect of the interview process. The bar is much higher than the technical screen.

• 📝 Format: Video Conference Calls
• 💭 Interviewer: Senior/Staff DS or Data Science Manager
• 📚 Rounds/Questions: 4 to 5 Rounds - Programming, Research Design, Metrics, Data Analysis, Behavioral

Pro Tip💡 - Continue to ramp up on data manipulation skills and practice case problems verbally.

3. Interview Questions

Throughout the interview process, you will be assessed on a combination of the following areas:

📚  Areas Covered

• Programming
• Research Design
• Determining Goals and Success Metrics
• Data Analysis

3.1 Programming

The interviewer will assess you on on your familiarity with data manipulations (e.g. merging datasets, filtering data to find insights). Expect to discuss trade-offs in coding/query efficiency. Things to consider:

• You will be given a choice of language to solve the problem - SQL, Python, R
• It doesn’t matter which SQL (e.g. MySQL, PostgresQL) you are using as long as you know the common syntax
• Explain your solution verbally as you write and/or after you are done.

📝 Here’s an actual question…

3.2 Research Design

Your interviewer is assessing design and explanation of AB testing and/or causal inference in various product cases. The general form of this question is formatted as follows:

• Suppose we want to change feature [X] how would you design an experiment and test whether to change the feature or not?
• What are the downsides of the methodology you propose? Are there biases in the analysis or experiment that we should correct for?

The Messenger team proposes a feature that enables users to receive recent messages either unread or unresponded. How would you measure the effectiveness of this feature in an experiment?

👇 Here’s the solution

3.3 Determining Goals and Success Metrics

Your interviewer wants to see your ability to define metrics that reflect success and inform business objectives. The question is usually formatted the following way: How would you measure [X] of a product [Y]? [X] is a quality like success, health, satisfaction; [Y] is any feature or product of Meta like Feed, Notifications, Instagram, and WhatsApp.

How would you set goals and measure success for Facebook notifications?

3.4 Data Analysis

Your interviewer will be looking for how you leverage methods ranging from descriptive statistics to statistical models to answer hypothesis-driven questions. Things to consider are the following:

• What are the hypotheses that would lead to a decision? How would you prove a hypothesis is true?
• Can you translate concepts generated into a specific analysis plan? Are you able to use data to answer the original question posed with enough detail to demonstrate the ability to execute on an analysis?

How would you measure the impact of parents being on Facebook on teenagers?

4. Preparation Tips

Use the following resources to further help your prep!

• Read the Meta’s Financials and KPIs
• Visit the Meta’s Engineering Blog
• Practice SQL Problem Sets
• Join the Data Science Interview Bootcamp led by FAANG Data Scientists/Interviewers

• Case Interview: A comprehensive guide
• Pyramid Principle
• Hypothesis driven structure
• Fit Interview
• Consulting math
• The key to landing your consulting job
• What is a case interview?
• Types of case interview
• How to solve cases with the Problem-Driven Structure?
• What to remember in case interviews
• Case examples or building blocks?
• How do I prepare for case interviews
• Interview day tips
• How we can help

1. The key to landing your consulting job.

Case interviews - where you are asked to solve a business case study under scrutiny - are the core of the selection process right across McKinsey, Bain and BCG (the “MBB” firms). This interview format is also used pretty much universally across other high-end consultancies; including LEK, Kearney, Oliver Wyman and the consulting wings of the “Big Four”.

If you want to land a job at any of these firms, you will have to ace multiple case interviews.

It is increasingly likely that you will also have to solve online cases given by chatbots. You might need to pass these either before making it to interview or be asked to sit them alongside first round interviews.

Importantly, case studies aren’t something you can just wing . Firms explicitly expect you to have thoroughly prepared and many of your competitors on interview day will have been prepping for months.

Don’t worry though - MCC is here to help!

This article will take you through a full overview of everything you’ll need to know to do well, linking to more detailed articles and resources at each stage to let you really drill down into the details.

As well as traditional case interviews, we’ll also attend to the new formats in which cases are being delivered and otherwise make sure you’re up to speed with recent trends in this overall part of consulting recruitment.

Before we can figure out how to prepare for a case interview, though, we will first have to properly understand in detail what exactly you are up against. What format does a standard consulting case interview take? What is expected of you? How will you be assessed?

Let's dive right in and find out!

Professional help

Before going further, if this sounds like a lot to get your head around on your own, don't worry - help is available!

Our Case Academy course gives you everything you need to know to crack cases like a pro:

Case Academy Course

To put what you learn into practice (and secure some savings in the process) you can add mock interview coaching sessions with expereinced MBB consultants:

Coaching options

And, if you just want an experienced consultant to take charge of the whole selection process for you, you can check out our comprehensive mentoring programmes:

Explore mentoring

Now, back to the article!

2. What is a case interview?

Before we can hope to tackle a case interview, we have to understand what one is.

In short, a case interview simulates real consulting work by having you solve a business case study in conversation with your interviewer.

This case study will be a business problem where you have to advise a client - that is, an imaginary business or similar organisation in need of guidance.

You must help this client solve a problem and/or make a decision. This requires you to analyse the information you are given about that client organisation and figure out a final recommendation for what they should do next.

Business problems in general obviously vary in difficulty. Some are quite straightforward and can be addressed with fairly standard solutions. However, consulting firms exist precisely to solve the tough issues that businesses have failed to deal with internally - and so consultants will typically work on complex, idiosyncratic problems requiring novel solutions.

Some examples of case study questions might be:

• How much would you pay for a banking licence in Ghana?
• Estimate the potential value of the electric vehicle market in Germany
• How much gas storage capacity should a UK domestic energy supplier build?

Consulting firms need the brightest minds they can find to put to work on these important, difficult projects. You can expect the case studies you have to solve in interview, then, to echo the unique, complicated problems consultancies deal with every day. As we’ll explain here, this means that you need to be ready to think outside the box to figure out genuinely novel solutions.

2.1. Where are case interviews in the consulting selection process?

Not everyone who applies to a consulting firm will have a case interview - far from it!

In fact, case interviews are pretty expensive and inconvenient for firms to host, requiring them to take consultants off active projects and even fly them back to the office from location for in-person interviews (although this happens less frequently now). Ideally, firms want to cut costs and save time by narrowing down the candidate pool as much as possible before any live interviews.

As such, there are some hoops to jump through before you make it to interview rounds.

Firms will typically eliminate as much as 80% of the applicant pool before interviews start . For most firms, 50%+ of applicants might be cut based on resumes, before a similar cut is made on those remaining based on aptitude tests. McKinsey currently gives their Solve assessment to most applicants, but will use their resulting test scores alongside resumes to cut 70%+ of the candidate pool before interviews.

You'll need to be on top of your game to get as far as an interview with a top firm. Getting through the resume screen and any aptitude tests is an achievement in itself! Also we need to note that the general timeline of an application can differ depending on a series of factors, including which position you apply, your background, and the office you are applying to. For example, an undergraduate applying for a Business Analyst position (the entry level job at McKinsey) will most likely be part of a recruitment cycle and as such have pretty fixed dates when they need to sit the pre-screening test, and have the first and second round interviews (see more on those below). Conversely, an experienced hire will most likely have a much greater choice of test and interview dates as well as more time at their disposal to prepare.

For readers not yet embroiled in the selection process themselves, let’s put case interviews in context and take a quick look at each stage in turn. Importantly, note that you might also be asked to solve case studies outside interviews as well…

2.1.1. Application screen

It’s sometimes easy to forget that such a large cut is made at the application stage. At larger firms, this will mean your resume and cover letter is looked at by some combination of AI tools, recruitment staff and junior consulting staff (often someone from your own university).

Only the best applications will be passed to later stages, so make sure to check out our free resume and cover letter guides, and potentially get help with editing , to give yourself the best chance possible.

2.1.2. Aptitude tests and online cases

This part of the selection process has been changing quickly in recent years and is increasingly beginning to blur into the traditionally separate case interview rounds.

In the past, GMAT or PST style tests were the norm. Firms then used increasingly sophisticated and often gamified aptitude tests, like the Pymetrics test currently used by several firms, including BCG and Bain, and the original version of McKinsey’s Solve assessment (then branded as the Problem Solving Game).

Now, though, there is a move towards delivering relatively sophisticated case studies online. For example, McKinsey has replaced half the old Solve assessment with an online case. BCG’s Casey chatbot case now directly replaces a live first round case interview, and in the new era of AI chatbots, we expect these online cases to quickly become more realistic and increasingly start to relieve firms of some of the costs of live interviews.

Our consultants collectively reckon that, over time, 50% of case interviews are likely to be replaced with these kinds of cases . We give some specific advice for online cases in section six. However, the important thing to note is that these are still just simulations of traditional case interviews - you still need to learn how to solve cases in precisely the same way, and your prep will largely remain the same.

2.1.3. Rounds of Interviews

Now, let’s not go overboard with talk of AI. Even in the long term, the client facing nature of consulting means that firms will have live case interviews for as long as they are hiring anyone. And in the immediate term, case interviews are still absolutely the core of consulting selection.

Before landing an offer at McKinsey, Bain, BCG or any similar firm, you won’t just have one case interview, but will have to complete four to six case interviews, usually divided into two rounds, with each interview lasting approximately 50-60 minutes .

Being invited to first round usually means two or three case interviews. As noted above, you might also be asked to complete an online case or similar alongside your first round interviews.

If you ace first round, you will be invited to second round to face the same again, but more gruelling. Only then - after up to six case interviews in total, can you hope to receive an offer.

2.2. Differences between first and second round interviews

Despite interviews in the first and second round following the same format, second/final round interviews will be significantly more intense . The seniority of the interviewer, time pressure (with up to three interviews back-to-back), and the sheer value of the job at stake will likely make a second round consulting case interview one of the most challenging moments of your professional life.

There are three key differences between the two rounds:

• Time Pressure : Final round case interviews test your ability to perform under pressure, with as many as three interviews in a row and often only very small breaks between them.
• Focus : Since second round interviewers tend to be more senior (usually partners with 12+ years experience) and will be more interested in your personality and ability to handle challenges independently. Some partners will drill down into your experiences and achievements to the extreme. They want to understand how you react to challenges and your ability to identify and learn from past mistakes.
• Psychological Pressure: While case interviews in the first round are usually more focused on you simply cracking the case, second round interviewers often employ a "bad cop" strategy to test the way you react to challenges and uncertainty.

2.3. What skills do case interviews assess?

Reliably impressing your interviewers means knowing what they are looking for. This means understanding the skills you are being assessed against in some detail.

Overall, it’s important always to remember that, with case studies, there are no strict right or wrong answers. What really matters is how you think problems through, how confident you are with your conclusions and how quick you are with the back of the envelope arithmetic.

The objective of this kind of interview isn’t to get to one particular solution, but to assess your skillset. This is even true of modern online cases, where sophisticated AI algorithms score how you work as well as the solutions you generate.

If you visit McKinsey , Bain and BCG web pages on case interviews, you will find that the three firms look for very similar traits, and the same will be true of other top consultancies.

Broadly speaking, your interviewer will be evaluating you across five key areas:

2.1.1.One: Probing mind

Showing intellectual curiosity by asking relevant and insightful questions that demonstrate critical thinking and a proactive nature. For instance, if we are told that revenues for a leading supermarket chain have been declining over the last ten years, a successful candidate would ask:

“ We know revenues have declined. This could be due to price or volume. Do we know how they changed over the same period? ”

This is as opposed to a laundry list of questions like:

• Did customers change their preferences?
• Which segment has shown the decline in volume?
• Is there a price war in the industry?

2.1.2. Structure

Structure in this context means structuring a problem. This, in turn, means creating a framework - that is, a series of clear, sequential steps in order to get to a solution.

As with the case interview in general, the focus with case study structures isn’t on reaching a solution, but on how you get there.

This is the trickiest part of the case interview and the single most common reason candidates fail.

We discuss how to properly structure a case in more detail in section three. In terms of what your interviewer is looking for at high level, though, key pieces of your structure should be:

• Proper understanding of the objective of the case - Ask yourself: "What is the single crucial piece of advice that the client absolutely needs?"
• Identification of the drivers - Ask yourself: "What are the key forces that play a role in defining the outcome?"

Our Problem Driven Structure method, discussed in section three, bakes this approach in at a fundamental level. This is as opposed to the framework-based approach you will find in older case-solving

Focus on going through memorised sequences of steps too-often means failing to develop a full understanding of the case and the real key drivers.

At this link, we run through a case to illustrate the difference between a standard framework-based approach and our Problem Driven Structure method.

2.1.3. Problem Solving

You’ll be tested on your ability to identify problems and drivers, isolate causes and effects, demonstrate creativity and prioritise issues. In particular, the interviewer will look for the following skills:

• Prioritisation - Can you distinguish relevant and irrelevant facts?
• Connecting the dots - Can you connect new facts and evidence to the big picture?
• Establishing conclusions - Can you establish correct conclusions without rushing to inferences not supported by evidence?

2.1.4. Numerical Agility

In case interviews, you are expected to be quick and confident with both precise and approximated numbers. This translates to:

• Performing simple calculations quickly - Essential to solve cases quickly and impress clients with quick estimates and preliminary conclusions.
• Analysing data - Extract data from graphs and charts, elaborate and draw insightful conclusions.
• Solving business problems - Translate a real world case to a mathematical problem and solve it.

Our article on consulting math is a great resource here, though the extensive math content in our MCC Academy is the best and most comprehensive material available.

2.1.5. Communication

Real consulting work isn’t just about the raw analysis to come up with a recommendation - this then needs to be sold to the client as the right course of action.

Similarly, in a case interview, you must be able to turn your answer into a compelling recommendation. This is just as essential to impressing your interviewer as your structure and analysis.

Consultants already comment on how difficult it is to find candidates with the right communication skills. Add to this the current direction of travel, where AI will be able to automate more and more of the routine analytic side of consulting, and communication becomes a bigger and bigger part of what consultants are being paid for.

So, how do you make sure that your recommendations are relevant, smart, and engaging? The answer is to master what is known as CEO-level communication .

This art of speaking like a CEO can be quite challenging, as it often involves presenting information in effectively the opposite way to how you might normally.

To get it right, there are three key areas to focus on in your communications:

• Top down : A CEO wants to hear the key message first. They will only ask for more details if they think that will actually be useful. Always consider what is absolutely critical for the CEO to know, and start with that. You can read more in our article on the Pyramid Principle .
• Concise : This is not the time for "boiling the ocean" or listing an endless number possible solutions. CEOs, and thus consultants, want a structured, quick and concise recommendation for their business problem, that they can implement immediately.
• Fact-based : Consultants share CEOs' hatred of opinions based on gut feel rather than facts. They want recommendations based on facts to make sure they are actually in control. Always go on to back up your conclusions with the relevant facts.

Being concise and to the point is key in many areas, networking being one for them. For more detail on all this, check out our full article on delivering recommendations .

Prep the right way

3. types of case interview.

While most case interviews share a similar structure, firms will have some differences in the particular ways they like to do things in terms of both the case study and the fit component.

As we’ll see, these differences aren’t hugely impactful in terms of how you prepare. That said, it's always good to know as much as possible about what you will be going up against.

3.1. Different case objectives

A guiding thread throughout this article and our approach in general will be to treat each case as a self-contained problem and not try to pigeonhole it into a certain category. Having said that, there are of course similarities between cases and we can identify certain parameters and objectives.

Broadly speaking, cases can be divided into issue-based cases and strategic decision cases. In the former you will be asked to solve a certain issue, such as declining profits, or low productivity whereas in the latter you will be ask whether your client should or should not do something, such as enter a specific market or acquire another company. The chart below is a good breakdown of these different objectives:

3.2. How do interviewers craft cases

While interviewers will very likely be given a case bank to choose from by their company, a good number of them will also choose to adapt the cases they would currently be working on to an interview setting. The difference is that the latter cases will be harder to pigeonhole and apply standard frameworks to, so a tailored approach will be paramount.

If you’ve applied for a specific practice or type of consulting - such as operational consulting, for example - it’s very likely that you will receive a case geared towards that particular area alongside a ‘generalist’ consulting case (however, if that’s the case, you will generally be notified). The other main distinction when it comes to case interviews is between interviewer-led and candidate-led.

3.3. Candidate-led cases

Most consulting case interview questions test your ability to crack a broad problem, with a case prompt often going something like:

" How much would you pay to secure the rights to run a restaurant in the British Museum? "

You, as a candidate, are then expected to identify your path to solve the case (that is, provide a structure), leveraging your interviewer to collect the data and test your assumptions.

This is known as a “candidate-led” case interview and is used by Bain, BCG and other firms. From a structuring perspective, it’s easier to lose direction in a candidate-led case as there are no sign-posts along the way. As such, you need to come up with an approach that is both broad enough to cover all of the potential drivers in a case but also tailored enough to the problem you are asked to solve. It’s also up to you to figure out when you need to delve deeper into a certain branch of the case, brainstorm or ask for data. The following case from Bain is an excellent example on how to navigate a candidate-led case.

3.4. Interviewer-led cases

This type of case - employed most famously by McKinsey - is slightly different, with the interviewer controlling the pace and direction of the conversation much more than with other case interviews.

At McKinsey, your interviewer will ask you a set of pre-determined questions, regardless of your initial structure. For each question, you will have to understand the problem, come up with a mini structure, ask for additional data (if necessary) and come to the conclusion that answers the question. This more structured format of case also shows up in online cases by other firms - notably including BCG’s Casey chatbot (with the amusing result that practising McKinsey-style cases can be a great addition when prepping for BCG).

Essentially, these interviewer-led case studies are large cases made up of lots of mini-cases. You still use basically the same method as you would for standard (or candidate-led) cases - the main difference is simply that, instead of using that method to solve one big case, you are solving several mini-cases sequentially. These cases are easier to follow as the interviewer will guide you in the right direction. However, this doesn’t mean you should pay less attention to structure and deliver a generic framework! Also, usually (but not always!) the first question will ask you to map your approach and is the equivalent of the structuring question in candidate-led cases. Sometimes, if you’re missing key elements, the interviewer might prompt you in the right direction - so make sure to take those prompts seriously as they are there to help you get back on track (ask for 30 seconds to think on the prompt and structure your approach). Other times - and this is a less fortunate scenario - the interviewer might say nothing and simply move on to the next question. This is why you should put just as much thought (if not more) into the framework you build for interviewer-led cases , as you may be penalized if you produce something too generic or that doesn’t encompass all the issues of the case.

3.5. Case and fit

The standard case interview can be thought of as splitting into two standalone sub-interviews. Thus “case interviews” can be divided into the case study itself and a “fit interview” section, where culture fit questions are asked.

This can lead to a bit of confusion, as the actual case interview component might take up as little as half of your scheduled “case interview”. You need to make sure you are ready for both aspects.

To illustrate, here is the typical case interview timeline:

• First 15-30 minutes: Fit Interview - with questions assessing your motivation to be a consultant in that specific firm and your traits around leadership and teamwork. Learn more about the fit interview in our in-depth article here .
• Next 30-40 minutes: Case Interview - solving a case study
• Last 5 minutes: Fit Interview again - this time focussing on your questions for your interviewer.

Both the Case and Fit interviews play crucial roles in the finial hiring decision. There is no “average” taken between case and fit interviews: if your performance is not up to scratch in either of the two, you will not be able to move on to the next interview round or get an offer.

NB: No case without fit

Note that, even if you have only been told you are having a case interview or otherwise are just doing a case study, always be prepared to answer fit questions. At most firms, it is standard practice to include some fit questions in all case interviews, even if there are also separate explicit fit interviews, and interviewers will almost invariably include some of these questions around your case. This is perfectly natural - imagine how odd and artificial it would be to show up to an interview, simply do a case and leave again, without talking about anything else with the interviewer before or after.

3.5.1 Differences between firms

For the most part, a case interview is a case interview. However, firms will have some differences in the particular ways they like to do things in terms of both the case study and the fit component.

3.5.2. The McKinsey PEI

McKinsey brands its fit aspect of interviews as the Personal Experience Interview or PEI. Despite the different name, this is really much the same interview you will be going up against in Bain, BCG and any similar firms.

McKinsey does have a reputation for pushing candidates a little harder with fit or PEI questions , focusing on one story per interview and drilling down further into the specific details each time. We discuss this tendency more in our fit interview article . However, no top end firm is going to go easy on you and you should absolutely be ready for the same level of grilling at Bain, BCG and others. Thus any difference isn’t hugely salient in terms of prep.

3.6. What is different in 2023?

For the foreseeable future, you are going to have to go through multiple live case interviews to secure any decent consulting job. These might increasingly happen via Zoom rather than in person, but they should remain largely the same otherwise.

However, things are changing and the rise of AI in recent months seems pretty much guaranteed to accelerate existing trends.

Even before the explosive development of AI chatbots like ChatGPT we have seen in recent months, automation was already starting to change the recruitment process.

As we mentioned, case interviews are expensive and inconvenient for firms to run . Ideally, then, firms will try to reduce the number of interviews required for recruitment as far as possible. For many years, tests of various kinds served to cut down the applicant pool and thus the number of interviews. However, these tests had a limited capacity to assess candidates against the full consulting skillset in the way that case interviews do so well.

More recently, though, the development of online testing has allowed for more and more advanced assessments. Top consulting firms have been leveraging screening tests that better and better capture the same skillset as case interviews. Eventually this is converging on automated case studies. We see this very clearly with the addition of the Redrock case to McKinsey’s Solve assessment.

As these digital cases become closer to the real thing, the line between test and interview blurs. Online cases don’t just reduce the number of candidates to interview, but start directly replacing interviews.

Case in point here is BCG’s Casey chatbot . Previously, BCG had deployed less advanced online cases and similar tests to weed out some candidates before live case interviews began. Now, though, Casey actually replaces one first round case interview.

Casey, at time of writing, is still a relatively “basic” chatbot, basically running through a pre-set script. The Whatsapp-like interface does a lot of work to make it feel like one is chatting to a “real person” - the chatbot itself, though, cannot provide feedback or nudges to candidates as would a human interviewer.

We fully expect that, as soon as BCG and other firms can train a truer AI, these online cases will become more widespread and start replacing more live interviews.

We discuss the likely impacts of advanced AI on consulting recruitment and the industry more broadly in our blog.

Here, though, the real message is that you should expect to run into digital cases as well as traditional case interviews.

Luckily, despite any changes in specific format, you will still need to master the same fundamental skills and prepare in much the same way.

We’ll cover a few ways to help prepare for chatbot cases in section four. Ultimately, though, firms are looking for the same problem solving ability and mindset as a real interviewer. Especially as chatbots get better at mimicking a real interviewer, candidates who are well prepared for case cracking in general should have no problem with AI administered cases.

3.6.1. Automated fit interviews

Analogous to online cases, in recent years there has been a trend towards automated, “one way” fit interviews, with these typically being administered for consultancies by specialist contractors like HireVue or SparkHire.

These are kind of like Zoom interviews, but if the interviewer didn’t show up. Instead you will be given fit questions to answer and must record your answer in your computer webcam. Your response will then go on to be assessed by an algorithm, scoring both what you say and how you say it.

Again, with advances in AI, it is easy to imagine these automated interviews going from fully scripted interactions, where all candidates are asked the same list of questions, to a more interactive experience. Thus, we might soon arrive at a point where you are being grilled on the details of your stories - McKinsey PEI style - but by a bot rather than a human.

We include some tips on this kind of “one way” fit interview in section six here.

4. How to solve cases with the Problem-Driven Structure?

If you look around online for material on how to solve case studies, a lot of what you find will set out framework-based approaches. However, as we have mentioned, these frameworks tend to break down with more complex, unique cases - with these being exactly the kind of tough case studies you can expect to be given in your interviews.

To address this problem, the MyConsultingCoach team has synthesized a new approach to case cracking that replicates how top management consultants approach actual engagements.

MyConsultingCoach’s Problem Driven Structure approach is a universal problem solving method that can be applied to any business problem , irrespective of its nature.

As opposed to just selecting a generic framework for each case, the Problem Driven Structure approach works by generating a bespoke structure for each individual question and is a simplified version of the roadmap McKinsey consultants use when working on engagements.

The canonical seven steps from McKinsey on real projects are simplified to four for case interview questions, as the analysis required for a six-month engagement is somewhat less than that needed for a 45-minute case study. However, the underlying flow is the same (see the method in action in the video below)

Let's zoom in to see how our method actually works in more detail:

4.1. Identify the problem

Identifying the problem means properly understanding the prompt/question you are given, so you get to the actual point of the case.

This might sound simple, but cases are often very tricky, and many candidates irretrievably mess things up within the first few minutes of starting. Often, they won’t notice this has happened until they are getting to the end of their analysis. Then, they suddenly realise that they have misunderstood the case prompt - and have effectively been answering the wrong question all along!

With no time to go back and start again, there is nothing to do. Even if there were time, making such a silly mistake early on will make a terrible impression on their interviewer, who might well have written them off already. The interview is scuppered and all the candidate’s preparation has been for nothing.

This error is so galling as it is so readily avoidable.

Our method prevents this problem by placing huge emphasis on a full understanding of the case prompt. This lays the foundations for success as, once we have identified the fundamental, underlying problem our client is facing, we focus our whole analysis around finding solutions to this specific issue.

Now, some case interview prompts are easy to digest. For example, “Our client, a supermarket, has seen a decline in profits. How can we bring them up?”. However, many of the prompts given in interviews for top firms are much more difficult and might refer to unfamiliar business areas or industries. For example, “How much would you pay for a banking license in Ghana?” or “What would be your key areas of concern be when setting up an NGO?”

Don’t worry if you have no idea how you might go about tackling some of these prompts!

In our article on identifying the problem and in our full lesson on the subject in our MCC Academy course, we teach a systematic, four step approach to identifying the problem , as well as running through common errors to ensure you start off on the right foot every time!

This is summarised here:

Following this method lets you excel where your competitors mess up and get off to a great start in impressing your interviewer!

4.2. Build your problem driven structure

After you have properly understood the problem, the next step is to successfully crack a case is to draw up a bespoke structure that captures all the unique features of the case.

This is what will guide your analysis through the rest of the case study and is precisely the same method used by real consultants working on real engagements.

Of course, it might be easier here to simply roll out one an old-fashioned framework, and a lot of candidates will do so. This is likely to be faster at this stage and requires a lot less thought than our problem-driven structure approach.

However, whilst our problem driven structure approach requires more work from you, our method has the advantage of actually working in the kind of complex case studies where generic frameworks fail - that is exactly the kind of cases you can expect at an MBB interview .

Since we effectively start from first principles every time, we can tackle any case with the same overarching method. Simple or complex, every case is the same to you and you don’t have to gamble a job on whether a framework will actually work

4.2.1 Issue trees

Issue trees break down the overall problem into a set of smaller problems that you can then solve individually. Representing this on a diagram also makes it easy for both you and your interviewer to keep track of your analysis.

To see how this is done, let’s look at the issue tree below breaking down the revenues of an airline:

These revenues can be segmented as the number of customers multiplied by the average ticket price. The number of customers can be further broken down into a number of flights multiplied by the number of seats, times average occupancy rate. The node corresponding to the average ticket price can then be segmented further.

4.2.2 Hypothesis trees

Hypothesis trees are similar, the only difference being that rather than just trying to break up the issue into smaller issues you are assuming that the problem can be solved and you are formulating solutions.

In the example above, you would assume revenues can be increased by either increasing the average ticket price or the number of customers . You can then hypothesize that you can increase the average occupancy rate in three ways: align the schedule of short and long haul flights, run a promotion to boost occupancy in off-peak times, or offer early bird discounts.

4.2.3 Other structures:structured lists

Structured lists are simply subcategories of a problem into which you can fit similar elements. This McKinsey case answer starts off by identifying several buckets such as retailer response, competitor response, current capabilities and brand image and then proceeds to consider what could fit into these categories.

Buckets can be a good way to start the structure of a complex case but when using them it can be very difficult to be MECE and consistent, so you should always aim to then re-organize them into either an issue or a hypothesis tree.

It is worth noting that the same problem can be structured in multiple valid ways by choosing different means to segment the key issues. Ultimately all these lists are methods to set out a logical hierachy among elements.

4.2.4 Structures in practice

That said, not all valid structures are equally useful in solving the underlying problem. A good structure fulfils several requirements - including MECE-ness , level consistency, materiality, simplicity, and actionability. It’s important to put in the time to master segmentation, so you can choose a scheme isn’t only valid, but actually useful in addressing the problem.

After taking the effort to identify the problem properly, an advantage of our method is that it will help ensure you stay focused on that same fundamental problem throughout. This might not sound like much, but many candidates end up getting lost in their own analysis, veering off on huge tangents and returning with an answer to a question they weren’t asked.

Another frequent issue - particularly with certain frameworks - is that candidates finish their analysis and, even if they have successfully stuck to the initial question, they have not actually reached a definite solution. Instead, they might simply have generated a laundry list of pros and cons, with no clear single recommendation for action.

Clients employ consultants for actionable answers, and this is what is expected in the case interview. The problem driven structure excels in ensuring that everything you do is clearly related back to the key question in a way that will generate a definitive answer. Thus, the problem driven structure builds in the hypothesis driven approach so characteristic of real consulting practice.

You can learn how to set out your own problem driven structures in our article here and in our full lesson in the MCC Academy course.

4.2. Lead the analysis

A problem driven structure might ensure we reach a proper solution eventually, but how do we actually get there?

We call this step " leading the analysis ", and it is the process whereby you systematically navigate through your structure, identifying the key factors driving the issue you are addressing.

Generally, this will mean continuing to grow your tree diagram, further segmenting what you identify as the most salient end nodes and thus drilling down into the most crucial factors causing the client’s central problem.

Once you have gotten right down into the detail of what is actually causing the company’s issues, solutions can then be generated quite straightforwardly.

To see this process in action, we can return to our airline revenue example:

Let’s say we discover the average ticket price to be a key issue in the airline’s problems. Looking closer at the drivers of average ticket price, we find that the problem lies with economy class ticket prices. We can then further segment that price into the base fare and additional items such as food.

Having broken down the issue to such a fine-grained level and considering the 80/20 rule(see below), solutions occur quite naturally. In this case, we can suggest incentivising the crew to increase onboard sales, improving assortment in the plane, or offering discounts for online purchases.

Our article on leading the analysis is a great primer on the subject, with our video lesson in the MCC Academy providing the most comprehensive guide available.

4.4. Provide recommendations

So you have a solution - but you aren’t finished yet!

Now, you need to deliver your solution as a final recommendation.

This should be done as if you are briefing a busy CEO and thus should be a one minute, top-down, concise, structured, clear, and fact-based account of your findings.

The brevity of the final recommendation belies its importance. In real life consulting, the recommendation is what the client has potentially paid millions for - from their point of view, it is the only thing that matters.

In an interview, your performance in this final summing up of your case is going to significantly colour your interviewer’s parting impression of you - and thus your chances of getting hired!

So, how do we do it right?

Barbara Minto's Pyramid Principle elegantly sums up almost everything required for a perfect recommendation. The answer comes first , as this is what is most important. This is then supported by a few key arguments , which are in turn buttressed by supporting facts .

Across the whole recommendation, the goal isn’t to just summarise what you have done. Instead, you are aiming to synthesize your findings to extract the key "so what?" insight that is useful to the client going forward.

All this might seem like common sense, but it is actually the opposite of how we relay results in academia and other fields. There, we typically move from data, through arguments and eventually to conclusions. As such, making good recommendations is a skill that takes practice to master.

We can see the Pyramid Principle illustrated in the diagram below:

To supplement the basic Pyramid Principle scheme, we suggest candidates add a few brief remarks on potential risks and suggested next steps . This helps demonstrate the ability for critical self-reflection and lets your interviewer see you going the extra mile.

The combination of logical rigour and communication skills that is so definitive of consulting is particularly on display in the final recommendation.

Despite it only lasting 60 seconds, you will need to leverage a full set of key consulting skills to deliver a really excellent recommendation and leave your interviewer with a good final impression of your case solving abilities.

Our specific article on final recommendations and the specific video lesson on the same topic within our MCC Academy are great, comprehensive resources. Beyond those, our lesson on consulting thinking and our articles on MECE and the Pyramid Principle are also very useful.

4.5. What if I get stuck?

Naturally with case interviews being difficult problems there may be times where you’re unsure what to do or which direction to take. The most common scenario is that you will get stuck midway through the case and there are essentially two things that you should do:

• 1. Go back to your structure
• 2. Ask the interviewer for clarification

Your structure should always be your best friend - after all, this is why you put so much thought and effort into it: if it’s MECE it will point you in the right direction. This may seem abstract but let’s take the very simple example of a profitability issue: if you’ve started your analysis by segmenting profit into revenue minus costs and you’ve seen that the cost side of the analysis is leading you nowhere, you can be certain that the declining profit is due to a decline in revenue.

Similarly, when you’re stuck on the quantitative section, make sure that your framework for calculations is set up correctly (you can confirm this with the interviewer) and see what it is you’re trying to solve for: for example if you’re trying to find what price the client should sell their new t-shirt in order to break even on their investment, you should realize that what you’re trying to find is the break even point, so you can start by calculating either the costs or the revenues. You have all the data for the costs side and you know they’re trying to sell 10.000 pairs so you can simply set up the equation with x being the price.

As we’ve emphasised on several occasions, your consulting interview will be a dialogue. As such, if you don’t know what to do next or don’t understand something, make sure to ask the interviewer (and as a general rule always follow their prompts as they are trying to help, not trick you). This is especially true for the quantitative questions, where you should really understand what data you’re looking at before you jump into any calculations. Ideally you should ask your questions before you take time to formulate your approach but don’t be afraid to ask for further clarification if you really can’t make sense of what’s going on. It’s always good to walk your interviewer through your approach before you start doing the calculations and it’s no mistake to make sure that you both have the same understanding of the data. For example when confronted with the chart below, you might ask what GW (in this case gigawatt) means from the get-go and ask to confirm the different metrics (i.e. whether 1 GW = 1000 megawatts). You will never be penalised for asking a question like that.

5. What to remember in case interviews

If you’re new to case cracking you might feel a bit hopeless when you see a difficult case question, not having any idea where to start.

In fact though, cracking cases is much like playing chess. The rules you need to know to get started are actually pretty simple. What will make you really proficient is time and practice.

In this section, we’ll run through a high level overview of everything you need to know, linking to more detailed resources at every step.

5.1. An overall clear structure

You will probably hear this more than you care for but it is the most important thing to keep in mind as you start solving cases, as not only it is a key evaluation criterion but the greatest tool you will have at your disposal. The ability to build a clear structure in all aspects of the case will be the difference between breezing through a complicated case and struggling at its every step. Let’s look a bit closer at the key areas where you should be structured!

5.1.1 Structured notes

Every case interview starts with a prompt, usually verbal, and as such you will have to take some notes. And here is where your foray into structure begins, as the notes you take should be clear, concise and structured in a way that will allow you to repeat the case back to the interviewer without writing down any unnecessary information.

This may sound very basic but you should absolutely not be dismissive about it: taking clear and organized notes will allow you to navigate a case just like you would a powerpoint! While you should obviously adopt a system that you are comfortable with, what we found helps is to have separate sections for:

• The case brief
• Follow-up questions and answers
• Numerical data
• Case structure (the most crucial part when solving the case)
• Any scrap work during the case (usually calculations)

When solving the case - or, as we call it here, in the Lead the analysis step, it is highly recommended to keep on feeding and integrating your structure, so that you never get lost. Maintaining a clear high level view is one of the most critical skills in consulting: by constantly keeping track of where you are following your structure, you’ll never lose your focus on the end goal.

In the case of an interviewer-led case, you can also have separate sheets for each question (e.g. Question 1. What factors can we look at that drive profitability?). If you develop a system like this you’ll know exactly where to look for each point of data rather than rummage around in untidy notes. There are a couple more sections that you may have, depending on preference - we’ll get to these in the next sections.

5.1.2 Structured communication

There will be three main types of communication in cases:

• 1. Asking and answering questions
• 2. Walking the interviewer through your structure (either the case or calculation framework - we’ll get to that in a bit!)
• 3. Delivering your recommendation

Asking and answering questions will be the most common of these and the key thing to do before you speak is ask for some time to collect your thoughts and get organised. What you want to avoid is a ‘laundry list’ of questions or anything that sounds too much like a stream of consciousness.

Different systems work for different candidates but a sure-fire way of being organised is numbering your questions and answers. So rather than saying something like ‘I would like to ask about the business model, operational capacity and customer personas’ it’s much better to break it down and say something along the lines of ‘I’ve got three key questions. Firstly I would like to inquire into the business model of our client. Secondly I would like to ask about their operational capacity. Thirdly I would like to know more about the different customer personas they are serving’.

A similar principle should be applied when walking the interview through your structure, and this is especially true of online interviews (more and more frequent now) when the interviewer can’t see your notes. Even if you have your branches or buckets clearly defined, you should still use a numbering system to make it obvious to the interviewer. So, for example, when asked to identify whether a company should make an acquisition, you might say ‘I would like to examine the following key areas. Firstly the financial aspects of this issue, secondly the synergies and thirdly the client’s expertise’

The recommendation should be delivered top-down (see section 4.4 for specifics) and should employ the same numbering principle. To do so in a speedy manner, you should circle or mark the key facts that you encounter throughout the case so you can easily pull them out at the end.

5.1.3 Structured framework

It’s very important that you have a systematic approach - or framework - for every case. Let’s get one thing straight: there is a difference between having a problem-solving framework for your case and trying to force a case into a predetermined framework. Doing the former is an absolute must , whilst doing the latter will most likely have you unceremoniously dismissed.

We have seen there are several ways of building a framework, from identifying several categories of issues (or ‘buckets’) to building an issue or hypothesis tree (which is the most efficient type of framework). For the purpose of organization, we recommend having a separate sheet for the framework of the case, or, if it’s too much to manage, you can have it on the same sheet as the initial case prompt. That way you’ll have all the details as well as your proposed solution in one place.

5.1.4 Structured calculations

Whether it’s interviewer or candidate-led, at some point in the case you will get a bunch of numerical data and you will have to perform some calculations (for the specifics of the math you’ll need on consulting interviews, have a look at our Consulting Math Guide ). Here’s where we urge you to take your time and not dive straight into calculating! And here’s why: while your numerical agility is sure to impress interviewers, what they’re actually looking for is your logic and the calculations you need to perform in order to solve the problem . So it’s ok if you make a small mistake, as long as you’re solving for the right thing.

As such, make it easy for them - and yourself. Before you start, write down in steps the calculations you need to perform. Here’s an example: let’s say you need to find out by how much profits will change if variable costs are reduced by 10%. Your approach should look something like:

• 1. Calculate current profits: Profits = Revenues - (Variable costs + Fixed costs)
• 2. Calculate the reduction in variable costs: Variable costs x 0.9
• 3. Calculate new profits: New profits = Revenues - (New variable costs + Fixed costs)

Of course, there may be more efficient ways to do that calculation, but what’s important - much like in the framework section - is to show your interviewer that you have a plan, in the form of a structured approach. You can write your plan on the sheet containing the data, then perform the calculations on a scrap sheet and fill in the results afterward.

5.2. Common business knowledge and formulas

Although some consulting firms claim they don’t evaluate candidates based on their business knowledge, familiarity with basic business concepts and formulae is very useful in terms of understanding the case studies you are given in the first instance and drawing inspiration for structuring and brainstorming.

If you are coming from a business undergrad, an MBA or are an experienced hire, you might well have this covered already. For those coming from a different background, it may be useful to cover some.

Luckily, you don’t need a degree-level understanding of business to crack interview cases , and a lot of the information you will pick up by osmosis as you read through articles like this and go through cases.

However, some things you will just need to sit down and learn. We cover everything you need to know in some detail in our Case Academy Course course. However, some examples here of things you need to learn are:

• Basic accounting (particularly how to understand all the elements of a balance sheet)
• Basic economics
• Basic marketing
• Basic strategy

Below we include a few elementary concepts and formulae so you can hit the ground running in solving cases. We should note that you should not memorise these and indeed a good portion of them can be worked out logically, but you should have at least some idea of what to expect as this will make you faster and will free up much of your mental computing power. In what follows we’ll tackle concepts that you will encounter in the private business sector as well as some situations that come up in cases that feature clients from the NGO or governmental sector.

5.2.1 Business sector concepts

These concepts are the bread and butter of almost any business case so you need to make sure you have them down. Naturally, there will be specificities and differences between cases but for the most part here is a breakdown of each of them.

5.2.1.1. Revenue

The revenue is the money that the company brings in and is usually equal to the number of products they sell multiplied to the price per item and can be expressed with the following equation:

Revenue = Volume x Price

Companies may have various sources of revenue or indeed multiple types of products, all priced differently which is something you will need to account for. Let’s consider some situations. A clothing company such as Nike will derive most of their revenue from the number of products they sell times the average price per item. Conversely, for a retail bank revenue is measured as the volume of loans multiplied by the interest rate at which the loans are given out. As we’ll see below, we might consider primary revenues and ancillary revenues: in the case of a football club, we might calculate primary revenues by multiplying the number of tickets sold by the average ticket price, and ancillary revenues those coming from sales of merchandise (similarly, let’s say average t-shirt price times the number of t-shirts sold), tv rights and sponsorships.

These are but a few examples and another reminder that you should always aim to ask questions and understand the precise revenue structure of the companies you encounter in cases.

5.2.1.2. Costs

The costs are the expenses that a company incurs during its operations. Generally, they can be broken down into fixed and variable costs :

Costs = Fixed Costs + Variable Costs

As their name implies, fixed costs do not change based on the number of units produced or sold. For example, if you produce shoes and are renting the space for your factory, you will have to pay the rent regardless of whether you produce one pair or 100. On the other hand, variable costs depend on the level of activity, so in our shoe factory example they would be equivalent to the materials used to produce each pair of shoes and would increase the more we produce.

These concepts are of course guidelines used in order to simplify the analysis in cases, and you should be aware that in reality often the situation can be more complicated. Costs can also be quasi-fixed, in that they increase marginally with volume. Take the example of a restaurant which has a regular staff, incurring a fixed cost but during very busy hours or periods they also employ some part-time workers. This cost is not exactly variable (as it doesn’t increase with the quantity of food produced) but also not entirely fixed, as the number of extra hands will depend on how busy the restaurant is. Fixed costs can also be non-linear in nature. Let’s consider the rent in the same restaurant: we would normally pay a fixed amount every month, but if the restaurant becomes very popular we might need to rent out some extra space so the cost will increase.

5.2.1.3. Profit and profit margin

The profit is the amount of money a company is left with after it has paid all of its expenses and can be expressed as follows:

Profit = Revenue - Costs

It’s very likely that you will encounter a profitability issue in one of your cases, namely you will be asked to increase a company’s profit. There are two main ways of doing this: increasing revenues and reducing costs , so these will be the two main areas you will have to investigate. This may seem simple but what you will really need to understand in a case are the key drivers of a business (and this should be done through clarifying questions to the interviewer - just as a real consultant would question their client).

For example, if your client is an airline you can assume that the main source of revenue is sales of tickets, but you should inquire how many types of ticket the specific airline sells. You may naturally consider economy and business class tickets, but you may find out that there is a more premium option - such as first class - and several in-between options. Similarly to our football club example, there may be ancillary revenues from selling of food and beverage as well as advertising certain products or services on flights.

You may also come across the profit margin in cases. This is simply the percentage of profit compared to the revenue and can be expressed as follows:

Profit margin = Profit/Revenue x 100

5.2.1.4. Break-even point

An ancillary concept to profit, the break-even point is the moment where revenues equal costs making the profit zero and can be expressed as the following equation:

Revenues = Costs (Fixed costs + Variable costs)

This formula will be useful when you are asked questions such as ‘What is the minimum price I should sell product X?’ or ‘What quantity do I need to sell in order to recoup my investment?’. Let’s say an owner of a sandwich store asks us to figure out how many salami and cheese salami sandwiches she needs to sell in order to break even. She’s spending $4 on salami and $2 for cheese and lettuce per sandwich, and believes she can sell the sandwiches at around $7. The cost of utilities and personnel is around $5000 per month. We could lay this all out in the break-even equation:

7 x Q ( quantity ) = (4+2) x Q + 5000 ( variable + fixed costs )

In a different scenario, we may be asked to calculate the break-even price . Let’s consider our sandwich example and say our owner knows she has enough ingredients for about 5000 sandwiches per month but is not sure how much to sell them for. In that case, if we know our break-even equation, we can simply make the following changes:

P ( price ) x 5000 = (4+2) x 5000 + 5000

By solving the equation we get to the price of $7 per sandwich.

5.2.1.5. Market share and market size

We can also consider the market closely with profit, as in fact the company’s performance in the market is what drives profits. The market size is the total number of potential customers for a certain business or product, whereas the market share is the percentage of that market that your business controls (or could control, depending on the case).

There is a good chance you will have to estimate the market size in one of your case interviews and we get into more details on how to do that below. You may be asked to estimate this in either number of potential customers or total value . The latter simply refers to the number of customers multiplied by the average value of the product or service.

To calculate the market share you will have to divide the company’s share by the total market size and multiply by 100:

Note, though, that learning the very basics of business is the beginning rather than the end of your journey. Once you are able to “speak business” at a rudimentary level, you should try to “become fluent” and immerse yourself in reading/viewing/listening to as wide a variety of business material as possible, getting a feel for all kinds of companies and industries - and especially the kinds of problems that can come up in each context and how they are solved. The material put out by the consulting firms themselves is a great place to start, but you should also follow the business news and find out about different companies and sectors as much as possible between now and interviews. Remember, if you’re going to be a consultant, this should be fun rather than a chore!

5.3 Public sector and NGO concepts

As we mentioned, there will be some cases (see section 6.6 for a more detailed example) where the key performance indicators (or KPIs in short) will not be connected to profit. The most common ones will involve the government of a country or an NGO, but they can be way more diverse and require more thought and application of first principles. We have laid out a couple of the key concepts or KPIs that come up below

5.3.1 Quantifiability

In many such scenarios you will be asked to make an important strategic decision of some kind or to optimise a process. Of course these are not restricted to non-private sector cases but this is where they really come into their own as there can be great variation in the type of decision and the types of field.

While there may be no familiar business concepts to anchor yourself onto, a concept that is essential is quantifiability . This means, however qualitative the decision might seem, consultants rely on data so you should always aim to have aspects of a decision that can be quantified, even if the data doesn’t present itself in a straightforward manner.

Let’s take a practical example. Your younger sibling asks you to help them decide which university they should choose if they want to study engineering. One way to structure your approach would be to segment the problem into factors affecting your sibling’s experience at university and experience post-university. Within the ‘at uni’ category you might think about the following:

• Financials : How much are tuition costs and accommodation costs?
• Quality of teaching and research : How are possible universities ranked in the QS guide based on teaching and research?
• Quality of resources : How well stocked is their library, are the labs well equipped etc.?
• Subject ranking : How is engineering at different unis ranked?
• Life on campus and the city : What are the living costs in the city where the university is based? What are the extracurricular opportunities and would your sibling like to live in that specific city based on them?

Within the ‘out of uni’ category you might think about:

• Exit options : What are the fields in which your sibling could be employed and how long does it take the average student of that university to find a job?
• Alumni network : What percentage of alumni are employed by major companies?
• Signal : What percentage of applicants from the university get an interview in major engineering companies and related technical fields?

You will perhaps notice that all the buckets discussed pose quantifiable questions meant to provide us with data necessary to make a decision. It’s no point to ask ‘Which university has the nicest teaching staff?’ as that can be a very subjective metric.

5.3.1 Impact

Another key concept to consider when dealing with sectors other than the private one is how impactful a decision or a line of inquiry is on the overarching issue , or whether all our branches in our issue tree have a similar impact. This can often come in the form of impact on lives, such as in McKinsey’s conservation case discussed below, namely how many species can we save with our choice of habitat.

5.4 Common consulting concepts

Consultants use basic business concepts on an every day basis, as they help them articulate their frameworks to problems. However, they also use some consulting specific tools to quality check their analysis and perform in the most efficient way possible. These principles can be applied to all aspects of a consultant’s work, but for brevity we can say they mostly impact a consultant’s systematic approach and communication - two very important things that are also tested in case interviews. Therefore, it’s imperative that you not only get to know them, but learn how and when to use them as they are at the very core of good casing. They are MECE-ness, the Pareto Principle and the Pyramid principle and are explained briefly below - you should, however, go on to study them in-depth in their respective articles.

Perhaps the central pillar of all consulting work and an invaluable tool to solve cases, MECE stands for Mutually Exclusive and Collectively Exhaustive . It can refer to any and every aspect in a case but is most often used when talking about structure. We have a detailed article explaining the concept here , but the short version is that MECE-ness ensures that there is no overlap between elements of a structure (i.e. the Mutually Exclusive component) and that it covers all the drivers or areas of a problem (Collectively Exhaustive). It is a concept that can be applied to any segmentation when dividing a set into subsets that include it wholly but do not overlap.

Let’s take a simple example and then a case framework example. In simple terms, when we are asked to break down the set ‘cars’ into subsets, dividing cars into ‘red cars’ and ‘sports cars’ is neither mutually exclusive (as there are indeed red sports cars) nor exhaustive of the whole set (i.e. there are also yellow non-sports cars that are not covered by this segmentation). A MECE way to segment would be ‘cars produced before 2000’ and ‘cars produced after 2000’ as this segmentation allows for no overlap and covers all the cars in existence.

Dividing cars can be simple, but how can we ensure MECEness in a case-interview a.k.a. a business situation. While the same principles apply, a good tip to ensure that your structure is MECE is to think about all the stakeholders - i.e. those whom a specific venture involves.

Let’s consider that our client is a soda manufacturer who wants to move from a business-to-business strategy, i.e. selling to large chains of stores and supermarkets, to a business-to-consumer strategy where it sells directly to consumers. In doing so they would like to retrain part of their account managers as direct salespeople and need to know what factors to consider.

A stakeholder-driven approach would be to consider the workforce and customers and move further down the issue tree, thinking about individual issues that might affect them. In the case of the workforce, we might consider how the shift would affect their workload and whether it takes their skillset into account. As for the customers, we might wonder whether existing customers would be satisfied with this move: will the remaining B2B account managers be able to provide for the needs of all their clients and will the fact that the company is selling directly to consumers now not cannibalise their businesses? We see how by taking a stakeholder-centred approach we can ensure that every single perspective and potential issue arising from it is fully covered.

5.4.2 The Pareto Principle

Also known as the 80/20 rule, this principle is important when gauging the impact of a decision or a factor in your analysis. It simply states that in business (but not only) 80% of outcomes come from 20% of causes. What this means is you can make a few significant changes that will impact most of your business organisation, sales model, cost structure etc.

Let’s have a look at 3 quick examples to illustrate this:

• 80% of all accidents are caused by 20% of drivers
• 20% of a company’s products account for 80% of the sales
• 80% of all results in a company are driven by 20% of its employees

The 80/20 rule will be a very good guide line in real engagements as well as case interviews, as it will essentially point to the easiest and most straightforward way of doing things. Let’s say one of the questions in a case is asking you to come up with an approach to understand the appeal of a new beard trimmer. Obviously you can’t interview the whole male population so you might think about setting up a webpage and asking people to comment their thoughts. But what you would get would be a laundry list of difficult to sift through data.

Using an 80/20 approach you would segment the population based on critical factors (age groups, grooming habits etc.) and then approach a significant sample size of each (e.g. 20), analysing the data and reaching a conclusion.

5.4.3 The Pyramid Principle

This principle refers to organising your communication in a top-down , efficient manner. While this is generally applicable, the pyramid principle will most often be employed when delivering the final recommendation to your client. This means - as is implicit in the name - that you would organise your recommendation (and communication in general) as a pyramid, stating the conclusion or most important element at the top then go down the pyramid listing 3 supporting arguments and then further (ideally also 3) supporting arguments for those supporting arguments.

Let’s look at this in practice: your client is a German air-conditioning unit manufacturer who was looking to expand into the French market. However, after your analysis you’ve determined that the market share they were looking to capture would not be feasible. A final recommendation using the Pyramid Principle would sound something like this: ‘I recommend that we do not enter the German market for the following three reasons. Firstly, the market is too small for our ambitions of $50 million. Secondly the market is heavily concentrated, being controlled by three major players and our 5 year goal would amount to controlling 25% of the market, a share larger than that of any of the players. Thirdly, the alternative of going into the corporate market would not be feasible, as it has high barriers to entry.Then, if needed, we could delve deeper into each of our categories

6. Case examples or building blocks?

As we mentioned before, in your preparation you will undoubtedly find preparation resources that claim that there are several standard types of cases and that there is a general framework that can be applied to each type of case. While there are indeed cases that are straightforward at least in appearance and seemingly invite the application of such frameworks, the reality is never that simple and cases often involve multiple or more complicated components that cannot be fitted into a simple framework.

At MCC we don’t want you to get into the habit of trying to identify which case type you’re dealing with and pull out a framework, but we do recognize that there are recurring elements in frameworks that are useful - such as the profitability of a venture (with its revenues and costs), the valuation of a business, estimating and segmenting a market and pricing a product.

We call these building blocks because they can be used to build case frameworks but are not a framework in and of themselves, and they can be shuffled around and rearranged in any way necessary to be tailored to our case. Hence, our approach is not to make you think in terms of case types but work from first principles and use these building blocks to build your own framework. Let’s take two case prompts to illustrate our point.

The first is from the Bain website, where the candidate is asked whether they think it’s a good idea for their friend to open a coffee shop in Cambridge UK (see the case here ). The answer framework provided here is a very straightforward profitability analysis framework, examining the potential revenues and potential costs of the venture:

While this is a good point to start (especially taken together with the clarifying questions), we will notice that this approach will need more tailoring to the case - for example the quantity of coffee will be determined by the market for coffee drinkers in Cambridge, which we have to determine based on preference. We are in England so a lot of people will be drinking tea but we are in a university town so perhaps more people than average are drinking coffee as it provides a better boost when studying. All these are some much needed case-tailored hypotheses that we can make based on the initial approach.

Just by looking at this case we might be tempted to say that we can just take a profitability case and apply it without any issues. However, this generic framework is just a starting point and in reality we would need to tailor it much further in the way we had started to do in order to get to a satisfactory answer. For example, the framework itself doesn’t cover aspects such as the customer’s expertise: does the friend have any knowledge of the coffee business, such as where to source coffee and how to prepare it? Also, we could argue there may be some legal factors to consider here, such as any approvals that they might need from the city council to run a coffee shop on site, or some specific trade licences that are not really covered in the basic profitability framework.

Let’s take a different case , however, from the McKinsey website. In this scenario, the candidate is being asked to identify some factors in order to choose where to focus the client’s conservation efforts. Immediately we can realise that this case doesn’t lend itself to any pre-packaged framework and we will need to come up with something from scratch - and take a look at McKinsey’s answer of the areas to focus on:

We notice immediately that this framework is 100% tailored to the case - of course there are elements which we encounter in other cases, such as costs and risks but again these are applied in an organic way. It’s pretty clear that while no standard framework would work in this case, the aforementioned concepts - costs and risks - and the way to approach them (a.k.a building blocks ) are fundamentally similar throughout cases (with the obvious specificities of each case).

In what follows, we’ll give a brief description of each building block starting from the Bain example discussed previously, in order to give you a general idea of what they are and their adaptability, but you should make sure to follow the link to the in-depth articles to learn all their ins and outs.

6.1 Estimates and segmentation

This building block will come into play mostly when you’re thinking about the market for a certain product (but make sure to read the full article for more details). Let’s take our Bain Cambridge coffee example. As we mentioned under the quantity bucket we need to understand what the market size for coffee in Cambridge would be - so we can make an estimation based on segmentation .

The key to a good estimation is the ability to logically break down the problem into more manageable pieces. This will generally mean segmenting a wider population to find a particular target group. We can start off with the population of Cambridge - which we estimate at 100.000. In reality the population is closer to 150.000 but that doesn’t matter - the estimation has to be reasonable and not accurate , so unless the interviewer gives you a reason to reconsider you can follow your instinct. We can divide that into people who do and don’t drink coffee. Given our arguments before, we can conclude that 80% of those, so 80.000 drink coffee. Then we can further segment into those who drink regularly - let’s say every day - and those who drink occasionally - let’s say once a week. Based on the assumptions before about the student population needing coffee to function, and with Cambridge having a high student population, we can assume that 80% of those drinking coffee are regular drinkers, so that would be 64.000 regular drinkers and 16.000 occasional drinkers. We can then decide whom we want to target what our strategy needs to be:

This type of estimation and segmentation can be applied to any case specifics - hence why it is a building block.

6.2 Profitability

We had several looks at this building block so far (see an in-depth look here ) as it will show up in most scenarios, since profit is a key element in any company’s strategy. As we have seen, the starting point to this analysis is to consider both the costs and revenues of a company, and try to determine whether revenues need to be improved or whether costs need to be lowered. In the coffee example, the revenues are dictated by the average price per coffe x the number of coffees sold , whereas costs can be split into fixed and variable .

Some examples of fixed costs would be the rent for the stores and the cost of the personnel and utilities, while the most obvious variable costs would be the coffee beans used and the takeaway containers (when needed). We may further split revenues in this case into Main revenues - i.e. the sales of coffee - and Ancillary revenues , which can be divided into Sales of food products (sales of pastries, sandwiches etc., each with the same price x quantity schema) and Revenues from events - i.e renting out the coffee shop to events and catering for the events themselves. Bear in mind that revenues will be heavily influenced by the penetration rate , i.e. the share of the market which we can capture.

6.3 Pricing

Helping a company determine how much they should charge for their goods or services is another theme that comes up frequently in cases. While it may seem less complicated than the other building blocks, we assure you it’s not - you will have to understand and consider several factors, such as the costs a company is incurring, their general strategic positioning, availability, market trends as well as the customers’ willingness to pay (or WTP in short) - so make sure to check out our in-depth guide here .

In our example, we may determine that the cost per cup (coffee beans, staff, rent) is £1. We want to be student friendly so we should consider how much students would want to pay for a coffee as well as how much are competitors are charging. Based on those factors, it would be reasonable to charge on average £2 per cup of coffee. It’s true that our competitors are charging £3 but they are targeting mostly the adult market, whose willingness to pay is higher, so their pricing model takes that into account as well as the lower volume of customers in that demographic.

6.4. Valuation

A variant of the pricing building block, a valuation problem generally asks the candidate to determine how much a client should pay for a specific company (the target of an acquisition) as well as what other factors to consider. The two most important factors (but not the only ones - for a comprehensive review see our Valuation article ) to consider are the net present value (in consulting interviews usually in perpetuity) and the synergies .

In short, the net present value of a company is how much profit it currently brings in, divided by how much that cash flow will depreciate in the future and can be represented with the equation below:

The synergies refer to what could be achieved should the companies operate as one, and can be divided into cost and revenue synergies .

Let’s expand our coffee example a bit to understand these. Imagine that our friend manages to open a chain of coffee shops in Cambridge and in the future considers acquiring a chain of take-out restaurants. The most straightforward example of revenue synergies would be cross-selling, in this case selling coffee in the restaurants as well as in the dedicated stores, and thus getting an immediate boost in market share by using the existing customers of the restaurant chain. A cost synergy would be merging the delivery services of the two businesses to deliver both food and coffee, thus avoiding redundancies and reducing costs associated with twice the number of drivers and vehicles.

6.5. Competitive interaction

This component of cases deals with situations where the market in which a company is operating changes and the company must decide what to do. These changes often have to do with a new player entering the market (again for more details make sure to dive into the Competitive Interaction article ).

Let’s assume that our Cambridge coffee shop has now become a chain and has flagged up to other competitors that Cambridge is a blooming market for coffee. As such, Starbucks has decided to open a few stores in Cambridge themselves, to test this market. The question which might be posed to a candidate is what should our coffee chain do. One way (and a MECE one) to approach the problem is to decide between doing something and doing nothing . We might consider merging with another coffee chain and pooling our resources or playing to our strengths and repositioning ourselves as ‘your student-friendly, shop around the corner’. Just as easily we may just wait the situation out and see whether indeed Starbucks is cutting into our market share - after all, the advantages of our product and services might speak for themselves and Starbucks might end up tanking. Both of these are viable options if argued right and depending on the further specifics of the case.

6.6. Special cases

Most cases deal with private sectors, where the overarching objective entails profit in some form. However, as hinted before, there are cases which deal with other sectors where there are other KPIs in place . The former will usually contain one or several of these building blocks whereas the latter will very likely have neither. This latter category is arguably the one that will stretch your analytical and organisational skills to the limit, since there will be very little familiarity that you can fall back on (McKinsey famously employs such cases in their interview process).

So how do we tackle the structure for such cases? The short answer would be starting from first principles and using the problem driven structure outlined above, but let’s look at a quick example in the form of a McKinsey case :

The first question addressed to the candidate is the following:

This is in fact asking us to build a structure for the case. So what should we have in mind here? Most importantly, we should start with a structure that is MECE and we should remember to do that by considering all the stakeholders . They are on the one hand the government and affiliated institutions and on the other the population. We might then consider which issues might arise for each shareholder and what the benefits for them would be, as well as the risks. This approach is illustrated in the answer McKinsey provides as well:

More than anything, this type of case shows us how important it is to practise and build different types of structures, and think about MECE ways of segmenting the problem.

7. How Do I prepare for case interviews

In consulting fashion, the overall preparation can be structured into theoretical preparation and practical preparation , with each category then being subdivided into individual prep and prep with a partner .

As a general rule, the level and intensity of the preparation will differ based on your background - naturally if you have a business background (and have been part of a consulting club or something similar) your preparation will be less intensive than if you’re starting from scratch. The way we suggest you go about it is to start with theoretical preparation , which means learning about case interviews, business and basic consulting concepts (you can do this using free resources - such as the ones we provide - or if you want a more through preparation you can consider joining our Case Academy as well).

You can then move on to the practical preparation which should start with doing solo cases and focusing on areas of improvement, and then move on to preparation with a partner , which should be another candidate or - ideally - an ex-consultant.

Let’s go into more details with respect to each type of preparation.

7.1. Solo practice

The two most important areas of focus in sole preparation are:

• Mental math

As we mentioned briefly, the best use of your time is to focus on solving cases. You can start with cases listed on MBB sites since they are clearly stated and have worked solutions as well (e.g. Bain is a good place to start) and then move to more complex cases (our Case Library also offers a range of cases of different complexities). To build your confidence, start out on easier case questions, work through with the solutions, and don't worry about time. As you get better, you can move on to more difficult cases and try to get through them more quickly. You should practice around eight case studies on your own to build your confidence.

Another important area of practice is your mental mathematics as this skill will considerably increase your confidence and is neglected by many applicants - much to their immediate regret in the case interview. Find our mental math tool here or in our course, and practice at least ten minutes per day, from day one until the day before the interview.

7.2. Preparation with a partner

There are aspects of an interview - such as asking clarifying questions - which you cannot do alone and this is why, after you feel comfortable, you should move on to practice with another person. There are two options here:

• Practicing with a peer
• Practicing with an ex-consultant

In theory they can be complementary - especially if you’re peer is also preparing for consulting interviews - and each have advantages and disadvantages. A peer is likely to practice with you for free for longer, however you may end up reinforcing some bad habits or unable to get actionable feedback. A consultant will be able to provide you the latter but having their help for the same number of hours as a peer will come at a higher cost. Let’s look at each option in more detail.

7.2.1. Peer preparation

Once you have worked through eight cases solo, you should be ready to simulate the interview more closely and start working with another person.

Here, many candidates turn to peer practice - that is, doing mock case interviews with friends, classmates or others also applying to consulting. If you’re in university, and especially in business school, there will very likely be a consulting club for you to join and do lots of case practice with. If you don’t have anyone to practice, though, or if you just want to get a bit more volume in with others, our free meeting board lets you find fellow applicants from around the world with whom to practice. We recommend practicing around 10 to 15 ‘live’ cases to really get to a point where you feel comfortable.

7.2.2. Preparation with a consultant

You can do a lot practising by yourself and with peers. However, nothing will bring up your skills so quickly and profoundly as working with a real consultant.

Perhaps think about it like boxing. You can practice drills and work on punch bags all you want, but at some point you need to get into the ring and do some actual sparring if you ever want to be ready to fight.

Practicing with an ex consultant is essentialy a simulation of an interview. Of course, it isn’t possible to secure the time of experienced top-tier consultants for free. However, when considering whether you should invest to boost your chances of success, it is worth considering the difference in your salary over even just a few years between getting into a top-tier firm versus a second-tier one. In the light of thousands in increased annual earnings (easily accumulating into millions over multiple years), it becomes clear that getting expert interview help really is one of the best investments you can make in your own future.

Should you decide to make this step, MyConsultingCoach can help, offering bespoke mentoring programmes , where you are paired with a 5+ year experienced, ex-MBB mentor of your choosing, who will then oversee your whole case interview preparation from start to finish - giving you your best possible chance of landing a job!

7.3. Practice for online interviews

Standard preparation for interview case studies will carry directly over to online cases.

However, if you want to do some more specific prep, you can work through cases solo to a timer and using a calculator and/or Excel (online cases generally allow calculators and second computers to help you, whilst these are banned in live case interviews).

Older PST-style questions also make great prep, but a particularly good simulation is the self-assessment tests included in our Case Academy course . These multiple choice business questions conducted with a strict time limit are great preparation for the current crop of online cases.

7.4. Fit interviews

As we’ve noted, even something billed as a case interview is very likely to contain a fit interview as a subset.

We have an article on fit interviews and also include a full set of lessons on how to answer fit questions properly as a subset of our comprehensive Case Academy course .

Here though, the important thing to convey is that you take preparing for fit questions every bit as seriously as you do case prep.

Since they sound the same as you might encounter when interviewing for other industries, the temptation is to regard these as “just normal interview questions”.

However, consulting firms take your answers to these questions a good deal more seriously than elsewhere.

This isn’t just for fluffy “corporate culture” reasons. The long hours and close teamwork, as well as the client-facing nature of management consulting, mean that your personality and ability to get on with others is going to be a big part of making you a tolerable and effective co-worker.

If you know you’ll have to spend 14+ hour working days with someone you hire and that your annual bonus depends on them not alienating clients, you better believe you’ll pay attention to their character in interview.

There are also hard-nosed financial reasons for the likes of McKinsey, Bain and BCG to drill down so hard on your answers.

In particular, top consultancies have huge issues with staff retention. The average management consultant only stays with these firms for around two years before they have moved on to a new industry.

In some cases, consultants bail out because they can’t keep up with the arduous consulting lifestyle of long hours and endless travel. In many instances, though, departing consultants are lured away by exit opportunities - such as the well trodden paths towards internal strategy roles, private equity or becoming a start-up founder.

Indeed, many individuals will intentionally use a two year stint in consulting as something like an MBA they are getting paid for - giving them accelerated exposure to the business world and letting them pivot into something new.

Consulting firms want to get a decent return on investment for training new recruits. Thus, they want hires who not only intend to stick with consulting longer-term, but also have a temperament that makes this feasible and an overall career trajectory where it just makes sense for them to stay put.

This should hammer home the point that, if you want to get an offer, you need to be fully prepared to answer fit questions - and to do so excellently - any time you have a case interview.

8. Interview day - what to expect, with tips

Of course, all this theory is well and good, but a lot of readers might be concerned about what exactly to expect in real life . It’s perfectly reasonable to want to get as clear a picture as possible here - we all want to know what we are going up against when we face a new challenge!

Indeed, it is important to think about your interview in more holistic terms, rather than just focusing on small aspects of analysis. Getting everything exactly correct is less important than the overall approach you take to reasoning and how you communicate - and candidates often lose sight of this fact.

In this section, then, we’ll run through the case interview experience from start to finish, directing you to resources with more details where appropriate. As a supplement to this, the following video from Bain is excellent. It portrays an abridged version of a case interview, but is very useful as a guide to what to expect - not just from Bain, but from McKinsey, BCG and any other high-level consulting firm.

8.1. Getting started

Though you might be shown through to the office by a staff member, usually your interviewer will come and collect you from a waiting area. Either way, when you first encounter them, you should greet your interviewer with a warm smile and a handshake (unless they do not offer their hand). Be confident without verging into arrogance. You will be asked to take a seat in the interviewer’s office, where the interview can then begin.

8.1.1. First impressions

In reality, your assessment begins before you even sit down at your interviewer’s desk. Whether at a conscious level or not, the impression you make within the first few seconds of meeting your interviewer is likely to significantly inform the final hiring decision (again, whether consciously or not).

Your presentation and how you hold yourself and behave are all important . If this seems strange, consider that, if hired, you will be personally responsible for many clients’ impressions of the firm. These things are part of the job! Much of material on the fit interview is useful here, whilst we also cover first impressions and presentation generally in our article on what to wear to interview .

As we have noted above, your interview might start with a fit segment - that is, with the interviewer asking questions about your experiences, your soft skills, and motivation to want to join consulting generally and that firm in particular. In short, the kinds of things a case study can’t tell them about you. We have a fit interview article and course to get you up to speed here.

8.1.2. Down to business

Following an initial conversation, your interviewer will introduce your case study , providing a prompt for the question you have to answer. You will have a pen and paper in front of you and should (neatly) note down the salient pieces of information (keep this up throughout the interview).

It is crucial here that you don’t delve into analysis or calculations straight away . Case prompts can be tricky and easy to misunderstand, especially when you are under pressure. Rather, ask any questions you need to fully understand the case question and then validate that understanding with the interviewer before you kick off any analysis. Better to eliminate mistakes now than experience that sinking feeling of realising you have gotten the whole thing wrong halfway through your case!

This process is covered in our article on identifying the problem and in greater detail in our Case Academy lesson on that subject.

8.1.3. Analysis

Once you understand the problem, you should take a few seconds to set your thoughts in order and draw up an initial structure for how you want to proceed. You might benefit from utilising one or more of our building blocks here to make a strong start. Present this to your interviewer and get their approval before you get into the nuts and bolts of analysis.

We cover the mechanics of how to structure your problem and lead the analysis in our articles here and here and more thoroughly in the MCC Case Academy . What it is important to convey here, though, is that your case interview is supposed to be a conversation rather than a written exam . Your interviewer takes a role closer to a co-worker than an invigilator and you should be conversing with them throughout.

Indeed, how you communicate with your interviewer and explain your rationale is a crucial element of how you will be assessed. Case questions in general, are not posed to see if you can produce the correct answer, but rather to see how you think . Your interviewer wants to see you approach the case in a structured, rational fashion. The only way they are going to know your thought processes, though, is if you tell them!

To demonstrate this point, here is another excellent video from Bain, where candidates are compared.

Note that multiple different answers to each question are considered acceptable and that Bain is primarily concerned with the thought processes of the candidate’s exhibit .

Another reason why communication is absolutely essential to case interview success is the simple reason that you will not have all the facts you need to complete your analysis at the outset. Rather, you will usually have to ask the interviewer for additional data throughout the case to allow you to proceed .

NB: Don't be let down by your math!

Your ability to quickly and accurately interpret these charts and other figures under pressure is one of the skills that is being assessed. You will also need to make any calculations with the same speed and accuracy (without a calculator!). As such, be sure that you are up to speed on your consulting math .

8.1.4. Recommendation

Finally, you will be asked to present a recommendation. This should be delivered in a brief, top-down "elevator pitch" format , as if you are speaking to a time-pressured CEO. Again here, how you communicate will be just as important as the details of what you say, and you should aim to speak clearly and with confidence.

For more detail on how to give the perfect recommendation, take a look at our articles on the Pyramid Principle and providing recommendations , as well the relevant lesson within MCC Academy .

8.1.5. Wrapping up

After your case is complete, there might be a few more fit questions - including a chance for you to ask some questions of the interviewer . This is your opportunity to make a good parting impression.

We deal with the details in our fit interview resources. However, it is always worth bearing in mind just how many candidates your interviewers are going to see giving similar answers to the same questions in the same office. A pretty obvious pre-requisite to being considered for a job is that your interviewer remembers you in the first place. Whilst you shouldn't do something stupid just to be noticed, asking interesting parting questions is a good way to be remembered.

Now, with the interview wrapped up, it’s time to shake hands, thank the interviewer for their time and leave the room .

You might have other interviews or tests that day or you might be heading home. Either way, if know that you did all you could to prepare, you can leave content in the knowledge that you have the best possible chance of receiving an email with a job offer. This is our mission at MCC - to provide all the resources you need to realise your full potential and land your dream consulting job!

8.2. Remote and one-way interview tips

Zoom case interviews and “one-way” automated fit interviews are becoming more common as selection processes are increasingly remote, with these new formats being accompanied by their own unique challenges.

Obviously you won’t have to worry about lobbies and shaking hands for a video interview. However, a lot remains the same. You still need to do the same prep in terms of getting good at case cracking and expressing your fit answers. The specific considerations around remote interviews are, in effect, around making sure you come across as effectively as you would in person.

8.2.1. Connection

It sounds trivial, but a successful video interview of any kind presupposes a functioning computer with a stable and sufficient internet connection.

Absolutely don’t forget to have your laptop plugged in, as your battery will definitely let you down mid-interview. Similarly, make sure any housemates or family know not to use the microwave, vacuum cleaner or anything else that makes wifi cut out (or makes a lot of noise, obviously)

If you have to connect on a platform you don’t use much (for example, if it’s on Teams and you’re used to Zoom), make sure you have the up to date version of the app in advance, rather than having to wait for an obligatory download and end up late to join. Whilst you’re at it, make sure you’re familiar with the controls etc. At the risk of being made fun of, don’t be afraid to have a practice call with a friend.

8.2.2. Dress

You might get guidance on a slightly more relaxed dress code for a Zoom interview. However, if in doubt, dress as you would for the real thing (see our article here ).

Either way, always remember that presentation is part of what you are being assessed on - the firm needs to know you can be presentable for clients. Taking this stuff seriously also shows respect for your interviewer and their time in interviewing you.

8.2.3. Lighting

An aspect of presentation that you have to devote some thought to for a Zoom interview is your lighting.

Hopefully, you long ago nailed a lighting set-up during the Covid lockdowns. However, make sure to check your lighting in advance with your webcam - bearing in mind what time if day your interview actually is. If your interview is late afternoon, don’t just check in the morning. Make sure you aren’t going to be blinded from light coming in a window behind your screen, or that you end up with the weird shadow stripes from blinds all over your face.

Natural light is always best, but if there won’t be much of that during your interview, you’ll likely want to experiment with moving some lamps around.

8.2.4. Clarity

The actual stories you tell in an automated “one-way” fit interview will be the same as for a live equivalent. If anything, things should be easier, as you can rattle off a practised monologue without an interviewer interrupting you to ask for clarifications.

You can probably also assume that the algorithm assessing your performance is sufficiently capable that it will be observing you at much the same level as a human interviewer. However, it is probably still worth speaking as clearly as possible with these kinds of interviews and paying extra attention to your lighting to ensure that your face is clearly visible.

No doubt the AIs scoring these interviews are improving all the time, but you still want to make their job as easy as possible. Just think about the same things as you would with a live Zoom interview, but more so.

9. How we can help

There are lots of great free resources on this site to get you started with preparation, from all our articles on case solving and consulting skills to our free case library and peer practice meeting board .

To step your preparation up a notch, though, our Case Academy course will give you everything you need to know to solve the most complex of cases - whether those are in live interviews, with chatbots, written tests or any other format.

Whatever kind of case you end up facing, nothing will bring up your skillset faster than the kind of acute, actionable feedback you can get from a mock case interview a real, MBB consultant. Whilst it's possible to get by without this kind of coaching, it does tend to be the biggest single difference maker for successful candidates.

You can find out more on our coaching page:

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Of course, for those looking for a truly comprehensive programme, with a 5+ year experienced MBB consultant overseeing their entire prep personally, from networking and applications right through to your offer, we have our mentoring programmes.

You can read more here:

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• Data Science Interview Questions

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Introduction:

Data science is an interdisciplinary field that mines raw data, analyses it, and comes up with patterns that are used to extract valuable insights from it. Statistics, computer science, machine learning, deep learning, data analysis, data visualization, and various other technologies form the core foundation of data science.

Over the years, data science has gained widespread importance due to the importance of data. Data is considered the new oil of the future which when analyzed and harnessed properly can prove to be very beneficial to the stakeholders. Not just this, a data scientist gets exposure to work in diverse domains, solving real-life practical problems all by making use of trendy technologies. The most common real-time application is fast delivery of food in apps such as Uber Eats by aiding the delivery person to show the fastest possible route to reach the destination from the restaurant. 

Data Science is also used in item recommendation systems in e-commerce sites like Amazon, Flipkart, etc which recommend the user what item they can buy based on their search history. Not just recommendation systems, Data Science is becoming increasingly popular in fraud detection applications to detect any fraud involved in credit-based financial applications. A successful data scientist can interpret data, perform innovation and bring out creativity while solving problems that help drive business and strategic goals. This makes it the most lucrative job of the 21st century.

In this article, we will explore what are the most commonly asked Data Science Technical Interview Questions which will help both aspiring and experienced data scientists.

Data Science Interview Questions for Freshers

Data science interview questions for experienced, frequently asked questions, data science mcq, 1. what is data science.

An interdisciplinary field that constitutes various scientific processes, algorithms, tools, and machine learning techniques working to help find common patterns and gather sensible insights from the given raw input data using statistical and mathematical analysis is called Data Science.

The following figure represents the life cycle of data science.

• It starts with gathering the business requirements and relevant data.
• Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture.
• Data processing does the task of exploring the data, mining it, and analyzing it which can be finally used to generate the summary of the insights extracted from the data.
• Once the exploratory steps are completed, the cleansed data is subjected to various algorithms like predictive analysis, regression, text mining, recognition patterns, etc depending on the requirements.
• In the final stage, the results are communicated to the business in a visually appealing manner. This is where the skill of data visualization, reporting, and different business intelligence tools come into the picture. Learn More .

2. What is the difference between data analytics and data science?

• Data science involves the task of transforming data by using various technical analysis methods to extract meaningful insights using which a data analyst can apply to their business scenarios.
• Data analytics deals with checking the existing hypothesis and information and answers questions for a better and effective business-related decision-making process.
• Data Science drives innovation by answering questions that build connections and answers for futuristic problems. Data analytics focuses on getting present meaning from existing historical context whereas data science focuses on predictive modeling.
• Data Science can be considered as a broad subject that makes use of various mathematical and scientific tools and algorithms for solving complex problems whereas data analytics can be considered as a specific field dealing with specific concentrated problems using fewer tools of statistics and visualization.

The following Venn diagram depicts the difference between data science and data analytics clearly:

3. What are some of the techniques used for sampling? What is the main advantage of sampling?

Data analysis can not be done on a whole volume of data at a time especially when it involves larger datasets. It becomes crucial to take some data samples that can be used for representing the whole population and then perform analysis on it. While doing this, it is very much necessary to carefully take sample data out of the huge data that truly represents the entire dataset.

There are majorly two categories of sampling techniques based on the usage of statistics, they are:

• Probability Sampling techniques: Clustered sampling, Simple random sampling, Stratified sampling.
• Non-Probability Sampling techniques: Quota sampling, Convenience sampling, snowball sampling, etc.

4. List down the conditions for Overfitting and Underfitting.

Overfitting: The model performs well only for the sample training data. If any new data is given as input to the model, it fails to provide any result. These conditions occur due to low bias and high variance in the model. Decision trees are more prone to overfitting.

Underfitting: Here, the model is so simple that it is not able to identify the correct relationship in the data, and hence it does not perform well even on the test data. This can happen due to high bias and low variance. Linear regression is more prone to Underfitting.

5. Differentiate between the long and wide format data.

The following image depicts the representation of wide format and long format data:

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6. what are eigenvectors and eigenvalues.

Eigenvectors are column vectors or unit vectors whose length/magnitude is equal to 1. They are also called right vectors. Eigenvalues are coefficients that are applied on eigenvectors which give these vectors different values for length or magnitude.

A matrix can be decomposed into Eigenvectors and Eigenvalues and this process is called Eigen decomposition. These are then eventually used in machine learning methods like PCA (Principal Component Analysis) for gathering valuable insights from the given matrix.

7. What does it mean when the p-values are high and low?

A p-value is the measure of the probability of having results equal to or more than the results achieved under a specific hypothesis assuming that the null hypothesis is correct. This represents the probability that the observed difference occurred randomly by chance.

• Low p-value which means values ≤ 0.05 means that the null hypothesis can be rejected and the data is unlikely with true null.
• High p-value, i.e values ≥ 0.05 indicates the strength in favor of the null hypothesis. It means that the data is like with true null.
• p-value = 0.05 means that the hypothesis can go either way.

8. When is resampling done?

Resampling is a methodology used to sample data for improving accuracy and quantify the uncertainty of population parameters. It is done to ensure the model is good enough by training the model on different patterns of a dataset to ensure variations are handled. It is also done in the cases where models need to be validated using random subsets or when substituting labels on data points while performing tests.

9. What do you understand by Imbalanced Data?

Data is said to be highly imbalanced if it is distributed unequally across different categories. These datasets result in an error in model performance and result in inaccuracy.

10. Are there any differences between the expected value and mean value?

There are not many differences between these two, but it is to be noted that these are used in different contexts. The mean value generally refers to the probability distribution whereas the expected value is referred to in the contexts involving random variables.

11. What do you understand by Survivorship Bias?

This bias refers to the logical error while focusing on aspects that survived some process and overlooking those that did not work due to lack of prominence. This bias can lead to deriving wrong conclusions.

12. Define the terms KPI, lift, model fitting, robustness and DOE.

• KPI: KPI stands for Key Performance Indicator that measures how well the business achieves its objectives.
• Lift: This is a performance measure of the target model measured against a random choice model. Lift indicates how good the model is at prediction versus if there was no model.
• Model fitting: This indicates how well the model under consideration fits given observations.
• Robustness: This represents the system’s capability to handle differences and variances effectively.
• DOE: stands for the design of experiments, which represents the task design aiming to describe and explain information variation under hypothesized conditions to reflect variables.

13. Define confounding variables.

Confounding variables are also known as confounders. These variables are a type of extraneous variables that influence both independent and dependent variables causing spurious association and mathematical relationships between those variables that are associated but are not casually related to each other.

14. Define and explain selection bias?

The selection bias occurs in the case when the researcher has to make a decision on which participant to study. The selection bias is associated with those researches when the participant selection is not random. The selection bias is also called the selection effect. The selection bias is caused by as a result of the method of sample collection.

Four types of selection bias are explained below:

• Sampling Bias: As a result of a population that is not random at all, some members of a population have fewer chances of getting included than others, resulting in a biased sample. This causes a systematic error known as sampling bias.
• Time interval: Trials may be stopped early if we reach any extreme value but if all variables are similar invariance, the variables with the highest variance have a higher chance of achieving the extreme value.
• Data: It is when specific data is selected arbitrarily and the generally agreed criteria are not followed.
• Attrition: Attrition in this context means the loss of the participants. It is the discounting of those subjects that did not complete the trial.

15. Define bias-variance trade-off?

Let us first understand the meaning of bias and variance in detail:

Bias: It is a kind of error in a machine learning model when an ML Algorithm is oversimplified. When a model is trained, at that time it makes simplified assumptions so that it can easily understand the target function. Some algorithms that have low bias are Decision Trees, SVM, etc. On the other hand, logistic and linear regression algorithms are the ones with a high bias.

Variance: Variance is also a kind of error. It is introduced into an ML Model when an ML algorithm is made highly complex. This model also learns noise from the data set that is meant for training. It further performs badly on the test data set. This may lead to over lifting as well as high sensitivity.

When the complexity of a model is increased, a reduction in the error is seen. This is caused by the lower bias in the model. But, this does not happen always till we reach a particular point called the optimal point. After this point, if we keep on increasing the complexity of the model, it will be over lifted and will suffer from the problem of high variance. We can represent this situation with the help of a graph as shown below:

As you can see from the image above, before the optimal point, increasing the complexity of the model reduces the error (bias). However, after the optimal point, we see that the increase in the complexity of the machine learning model increases the variance.

Trade-off Of Bias And Variance: So, as we know that bias and variance, both are errors in machine learning models, it is very essential that any machine learning model has low variance as well as a low bias so that it can achieve good performance.

Let us see some examples. The K-Nearest Neighbor Algorithm is a good example of an algorithm with low bias and high variance. This trade-off can easily be reversed by increasing the k value which in turn results in increasing the number of neighbours. This, in turn, results in increasing the bias and reducing the variance.

Another example can be the algorithm of a support vector machine. This algorithm also has a high variance and obviously, a low bias and we can reverse the trade-off by increasing the value of parameter C. Thus, increasing the C parameter increases the bias and decreases the variance.

So, the trade-off is simple. If we increase the bias, the variance will decrease and vice versa.

16. Define the confusion matrix?

It is a matrix that has 2 rows and 2 columns. It has 4 outputs that a binary classifier provides to it. It is used to derive various measures like specificity, error rate, accuracy, precision, sensitivity, and recall.

The test data set should contain the correct and predicted labels. The labels depend upon the performance. For instance, the predicted labels are the same if the binary classifier performs perfectly. Also, they match the part of observed labels in real-world scenarios. The four outcomes shown above in the confusion matrix mean the following:

• True Positive: This means that the positive prediction is correct.
• False Positive: This means that the positive prediction is incorrect.
• True Negative: This means that the negative prediction is correct.
• False Negative: This means that the negative prediction is incorrect.

The formulas for calculating basic measures that comes from the confusion matrix are:

• Error rate : (FP + FN)/(P + N)
• Accuracy : (TP + TN)/(P + N)
• Sensitivity = TP/P
• Specificity = TN/N
• Precision = TP/(TP + FP)
• F-Score  = (1 + b)(PREC.REC)/(b2 PREC + REC) Here, b is mostly 0.5 or 1 or 2.

In these formulas:

FP = false positive FN = false negative TP = true positive RN = true negative

Sensitivity is the measure of the True Positive Rate. It is also called recall. Specificity is the measure of the true negative rate. Precision is the measure of a positive predicted value. F-score is the harmonic mean of precision and recall.

17. What is logistic regression? State an example where you have recently used logistic regression.

Logistic Regression is also known as the logit model. It is a technique to predict the binary outcome from a linear combination of variables (called the predictor variables). 

For example , let us say that we want to predict the outcome of elections for a particular political leader. So, we want to find out whether this leader is going to win the election or not. So, the result is binary i.e. win (1) or loss (0). However, the input is a combination of linear variables like the money spent on advertising, the past work done by the leader and the party, etc. 

18. What is Linear Regression? What are some of the major drawbacks of the linear model?

Linear regression is a technique in which the score of a variable Y is predicted using the score of a predictor variable X. Y is called the criterion variable. Some of the drawbacks of Linear Regression are as follows:

• The assumption of linearity of errors is a major drawback.
• It cannot be used for binary outcomes. We have Logistic Regression for that.
• Overfitting problems are there that can’t be solved.

19. What is a random forest? Explain it’s working.

Classification is very important in machine learning. It is very important to know to which class does an observation belongs. Hence, we have various classification algorithms in machine learning like logistic regression, support vector machine, decision trees, Naive Bayes classifier, etc. One such classification technique that is near the top of the classification hierarchy is the random forest classifier. 

So, firstly we need to understand a decision tree before we can understand the random forest classifier and its works. So, let us say that we have a string as given below:

So, we have the string with 5 ones and 4 zeroes and we want to classify the characters of this string using their features. These features are colour (red or green in this case) and whether the observation (i.e. character) is underlined or not. Now, let us say that we are only interested in red and underlined observations. So, the decision tree would look something like this:

So, we started with the colour first as we are only interested in the red observations and we separated the red and the green-coloured characters. After that, the “No” branch i.e. the branch that had all the green coloured characters was not expanded further as we want only red-underlined characters. So, we expanded the “Yes” branch and we again got a “Yes” and a “No” branch based on the fact whether the characters were underlined or not. 

So, this is how we draw a typical decision tree. However, the data in real life is not this clean but this was just to give an idea about the working of the decision trees. Let us now move to the random forest.

Random Forest

It consists of a large number of decision trees that operate as an ensemble. Basically, each tree in the forest gives a class prediction and the one with the maximum number of votes becomes the prediction of our model. For instance, in the example shown below, 4 decision trees predict 1, and 2 predict 0. Hence, prediction 1 will be considered.

The underlying principle of a random forest is that several weak learners combine to form a keen learner. The steps to build a random forest are as follows:

• Build several decision trees on the samples of data and record their predictions.
• Each time a split is considered for a tree, choose a random sample of mm predictors as the split candidates out of all the pp predictors. This happens to every tree in the random forest.
• Apply the rule of thumb i.e. at each split m = p√m = p.
• Apply the predictions to the majority rule.

20. In a time interval of 15-minutes, the probability that you may see a shooting star or a bunch of them is 0.2. What is the percentage chance of you seeing at least one star shooting from the sky if you are under it for about an hour?

Let us say that Prob is the probability that we may see a minimum of one shooting star in 15 minutes.

So, Prob = 0.2

Now, the probability that we may not see any shooting star in the time duration of 15 minutes is = 1 - Prob

1-0.2 = 0.8

The probability that we may not see any shooting star for an hour is: 

= (1-Prob)(1-Prob)(1-Prob)*(1-Prob) = 0.8 * 0.8 * 0.8 * 0.8 = (0.8)⁴   ≈ 0.40

So, the probability that we will see one shooting star in the time interval of an hour is = 1-0.4 = 0.6

So, there are approximately 60% chances that we may see a shooting star in the time span of an hour.

21. What is deep learning? What is the difference between deep learning and machine learning?

Deep learning is a paradigm of machine learning. In deep learning,  multiple layers of processing are involved in order to extract high features from the data. The neural networks are designed in such a way that they try to simulate the human brain. 

Deep learning has shown incredible performance in recent years because of the fact that it shows great analogy with the human brain.

The difference between machine learning and deep learning is that deep learning is a paradigm or a part of machine learning that is inspired by the structure and functions of the human brain called the artificial neural networks. Learn More .

22. What is a Gradient and Gradient Descent?

Gradient: Gradient is the measure of a property that how much the output has changed with respect to a little change in the input. In other words, we can say that it is a measure of change in the weights with respect to the change in error. The gradient can be mathematically represented as the slope of a function.

Gradient Descent: Gradient descent is a minimization algorithm that minimizes the Activation function. Well, it can minimize any function given to it but it is usually provided with the activation function only. 

Gradient descent, as the name suggests means descent or a decrease in something. The analogy of gradient descent is often taken as a person climbing down a hill/mountain. The following is the equation describing what gradient descent means:

So, if a person is climbing down the hill, the next position that the climber has to come to is denoted by “b” in this equation. Then, there is a minus sign because it denotes the minimization (as gradient descent is a minimization algorithm). The Gamma is called a waiting factor and the remaining term which is the Gradient term itself shows the direction of the steepest descent. 

This situation can be represented in a graph as follows:

Here, we are somewhere at the “Initial Weights” and we want to reach the Global minimum. So, this minimization algorithm will help us do that.

1. How are the time series problems different from other regression problems?

• Time series data can be thought of as an extension to linear regression which uses terms like autocorrelation, movement of averages for summarizing historical data of y-axis variables for predicting a better future.
• Forecasting and prediction is the main goal of time series problems where accurate predictions can be made but sometimes the underlying reasons might not be known.
• Having Time in the problem does not necessarily mean it becomes a time series problem. There should be a relationship between target and time for a problem to become a time series problem.
• The observations close to one another in time are expected to be similar to the ones far away which provide accountability for seasonality. For instance, today’s weather would be similar to tomorrow’s weather but not similar to weather from 4 months from today. Hence, weather prediction based on past data becomes a time series problem.

2. What are RMSE and MSE in a linear regression model?

RMSE: RMSE stands for Root Mean Square Error. In a linear regression model, RMSE is used to test the performance of the machine learning model. It is used to evaluate the data spread around the line of best fit. So, in simple words, it is used to measure the deviation of the residuals.

RMSE is calculated using the formula:

• Yi is the actual value of the output variable.
• Y(Cap) is the predicted value and,
• N is the number of data points.

MSE: Mean Squared Error is used to find how close is the line to the actual data. So, we make the difference in the distance of the data points from the line and the difference is squared. This is done for all the data points and the submission of the squared difference divided by the total number of data points gives us the Mean Squared Error (MSE).

So, if we are taking the squared difference of N data points and dividing the sum by N, what does it mean? Yes, it represents the average of the squared difference of a data point from the line i.e. the average of the squared difference between the actual and the predicted values. The formula for finding MSE is given below:

• Yi is the actual value of the output variable (the ith data point)
• Y(cap) is the predicted value and,
• N is the total number of data points.

So, RMSE is the square root of MSE .

3. What are Support Vectors in SVM (Support Vector Machine)?

In the above diagram, we can see that the thin lines mark the distance from the classifier to the closest data points (darkened data points). These are called support vectors. So, we can define the support vectors as the data points or vectors that are nearest (closest) to the hyperplane. They affect the position of the hyperplane. Since they support the hyperplane, they are known as support vectors.

4. So, you have done some projects in machine learning and data science and we see you are a bit experienced in the field. Let’s say your laptop’s RAM is only 4GB and you want to train your model on 10GB data set.

What will you do have you experienced such an issue before.

In such types of questions, we first need to ask what ML model we have to train. After that, it depends on whether we have to train a model based on Neural Networks or SVM.

The steps for Neural Networks are given below:

• The Numpy array can be used to load the entire data. It will never store the entire data, rather just create a mapping of the data.
• Now, in order to get some desired data, pass the index into the NumPy Array.
• This data can be used to pass as an input to the neural network maintaining a small batch size.

The steps for SVM are given below:

• For SVM, small data sets can be obtained. This can be done by dividing the big data set.
• The subset of the data set can be obtained as an input if using the partial fit function.
• Repeat the step of using the partial fit method for other subsets as well.

Now, you may describe the situation if you have faced such an issue in your projects or working in machine learning/ data science.

5. Explain Neural Network Fundamentals.

In the human brain, different neurons are present. These neurons combine and perform various tasks. The Neural Network in deep learning tries to imitate human brain neurons. The neural network learns the patterns from the data and uses the knowledge that it gains from various patterns to predict the output for new data, without any human assistance.

A perceptron is the simplest neural network that contains a single neuron that performs 2 functions. The first function is to perform the weighted sum of all the inputs and the second is an activation function.

There are some other neural networks that are more complicated. Such networks consist of the following three layers:

• Input Layer: The neural network has the input layer to receive the input.
• Hidden Layer: There can be multiple hidden layers between the input layer and the output layer. The initially hidden layers are used for detecting the low-level patterns whereas the further layers are responsible for combining output from previous layers to find more patterns.
• Output Layer: This layer outputs the prediction.

An example neural network image is shown below:

6. What is Generative Adversarial Network?

This approach can be understood with the famous example of the wine seller. Let us say that there is a wine seller who has his own shop. This wine seller purchases wine from the dealers who sell him the wine at a low cost so that he can sell the wine at a high cost to the customers. Now, let us say that the dealers whom he is purchasing the wine from, are selling him fake wine. They do this as the fake wine costs way less than the original wine and the fake and the real wine are indistinguishable to a normal consumer (customer in this case). The shop owner has some friends who are wine experts and he sends his wine to them every time before keeping the stock for sale in his shop. So, his friends, the wine experts, give him feedback that the wine is probably fake. Since the wine seller has been purchasing the wine for a long time from the same dealers, he wants to make sure that their feedback is right before he complains to the dealers about it. Now, let us say that the dealers also have got a tip from somewhere that the wine seller is suspicious of them.

So, in this situation, the dealers will try their best to sell the fake wine whereas the wine seller will try his best to identify the fake wine. Let us see this with the help of a diagram shown below:

From the image above, it is clear that a noise vector is entering the generator (dealer) and he generates the fake wine and the discriminator has to distinguish between the fake wine and real wine. This is a Generative Adversarial Network (GAN).

In a GAN, there are 2 main components viz. Generator and Discrminator. So, the generator is a CNN that keeps producing images and the discriminator tries to identify the real images from the fake ones. 

7. What is a computational graph?

A computational graph is also known as a “Dataflow Graph”. Everything in the famous deep learning library TensorFlow is based on the computational graph. The computational graph in Tensorflow has a network of nodes where each node operates. The nodes of this graph represent operations and the edges represent tensors.

8. What are auto-encoders?

Auto-encoders are learning networks. They transform inputs into outputs with minimum possible errors. So, basically, this means that the output that we want should be almost equal to or as close as to input as follows. 

Multiple layers are added between the input and the output layer and the layers that are in between the input and the output layer are smaller than the input layer. It received unlabelled input. This input is encoded to reconstruct the input later.

9. What are Exploding Gradients and Vanishing Gradients?

• Exploding Gradients: Let us say that you are training an RNN. Say, you saw exponentially growing error gradients that accumulate, and as a result of this, very large updates are made to the neural network model weights. These exponentially growing error gradients that update the neural network weights to a great extent are called Exploding Gradients .
• Vanishing Gradients: Let us say again, that you are training an RNN. Say, the slope became too small. This problem of the slope becoming too small is called Vanishing Gradient . It causes a major increase in the training time and causes poor performance and extremely low accuracy.

10. What is the p-value and what does it indicate in the Null Hypothesis?

P-value is a number that ranges from 0 to 1. In a hypothesis test in statistics, the p-value helps in telling us how strong the results are. The claim that is kept for experiment or trial is called Null Hypothesis.

• A low p-value i.e. p-value less than or equal to 0.05 indicates the strength of the results against the Null Hypothesis which in turn means that the Null Hypothesis can be rejected. 
• A high p-value i.e. p-value greater than 0.05 indicates the strength of the results in favour of the Null Hypothesis i.e. for the Null Hypothesis which in turn means that the Null Hypothesis can be accepted.

11. Since you have experience in the deep learning field, can you tell us why TensorFlow is the most preferred library in deep learning?

Tensorflow is a very famous library in deep learning. The reason is pretty simple actually. It provides C++ as well as Python APIs which makes it very easier to work on. Also, TensorFlow has a fast compilation speed as compared to Keras and Torch (other famous deep learning libraries). Apart from that, Tenserflow supports both GPU and CPU computing devices. Hence, it is a major success and a very popular library for deep learning.

12. Suppose there is a dataset having variables with missing values of more than 30%, how will you deal with such a dataset?

Depending on the size of the dataset, we follow the below ways:

• In case the datasets are small, the missing values are substituted with the mean or average of the remaining data. In pandas, this can be done by using mean = df.mean() where df represents the pandas dataframe representing the dataset and mean() calculates the mean of the data. To substitute the missing values with the calculated mean, we can use df.fillna(mean) .
• For larger datasets, the rows with missing values can be removed and the remaining data can be used for data prediction.

13. What is Cross-Validation?

Cross-Validation is a Statistical technique used for improving a model’s performance. Here, the model will be trained and tested with rotation using different samples of the training dataset to ensure that the model performs well for unknown data. The training data will be split into various groups and the model is run and validated against these groups in rotation.

The most commonly used techniques are:

• K- Fold method
• Leave p-out method
• Leave-one-out method
• Holdout method

14. What are the differences between correlation and covariance?

Although these two terms are used for establishing a relationship and dependency between any two random variables, the following are the differences between them:

• Correlation: This technique is used to measure and estimate the quantitative relationship between two variables and is measured in terms of how strong are the variables related.
• Covariance: It represents the extent to which the variables change together in a cycle. This explains the systematic relationship between pair of variables where changes in one affect changes in another variable.

Mathematically, consider 2 random variables, X and Y where the means are represented as  μ X {"detectHand":false}  and  μ Y {"detectHand":false}  respectively and standard deviations are represented by  σ X {"detectHand":false}  and  σ Y {"detectHand":false}  respectively and E represents the expected value operator, then:

• covarianceXY = E[(X- μ X {"detectHand":false} ),(Y- μ Y {"detectHand":false} )]
• correlationXY = E[(X- μ X {"detectHand":false} ),(Y- μ Y {"detectHand":false} )]/( σ X {"detectHand":false} σ Y {"detectHand":false} ) so that

Based on the above formula, we can deduce that the correlation is dimensionless whereas covariance is represented in units that are obtained from the multiplication of units of two variables.

The following image graphically shows the difference between correlation and covariance:

15. How do you approach solving any data analytics based project?

Generally, we follow the below steps:

• The first step is to thoroughly understand the business requirement/problem
• Next, explore the given data and analyze it carefully. If you find any data missing, get the requirements clarified from the business.
• Data cleanup and preparation step is to be performed next which is then used for modelling. Here, the missing values are found and the variables are transformed.
• Run your model against the data, build meaningful visualization and analyze the results to get meaningful insights.
• Release the model implementation, and track the results and performance over a specified period to analyze the usefulness.
• Perform cross-validation of the model.

Check out the list of data analytics projects .

16. How regularly must we update an algorithm in the field of machine learning?

We do not want to update and make changes to an algorithm on a regular basis as an algorithm is a well-defined step procedure to solve any problem and if the steps keep on updating, it cannot be said well defined anymore. Also, this brings in a lot of problems to the systems already implementing the algorithm as it becomes difficult to bring in continuous and regular changes. So, we should update an algorithm only in any of the following cases:

• If you want the model to evolve as data streams through infrastructure, it is fair to make changes to an algorithm and update it accordingly.
• If the underlying data source is changing, it almost becomes necessary to update the algorithm accordingly.
• If there is a case of non-stationarity, we may update the algorithm.
• One of the most important reasons for updating any algorithm is its underperformance and lack of efficiency. So, if an algorithm lacks efficiency or underperforms it should be either replaced by some better algorithm or it must be updated.

17. Why do we need selection bias?

Selection Bias happens in cases where there is no randomization specifically achieved while picking a part of the dataset for analysis. This bias tells that the sample analyzed does not represent the whole population meant to be analyzed.

• For example, in the below image, we can see that the sample that we selected does not entirely represent the whole population that we have. This helps us to question whether we have selected the right data for analysis or not.

18. Why is data cleaning crucial? How do you clean the data?

While running an algorithm on any data, to gather proper insights, it is very much necessary to have correct and clean data that contains only relevant information. Dirty data most often results in poor or incorrect insights and predictions which can have damaging effects.

For example, while launching any big campaign to market a product, if our data analysis tells us to target a product that in reality has no demand and if the campaign is launched, it is bound to fail. This results in a loss of the company’s revenue. This is where the importance of having proper and clean data comes into the picture.

• Data Cleaning of the data coming from different sources helps in data transformation and results in the data where the data scientists can work on.
• Properly cleaned data increases the accuracy of the model and provides very good predictions.
• If the dataset is very large, then it becomes cumbersome to run data on it. The data cleanup step takes a lot of time (around 80% of the time) if the data is huge. It cannot be incorporated with running the model. Hence, cleaning data before running the model, results in increased speed and efficiency of the model.
• Data cleaning helps to identify and fix any structural issues in the data. It also helps in removing any duplicates and helps to maintain the consistency of the data.

The following diagram represents the advantages of data cleaning:

19. What are the available feature selection methods for selecting the right variables for building efficient predictive models?

While using a dataset in data science or machine learning algorithms, it so happens that not all the variables are necessary and useful to build a model. Smarter feature selection methods are required to avoid redundant models to increase the efficiency of our model. Following are the three main methods in feature selection:

• These methods pick up only the intrinsic properties of features that are measured via univariate statistics and not cross-validated performance. They are straightforward and are generally faster and require less computational resources when compared to wrapper methods.
• There are various filter methods such as the Chi-Square test, Fisher’s Score method, Correlation Coefficient, Variance Threshold, Mean Absolute Difference (MAD) method, Dispersion Ratios, etc.

• These methods need some sort of method to search greedily on all possible feature subsets, access their quality by learning and evaluating a classifier with the feature.
• The selection technique is built upon the machine learning algorithm on which the given dataset needs to fit.
• Forward Selection: Here, one feature is tested at a time and new features are added until a good fit is obtained.
• Backward Selection: Here, all the features are tested and the non-fitting ones are eliminated one by one to see while checking which works better.
• Recursive Feature Elimination: The features are recursively checked and evaluated how well they perform.
• These methods are generally computationally intensive and require high-end resources for analysis. But these methods usually lead to better predictive models having higher accuracy than filter methods.

• Embedded methods constitute the advantages of both filter and wrapper methods by including feature interactions while maintaining reasonable computational costs.
• These methods are iterative as they take each model iteration and carefully extract features contributing to most of the training in that iteration.
• Examples of embedded methods: LASSO Regularization (L1), Random Forest Importance.

20. During analysis, how do you treat the missing values?

To identify the extent of missing values, we first have to identify the variables with the missing values. Let us say a pattern is identified. The analyst should now concentrate on them as it could lead to interesting and meaningful insights. However, if there are no patterns identified, we can substitute the missing values with the median or mean values or we can simply ignore the missing values. 

If the variable is categorical, the common strategies for handling missing values include:

• Assigning a New Category: You can assign a new category, such as "Unknown" or "Other," to represent the missing values.
• Mode imputation: You can replace missing values with the mode, which represents the most frequent category in the variable.
• Using a Separate Category: If the missing values carry significant information, you can create a separate category to indicate missing values.

It's important to select an appropriate strategy based on the nature of the data and the potential impact on subsequent analysis or modelling.

If 80% of the values are missing for a particular variable, then we would drop the variable instead of treating the missing values.

21. Will treating categorical variables as continuous variables result in a better predictive model?

Yes! A categorical variable is a variable that can be assigned to two or more categories with no definite category ordering. Ordinal variables are similar to categorical variables with proper and clear ordering defines. So, if the variable is ordinal, then treating the categorical value as a continuous variable will result in better predictive models.

22. How will you treat missing values during data analysis?

The impact of missing values can be known after identifying what type of variables have missing values.

• If the data analyst finds any pattern in these missing values, then there are chances of finding meaningful insights.
• In case of patterns are not found, then these missing values can either be ignored or can be replaced with default values such as mean, minimum, maximum, or median values.
• Assigning a new category: You can assign a new category, such as "Unknown" or "Other," to represent the missing values.
• Using a separate category : If the missing values carry significant information, you can create a separate category to indicate the missing values. It's important to select an appropriate strategy based on the nature of the data and the potential impact on subsequent analysis or modelling.
• If 80% of values are missing, then it depends on the analyst to either replace them with default values or drop the variables.

23. What does the ROC Curve represent and how to create it?

ROC (Receiver Operating Characteristic) curve is a graphical representation of the contrast between false-positive rates and true positive rates at different thresholds. The curve is used as a proxy for a trade-off between sensitivity and specificity.

The ROC curve is created by plotting values of true positive rates (TPR or sensitivity) against false-positive rates (FPR or (1-specificity)) TPR represents the proportion of observations correctly predicted as positive out of overall positive observations. The FPR represents the proportion of observations incorrectly predicted out of overall negative observations. Consider the example of medical testing, the TPR represents the rate at which people are correctly tested positive for a particular disease.

24. What are the differences between univariate, bivariate and multivariate analysis?

Statistical analyses are classified based on the number of variables processed at a given time.

25. What is the difference between the Test set and validation set?

The test set is used to test or evaluate the performance of the trained model. It evaluates the predictive power of the model. The validation set is part of the training set that is used to select parameters for avoiding model overfitting.

26. What do you understand by a kernel trick?

Kernel functions are generalized dot product functions used for the computing dot product of vectors xx and yy in high dimensional feature space. Kernal trick method is used for solving a non-linear problem by using a linear classifier by transforming linearly inseparable data into separable ones in higher dimensions.

27. Differentiate between box plot and histogram.

Box plots and histograms are both visualizations used for showing data distributions for efficient communication of information. Histograms are the bar chart representation of information that represents the frequency of numerical variable values that are useful in estimating probability distribution, variations and outliers. Boxplots are used for communicating different aspects of data distribution where the shape of the distribution is not seen but still the insights can be gathered. These are useful for comparing multiple charts at the same time as they take less space when compared to histograms.

28. How will you balance/correct imbalanced data?

There are different techniques to correct/balance imbalanced data. It can be done by increasing the sample numbers for minority classes. The number of samples can be decreased for those classes with extremely high data points. Following are some approaches followed to balance data:

• Specificity/Precision: Indicates the number of selected instances that are relevant.
• Sensitivity: Indicates the number of relevant instances that are selected.
• F1 score: It represents the harmonic mean of precision and sensitivity.
• MCC (Matthews correlation coefficient): It represents the correlation coefficient between observed and predicted binary classifications.
• AUC (Area Under the Curve): This represents a relation between the true positive rates and false-positive rates.

For example, consider the below graph that illustrates training data:

Here, if we measure the accuracy of the model in terms of getting "0"s, then the accuracy of the model would be very high -> 99.9%, but the model does not guarantee any valuable information. In such cases, we can apply different evaluation metrics as stated above.

• Under-sampling This balances the data by reducing the size of the abundant class and is used when the data quantity is sufficient. By performing this, a new dataset that is balanced can be retrieved and this can be used for further modeling.
• Over-sampling This is used when data quantity is not sufficient. This method balances the dataset by trying to increase the samples size. Instead of getting rid of extra samples, new samples are generated and introduced by employing the methods of repetition, bootstrapping, etc.
• Perform K-fold cross-validation correctly: Cross-Validation needs to be applied properly while using over-sampling. The cross-validation should be done before over-sampling because if it is done later, then it would be like overfitting the model to get a specific result. To avoid this, resampling of data is done repeatedly with different ratios. 

29. What is better - random forest or multiple decision trees?

Random forest is better than multiple decision trees as random forests are much more robust, accurate, and lesser prone to overfitting as it is an ensemble method that ensures multiple weak decision trees learn strongly.

30. Consider a case where you know the probability of finding at least one shooting star in a 15-minute interval is 30%. Evaluate the probability of finding at least one shooting star in a one-hour duration?

So the probability is 0.8628 = 86.28%

31. Toss the selected coin 10 times from a jar of 1000 coins. Out of 1000 coins, 999 coins are fair and 1 coin is double-headed, assume that you see 10 heads. Estimate the probability of getting a head in the next coin toss.

We know that there are two types of coins - fair and double-headed. Hence, there are two possible ways of choosing a coin. The first is to choose a fair coin and the second is to choose a coin having 2 heads.

P(selecting fair coin) = 999/1000 = 0.999 P(selecting double headed coin) = 1/1000 = 0.001

Using Bayes rule,

So, the answer is 0.7531 or 75.3%.

32. What are some examples when false positive has proven important than false negative?

Before citing instances, let us understand what are false positives and false negatives.

• False Positives are those cases that were wrongly identified as an event even if they were not. They are called Type I errors.
• False Negatives are those cases that were wrongly identified as non-events despite being an event. They are called Type II errors.

Some examples where false positives were important than false negatives are:

• In the medical field: Consider that a lab report has predicted cancer to a patient even if he did not have cancer. This is an example of a false positive error. It is dangerous to start chemotherapy for that patient as he doesn’t have cancer as starting chemotherapy would lead to damage of healthy cells and might even actually lead to cancer.
• In the e-commerce field: Suppose a company decides to start a campaign where they give $100 gift vouchers for purchasing $10000 worth of items without any minimum purchase conditions. They assume it would result in at least 20% profit for items sold above $10000. What if the vouchers are given to the customers who haven’t purchased anything but have been mistakenly marked as those who purchased $10000 worth of products. This is the case of false-positive error.

33. Give one example where both false positives and false negatives are important equally?

In Banking fields: Lending loans are the main sources of income to the banks. But if the repayment rate isn’t good, then there is a risk of huge losses instead of any profits. So giving out loans to customers is a gamble as banks can’t risk losing good customers but at the same time, they can’t afford to acquire bad customers. This case is a classic example of equal importance in false positive and false negative scenarios.

34. Is it good to do dimensionality reduction before fitting a Support Vector Model?

If the features number is greater than observations then doing dimensionality reduction improves the SVM (Support Vector Model).

35. What are various assumptions used in linear regression? What would happen if they are violated?

Linear regression is done under the following assumptions:

• The sample data used for modeling represents the entire population.
• There exists a linear relationship between the X-axis variable and the mean of the Y variable.
• The residual variance is the same for any X values. This is called homoscedasticity
• The observations are independent of one another.
• Y is distributed normally for any value of X.

Extreme violations of the above assumptions lead to redundant results. Smaller violations of these result in greater variance or bias of the estimates.

36. How is feature selection performed using the regularization method?

The method of regularization entails the addition of penalties to different parameters in the machine learning model for reducing the freedom of the model to avoid the issue of overfitting. There are various regularization methods available such as linear model regularization, Lasso/L1 regularization, etc. The linear model regularization applies penalty over coefficients that multiplies the predictors. The Lasso/L1 regularization has the feature of shrinking some coefficients to zero, thereby making it eligible to be removed from the model.

37. How do you identify if a coin is biased?

To identify this, we perform a hypothesis test as below: According to the null hypothesis, the coin is unbiased if the probability of head flipping is 50%. According to the alternative hypothesis, the coin is biased and the probability is not equal to 500. Perform the below steps:

• Flip coin 500 times
• Calculate p-value.
• p-value > alpha: Then null hypothesis holds good and the coin is unbiased.
• p-value < alpha: Then the null hypothesis is rejected and the coin is biased.

38. What is the importance of dimensionality reduction?

The process of dimensionality reduction constitutes reducing the number of features in a dataset to avoid overfitting and reduce the variance. There are mostly 4 advantages of this process:

• This reduces the storage space and time for model execution.
• Removes the issue of multi-collinearity thereby improving the parameter interpretation of the ML model.
• Makes it easier for visualizing data when the dimensions are reduced.
• Avoids the curse of increased dimensionality.

39. How is the grid search parameter different from the random search tuning strategy?

Tuning strategies are used to find the right set of hyperparameters. Hyperparameters are those properties that are fixed and model-specific before the model is tested or trained on the dataset. Both the grid search and random search tuning strategies are optimization techniques to find efficient hyperparameters.

• Here, every combination of a preset list of hyperparameters is tried out and evaluated.
• The search pattern is similar to searching in a grid where the values are in a matrix and a search is performed. Each parameter set is tried out and their accuracy is tracked. after every combination is tried out, the model with the highest accuracy is chosen as the best one.
• The main drawback here is that, if the number of hyperparameters is increased, the technique suffers. The number of evaluations can increase exponentially with each increase in the hyperparameter. This is called the problem of dimensionality in a grid search.

• In this technique, random combinations of hyperparameters set are tried and evaluated for finding the best solution. For optimizing the search, the function is tested at random configurations in parameter space as shown in the image below.
• In this method, there are increased chances of finding optimal parameters because the pattern followed is random. There are chances that the model is trained on optimized parameters without the need for aliasing.
• This search works the best when there is a lower number of dimensions as it takes less time to find the right set.

Conclusion:

Data Science is a very vast field and comprises many topics like Data Mining, Data Analysis, Data Visualization, Machine Learning, Deep Learning, and most importantly it is laid on the foundation of mathematical concepts like Linear Algebra and Statistical analysis. Since there are a lot of pre-requisites for becoming a good professional Data Scientist, the perks and benefits are very big. Data Scientist has become the most sought job role these days. 

Looking for a comprehensive course on Data Science: Check out Scaler’s Data Science Course .

Useful Resources:

• Best Data Science Courses
• Python Data Science Interview Questions
• Google Data Scientist Salary
• Spotify Data Scientist Salary
• Data Scientist Salary
• Data Science Resume
• Data Analyst: Career Guide
• Tableau Interview
• Additional Technical Interview Questions

1. How do I prepare for a data science interview?

Some of the preparation tips for data science interviews are as follows:

• Resume Building: Firstly, prepare your resume well. It is preferable if the resume is only a 1-page resume, especially for a fresher. You should give great thought to the format of the resume as it matters a lot. The data science interviews can be based more on the topics like linear and logistic regression, SVM, root cause analysis, random forest, etc. So, prepare well for the data science-specific questions like those discussed in this article, make sure your resume has a mention of such important topics and you have a good knowledge of them. Also, please make sure that your resume contains some Data Science-based Projects as well. It is always better to have a group project or internship experience in the field that you are interested to go for. However, personal projects will also have a good impact on the resume. So, your resume should contain at least 2-3 data science-based projects that show your skill and knowledge level in data science. Please do not write any such skill in your resume that you do not possess. If you are just familiar with some technology and have not studied it at an advanced level, you can mention a beginner tag for those skills.
• Prepare Well: Apart from the specific questions on data science, questions on Core subjects like Database Management systems (DBMS), Operating Systems (OS), Computer Networks(CN), and Object-Oriented Programming (OOPS) can be asked from the freshers especially. So, prepare well for that as well.
• Data structures and Algorithms are the basic building blocks of programming. So, you should be well versed with that as well.
• Research the Company: This is the tip that most people miss and it is very important. If you are going for an interview with any company, read about the company before and especially in the case of data science, learn which libraries the company uses, what kind of models are they building, and so on. This gives you an edge over most other people.

2. Are data science interviews hard?

An honest reply will be “YES”. This is because of the fact that this field is newly emerging and will keep on emerging forever. In almost every interview, you have to answer many tough and challenging questions with full confidence and your concepts should be strong to satisfy the interviewer. However, with great practice, anything can be achieved. So, follow the tips discussed above and keep practising and learning. You will definitely succeed.

3. What are the top 3 technical skills of a data scientist?

The top 3 skills of a data scientist are:

• Mathematics: Data science requires a lot of mathematics and a good data scientist is strong in it. It is not possible to become a good data scientist if you are weak in mathematics.
• Machine Learning and Deep Learning : A data scientist should be very skilled in Artificial Intelligence technologies like deep learning and machine learning. Some good projects and a lot of hands-on practice will help in achieving excellence in that field.
• Programming: This is an obvious yet the most important skill. If a person is good at programming it does mean that he/she can solve complex problems as that is just a problem-solving skill. Programming is the ability to write clean and industry-understandable code. This is the skill that most freshers slack because of the lack of exposure to industry-level code. This also improves with practice and experience. 

4. Is data science a good career?

Yes, data science is one of the most futuristic and great career fields. Today and tomorrow or even years later, this field is just going to expand and never end. The reason is simple. Data can be compared to gold today as it is the key to selling everything in the world. Data scientists know how to play with this data to generate some tremendous outputs that are not even imaginable today making it a great career.

5. Are coding questions asked in data science interviews?

Yes, coding questions are asked in data science interviews. One more important thing to note here is that the data scientists are very good problem solvers as they are indulged in a lot of strict mathematics-based activities. Hence, the interviewer expects the data science interview candidates to know data structures and algorithms and at least come up with the solutions to most of the problems.

6. Is python and SQL enough for data science?

Yes. Python and SQL are sufficient for the data science roles. However, knowing the R programming Language can have also have a better impact. If you know these 3 languages, you have got the edge over most of the competitors. However, Python and SQL are enough for data science interviews.

7. What are Data Science tools?

There are various data science tools available in the market nowadays. Various tools can be of great importance. Tensorflow is one of the most famous data science tools. Some of the other famous tools are BigML, SAS (Statistical Analysis System), Knime, Scikit, Pytorch, etc.

Which among the below is NOT a necessary condition for weakly stationary time series data?

Overfitting more likely occurs when there is a huge data amount to train. True or False?

Given the information that the demand is 100 in October 2020, 150 in November 2020, 350 during December 2020 and 400 during January 2021. Calculate a 3-month simple moving average for February 2021.

Which of the below method depicts hierarchical data in nested format?

Which among the following defines the analysis of data objects not complying with general data behaviour?

What does a linear equation having 3 variables represent?

What would be the formula representation of this problem in terms of x and y variables: “The price of 2 pens and 1 pencil as 10 units”?

Which among the below is true regarding hypothesis testing?

What are the model parameters that are used to build ML models using iterative methods under model-based learning methods?

What skills are necessary for a Data Scientist?

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Top Data Science Interview Questions and Answers (2024)

Data science is experiencing rapid growth, transforming how organizations interpret data and drive decisions. Consequently, there’s a rising demand for data scientists who can extract insights and steer business strategies. This heightened demand has created intense competition for data science roles.

In this article, we’ll delve into the most commonly asked Data Science Interview Questions , which are beneficial for both freshers and experienced data scientists.

Certifications serve as valuable additions to your resume and can significantly boost your chances of success in interviews. If you’re a data scientist gearing up for an interview, showcasing your skills with certification can make a strong impression on your potential employer.

Consider enrolling in online courses by Whizlabs such as Microsoft Azure Exam DP-100 Certification to become a Data Scientist.

Let’s dive in!

Top 25 Data Science Interview Questions and Answers 

Here we have listed out some important Data Science Interview Questions and Answers for freshers and experienced:

1. What is data science?

Data science is an interdisciplinary field that uses scientific methods, tools, and techniques to extract meaningful insights from large datasets. It combines elements from statistics, mathematics, computer science, and domain expertise to analyze data and solve real-world problems

2. What are the key activities in data science?

Data scientists typically follow these steps:

• Data Collection and Cleaning: Gather data from various sources, clean it to ensure accuracy, and prepare it for analysis.
• Data Analysis: Utilizing statistical and machine learning techniques to analyze the data, identify patterns, and build models.
• Visualization and Communication: Effectively presenting the findings through visualizations and communicating them to stakeholders for informed decision-making.

3. What are recommender systems?

 Recommender systems are software tools that suggest items (products, services, content) to users based on their preferences, historical behavior, or similarities with other users. They aim to help users navigate the overwhelming amount of information and make informed choices.

4. What is dimensionality reduction?

Dimensionality reduction is a technique used in machine learning and data analysis to decrease the number of features (dimensions) in a dataset. This is often done without losing significant information, making the data easier to handle and analyze.

5. Define collaborative filtering & its types.

Collaborative filtering is a technique used in recommender systems to predict a user’s preference for an item based on the preferences of other similar users.

• Leverages User Similarity: It analyzes past user behavior and preferences to identify users with similar tastes to the target user.
• Recommends Based on Similarities: Based on these similar users’ preferences for items, the system recommends items that the target user might also enjoy.
• Data-Driven Approach: It relies heavily on the data of user interactions with items, typically represented in a user-item matrix.

Types of Collaborative Filtering:

• User-based Filtering: This approach focuses on finding users with similar tastes to the target user and recommends items that similar users have liked.
• Item-based Filtering: This approach focuses on finding items similar to those the user has already liked and recommends other similar items.

Examples of Collaborative Filtering:

• E-commerce platforms: Recommend products based on your browsing history and past purchases, often utilizing user-based filtering.
• Streaming services: Suggest movies, shows, or music based on what other users with similar viewing habits have watched or listened to.
• Social media platforms: Recommend friends, groups, or content based on your connections and the interests of those connections.

6. Explain star schema.

A star schema is a specific type of data warehouse schema designed for efficient querying and analysis of large datasets. It resembles a star shape, with one central fact table surrounded by multiple dimension tables.

Star schemas are ideal for:

• Data warehouses and data marts focused on analytical queries and reporting.
• Analyzing large datasets efficiently and providing fast response times.
• Scenarios where data complexity is moderate and relationships are relatively simple.

7. What is RMSE?

RMSE stands for Root Mean Square Error . It is a statistical metric used to measure the difference between predicted values and actual values in a dataset.

RMSE calculates the average magnitude of the errors between predictions and actual values. Here’s the process:

• Calculate the residuals: For each data point, calculate the difference between the predicted value and the actual value. This difference is called the residual.
• Square the residuals: Square each residual to emphasize larger errors.
• Calculate the mean: Average the squared residuals.
• Take the square root: Take the square root of the mean squared residuals. This final value is the RMSE.

5. Mention some of the data science tools.

Some popular data science tools include:

Programming Languages

• Python: Widely popular with libraries like NumPy, Pandas, Scikit-learn, and TensorFlow for data analysis, manipulation, and machine learning.
• R: Another popular language with powerful statistical capabilities and visualization libraries like ggplot2.

Data Manipulation and Analysis

• Pandas: Python library for efficient data manipulation, cleaning, and analysis.
• SQL: Structured Query Language for interacting with relational databases.

Machine Learning

• Scikit-learn: Python library with a comprehensive set of machine learning algorithms for classification, regression, clustering, and more.
• TensorFlow & PyTorch: Deep learning frameworks for building and training complex neural networks.

Data Visualization

• Matplotlib & Seaborn (Python): Libraries for creating various static and interactive visualizations.
• ggplot2 (R): Popular library for creating elegant and informative data visualizations.
• Apache Spark: Open-source framework for distributed computing and large-scale data processing.
• Hadoop: Distributed file system for storing and managing massive datasets.

9. What is Logistic Regression?

 Logistic Regression is a statistical method and machine learning algorithm used for classification tasks. It predicts the probability of an event occurring based on one or more independent variables. Unlike linear regression, which predicts continuous values, logistic regression deals with binary outcomes (e.g., yes/no, pass/fail, spam/not spam).

10. When is Logistic Regression used?

Here are some common applications:

• Fraud Detection: Identifying fraudulent transactions based on customer data.
• Medical Diagnosis: Predicting the likelihood of a disease based on patient symptoms.
• Customer Churn Prediction: Identifying customers likely to leave a service.
• Email Spam Filtering: Classifying emails as spam or not spam.

11. What is the ROC curve?

 ROC stands for Receiver Operating Characteristic curve . It is a visual tool used to evaluate the performance of a binary classifier. It helps assess how well the classifier can distinguish between positive and negative cases across various classification thresholds. It is commonly used in various scenarios like machine learning for Evaluating the performance of classification models & medical diagnosis for Assessing the accuracy of diagnostic tests.Also, it can be used in Fraud detection to analyze the effectiveness of fraud detection algorithms

12. What are the differences between supervised and unsupervised learning?

13. What is a Confusion Matrix?

A confusion matrix is a powerful tool in machine learning, particularly for evaluating the performance of classification models. It provides a clear and concise visualization of how well a model performs in distinguishing between different classes.

14. Compare Data Science vs. Data Analytics.

Check out our detailed guide on how to become a data Scientist .

15. What is the process for constructing a random forest model

A random forest model is a machine learning algorithm that operates by constructing multiple decision trees during training and outputting the mode of the classes (classification) or the mean prediction (regression) of the individual trees. It is a type of ensemble learning method that combines the predictions of multiple individual models (in this case, decision trees) to improve overall prediction accuracy and robustness. Random forest models are known for their ability to handle complex datasets with high dimensionality and noisy features, as well as their resistance to overfitting.

Here comes the steps, you can build a random forest model capable of making accurate predictions across a wide range of classification and regression tasks.

• Start by randomly selecting ‘k’ features from a pool of ‘m’ features, where ‘k’ is significantly smaller than ‘m’.
• Among the chosen ‘k’ features, compute the optimal split point to generate node D.
• Divide the node into daughter nodes based on the most favorable split.
• Iterate through steps two and three until reaching the finalized leaf nodes.
• Construct the forest by repeating steps one to four ‘n’ times to produce ‘n’ trees.

16. What are Eigenvectors and Eigenvalues?

Eigenvalues are special scalar values associated with a square matrix. When a matrix is multiplied by an eigenvector, the resulting vector remains in the same direction but gets scaled by the eigenvalue.

Eigenvectors are non-zero vectors that when multiplied by a specific matrix, simply get scaled by a constant value (the eigenvalue). They represent specific directions along which the matrix stretches or shrinks vectors.

17. What is the p-value?

The p-value is a statistical measure used in hypothesis testing to assess the strength of evidence against the null hypothesis. It represents the probability of obtaining a test statistic at least as extreme as the observed one, assuming the null hypothesis is true.

Commonly used thresholds for rejecting the null hypothesis are:

• p-value < 0.05: Statistically significant result, strong evidence against the null hypothesis.
• p-value > 0.05: Fail to reject the null hypothesis, insufficient evidence to conclude against it.
• p-value at cutoff 0.05: This is considered to be marginal, meaning it could go either way

18. Define confounding variables.

Confounding variables are extraneous factors that can influence both the independent variable (exposure) and the dependent variable (outcome) in a study, potentially distorting the observed relationship between them. These variables are often correlated with the independent variable of interest and can distort the true relationship between the independent variable and the dependent variable. Identifying and controlling for confounding variables is essential in research to ensure accuracy and reliability.

19. What is MSE in a linear regression model?

In linear regression, Mean Squared Error (MSE) is a commonly used metric to evaluate how well the model fits the data. It measures the average squared difference between the predicted values from the model and the actual observed values.

What it measures:

• MSE quantifies the average squared error between the predicted and actual values.
• A lower MSE indicates a better fit, meaning the model’s predictions are closer to the actual observations.
• A higher MSE indicates a poorer fit, with larger discrepancies between predicted and actual values.

Formula: MSE = (1/n) * Σ(yi – ŷi)^2

• n is the number of data points
• yi is the actual value for the ith data point
• ŷi is the predicted value for the ith data point by the model

20. What Is a Decision Tree?

  A decision tree is a machine learning algorithm used for both classification and regression tasks. It represents a tree-like structure where each internal node (split point) poses a question based on a feature of the data, and each branch represents a possible answer or outcome. The leaves of the tree represent the final predictions.

Key Advantages for Decision Tree:

• Interpretability: Decision trees are easily interpretable, allowing you to understand the logic behind the model’s predictions by following the decision rules along each branch.
• Flexibility: They can handle both numerical and categorical features without extensive data preprocessing.
• Robustness to outliers: Decision trees are relatively insensitive to outliers in the data.

21. What is Overfitting and Underfitting?

Overfitting

• Occurs when a model becomes too complex and memorizes the training data, including the noise and irrelevant details, to the extent that it fails to generalize well to unseen data.
• The model performs very well on the training data but poorly on new, unseen data.
• High variance and low bias are characteristics of overfitting.

Underfitting

• Occurs when a model is too simple and fails to capture the underlying pattern in the training data itself.
• The model performs poorly on both the training and unseen data.
• High bias and low variance are characteristics of underfitting.

22. Differentiate between long-format data and wide-format data.

23. What is bias?

Bias refers to the systematic error or deviation in the results of a study or experiment that is caused by flaws in the design, execution, or analysis of the study. Bias can lead to inaccurate or misleading conclusions by favoring certain outcomes or groups over others. It can arise from various sources, including selection bias, measurement bias, and confounding variables. Identifying and minimizing bias is essential in research to ensure the validity and reliability of the findings.

24. Mention some popular libraries used in Data Science.

Here are some of the most popular libraries used in Data Science, primarily within the Python ecosystem:

Fundamental Libraries

• NumPy: Provides high-performance multidimensional arrays and mathematical operations, forming the foundation for other libraries.
• Pandas: Offers powerful data structures like DataFrames for efficient data manipulation, cleaning, and analysis.
• Matplotlib: A versatile library for creating various static, animated, and interactive visualizations.
• Seaborn: Built on top of Matplotlib, it provides high-level statistical data visualizations with a focus on aesthetics and clarity.
• Scikit-learn: A comprehensive library for various machine learning algorithms, including classification, regression, clustering, and dimensionality reduction.
• TensorFlow/PyTorch: Leading libraries for deep learning, enabling the development and training of complex neural networks.

25. Why R is important in the Data Science Domain?

R is a programming language and software environment primarily used for statistical computing and graphics. It provides a wide range of statistical and graphical techniques, making it popular among statisticians and data analysts for data analysis and visualization.

R is important in the data science domain for several reasons:

• Statistical Analysis: R offers a comprehensive set of built-in statistical functions and libraries, making it a powerful tool for statistical analysis. It supports various statistical techniques such as linear and nonlinear modeling, time-series analysis, and hypothesis testing.
• Data Visualization: R provides extensive capabilities for data visualization, allowing users to create a wide range of plots and graphics to explore and communicate data insights effectively. Packages like ggplot2 offer high-quality and customizable visualizations.
• Machine Learning: R has a vast ecosystem of packages for machine learning, enabling data scientists to build and deploy predictive models for classification, regression, clustering, and more. Popular machine learning libraries in R include caret, randomForest, and xgboost.
• Community and Resources: R has a large and active community of users, developers, and contributors who continually develop new packages, share tutorials, and provide support. This community-driven development model ensures that R remains up-to-date with the latest advancements in data science.
• Integration with Other Tools: R seamlessly integrates with other programming languages and tools, such as Python, SQL databases, and big data frameworks like Apache Spark. This interoperability allows data scientists to leverage the strengths of different tools within their workflow and integrate R code with existing systems.

Discover some top-paying data science jobs and advance your career to the next level now!

I hope these Data Science Interview Questions can be helpful in your upcoming interviews.

We don’t just limit ourselves to interview questions, we also have DP-100 exam practice tests to ensure thorough preparation for this Certification.

By combining certification with thorough preparation using resources like this comprehensive list of top Data Science interview questions and answers, you’ll be well-equipped to excel in your next job opportunity.

Best of luck on your journey!

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How to Solve Data Science Business Case Interview Questions

The Ultimate Guide to Preparing Business Case Interview Questions as a Data Scientist

Approaching Data Science Business Case Interview Questions

Similar to product sense interview questions , business case interview questions in data science are asked to understand your thought process behind your solution. Even if your solution provides an accurate answer to the question, if you do not give proper steps to your answer, this will not look good on the interviewee's part.

Business case interview questions are asked for:

• Ability to diagnose and solve real business case problems
• Understand how familiar the interviewee is with the economy/business surrounding the company’s products
• Feasibility of the interviewee’s solution
• Effective communication of the solution in a structured manner

(All these qualities in your answer are part of the data science job, so try to make sure you keep these in mind)

To effectively provide the solution to the business case interview questions presented, it is important to understand which category the question is categorized under.

• Applied Data (most common)

Theory Testing

Question Type

• Brief explanation
• Specific examples and generalized examples
• Companies look for: (in your answer)

Applied Data

These questions ask you to solve a specific business problem by leveraging company data or from external sources.

Examples of these questions:

• DemystData asked “How would a financial institution determine if an applicant makes more or less than $50k/year?”
• Facebook asked “How many high schools that people have listed on their Facebook profiles are real? How do we find out, and deploy at scale, a way of finding invalid schools?”

Companies look for:

• How well the interviewee can identify and define relevant data
• How well the interviewee understands the product in question
• How well the interviewee understands the business/economy surrounding the product

These questions ask to predict the number of products sold/exist. These are the questions that seem random and have no relevance to the company.

• Google asked “How many cans of blue paint were sold in the United States last year?”
• Ebay asked “What is the total length of all the roads in San Francisco?”
• How well the interviewee can identify the target market

These questions are asked to prove/disprove a theory that usually involves a change in a product/feature within the company.

Example of this question:

• If a PM says that they want to double the number of ads in the News Feed, how would you figure out if this is a good idea or not?
• How well the interviewee understands the impact of the changed product/feature
• How well the interviewee understands the relationship between the product and the target audience
• Choose relevant metrics to track whether the change is a success or not

Structuring Solution

After classifying the business case interview question posed, you should start to structure your solution. Do not answer the question immediately. Understand that even if you provide a good answer, interviewers want to understand how you arrived at your answer.

The interviewer looks for:

• A solution with a systematic approach
• Why a certain solution was chosen over another
• Cover the key areas of the question
• A feasible solution

While each data science business case interview question has a different approach to the solution, part of your solution has the same methodology. This part of the solution can be treated as the background research to create an exceptional solution.

General Framework

The first thing the interviewee should do is understand the question. While this may seem straightforward, the assumptions you made about the question may not be the same assumption the interviewer is looking for. Think when you are a data scientist and building the background information for a prospective client, you need to understand their needs. This is hard to capture through just one question. Take some time to think about why the company is asking this question.

Business case questions are often asked to increase: Market Share, User Engagement, or Revenue. Understand which category the company is trying to increase and depending on the response, your solution may or may not change! Even if you don’t think it will change your solution, ask just to show this is on your mind since it is an important aspect when developing an actual feature/product.

An important part of understanding the question is to understand the question’s key terms. To make sure you cover all the key terms, point out and ask if you have a proper assumption about the key terms in order from start to finish.

Example: “How many Big Macs does McDonald's sell each year in the US?”

• “Big Macs” → Assumption that Big Macs include both big macs sold by itself and part of a combo meal. Have there been any other variations of big macs sold such as through promotional/limited-time purposes?
• “Year” → Does year indicate from January 1st to December 31st (Gregorian Calendar) or the fiscal year of October 1st till September 30th?
• “US” → Does US mean just the 50 states and D.C or does it include the minor outlying islands and territories of the US?

After understanding the question thoroughly, you need to provide a solution based on the type of question asked (Applied Data, Sizing, Theory Testing).

• Specific Framework

Applied data are an extremely diverse set of questions, so it is hard to have a specific framework. This question truly tests how well you understand the company’s product and how to utilize relevant data.

When taking time to construct your solution, think about what type of data does the company have access to? Think about what internal data the company collects. When it comes to “applying data” questions, internal data is a great source, since the company usually has already collected relevant data. For example Uber asked “How do you estimate the impact that Uber has on driving conditions and congestion?” Uber has collected data related to what Uber cars are currently being used by customers. Uber also would have data on the traffic at any given time range in their supported cities. These are internal data that can be utilized to construct your solution. Do not forget about collecting data from external sources. Data can also be collected and utilized from trusted external sources but maybe a more riskier answer if you choose bad data sources.

If the data that is required to be collected are metrics, remember to mention both success metrics and guardrail metrics. Success metrics are metrics that you measure to quantify the success of the specific product. Guardrail metrics are metrics that are not supposed to be negatively affected in pursuit of changing the success metric. For example, Uber asked “What metrics would you use to track whether Uber's strategy of using paid advertising to acquire customers works?” A possible success metric could be inorganic growth of users and a guardrail metric could be the number of rides taken. While the purpose of paid advertising is probably to increase the number of users (These assumptions are the type of questions you as the interviewee would want to ask the interviewer!) Uber does not want the number of rides people take to reduce.

Once you have understood which data you require for your solution, mention how each of the data collected is relevant to your solution. This shows you understand where the chosen data will be applied and how this supports your solution.

Remember applied data questions typically involve users and the user experience. Try to think like a typical user and edge case users to construct your solution. For example Facebook asked “We at Facebook would like to develop a way to estimate the month and day of people's birthdays, regardless of whether people give us that information directly. What methods would you propose, and data would you use, to help with that task?” Imagine you are a Facebook user. You would receive birthday wishes on Facebook through direct messages or tagged Facebook posts. A possible solution would be to check the number of users who tag you in a post mentioning keyterms such as ‘birthday’ or ‘bday’. These posts tend to be more during your actual birthday, so Facebook could estimate your birthday based on this.

Sizing questions may seem extremely random and diverse, but it is easier to break down how to approach the solution. Sizing questions can also be called guesstimation, since you are trying to make an estimated guess. Sizing questions are asked to see how well you can identify the target audience for a specific product. For example, Google asked “How many cans of blue paint were sold in the United States last year?” Google wants to see how many target audiences you can identify and furthermore see if you can approximate how many blue paint cans each of these consumers will purchase.

A key point to remember is that you do not have to try and give a hyper-accurate number of how many blue paint cans a certain target audience will buy. The interviewer probably does not know the exact answer either, they just want to hear your thought process behind your answer.

Since sizing questions are trying to identify the target audience, these are some common splitting factors among potential users (not limited to):

• Rural/Urban
• Willingness

Another factor to consider is the consumption type of the product/service. There are 3 consumption types: Individual, Household, Structural.

• Individual refers to the personal consumption of products/services, such as toothbrushes, water bottles, or tshirts.
• Household refers to consumption by the entire household, such as cars, TVs, and refrigerators.
• Structural refers to the consumption by multiple people from different households. Examples include airplanes and restaurants.

Initially state a couple of target audiences (using the filters to help), then mention target audiences you will focus on who represent a majority of the market.

Generally, it is a good idea to involve at least 3 filters of who would use the product/service. Always explain the relevance between the filter and the target audience. For example, TikTok would be better suited to be advertised to a younger audience between ages of 13-18 rather than elderly people who are 65+.

As mentioned before, interviewers do not look for precise numbers, so try to use round numbers as much as possible. Let’s build on the TikTok example, and assume 20% of the US population is between the ages of 13-18. The population of the US is around 333,548,370. 20% of 333,543,000 is hard to calculate on the spot during an interview, especially if you are nervous. If you round the population to 300 million, it is much easier to calculate 20% of 300 million. 

There is more than one method people can use to estimate the size, but after analyzing various methods one method stuck out especially for data science business case interviews. The method is creating a layout of types of consumers that will use the product and fill out an approximate number of people who will use the product.

To explain this method better, let’s use a Facebook question “How many Big Macs does McDonald's sell each year in the US?”.

Let’s first take a rounded number for the US population, which we shall assume around 300 million.

Main target audiences for big macs would typically be older than the target audience for kids meals and younger than older people. The main target audiences from this would be college students, families, and people trying to buy a cheap meal.

With respect to college students, let’s assume 10% of the population are college students. 10% of 300M = 30M. Let’s assume that an average college student buys McDonalds once a week. Not everyone will buy a big mac, but since it is a popular item on the menu, let’s assume that a third of people who go to McDonald's buy a big mac. ⅓ of 30 million is 10 million. That means under these assumptions college students buy 10 million big macs per week. This is equivalent to 520 million big macs per year.

With respect to families, let’s assume that two-thirds of the population is in a family that lives under the same household. Two-thirds of 300M is 200M. When a family buys food, they tend to buy for the entire household. Let’s assume the average family buys food from a restaurant once a week and maybe from McDonald's once a month. Let’s assume that of a typical 4 person family, 1 person chooses to eat a big mac. 200M/4 = 50M. Under the family target audience, 50 million people buy a big mac once a month. This is equivalent to 600M big macs per year.

With respect to people who are trying to buy a cheap meal, let’s assume 10% of the population are trying to buy a quick cheap meal once a week. 300M * 10% = 30M. McDonald's is a popular fast food chain with branches nearly everywhere in the US. Let’s assume that of the 30 million people, 50% choose McDonald's. 30M * 50% = 15M. This results in 15 million people buying from McDonald's once a week. Let’s assume that a third of these people buy big macs, resulting in 5M big macs sold per week to the target audience who want a quick and cheap meal. This is equivalent to 260M big macs sold a year.

If you were to add these values together, 520M + 600M + 260M = 1380M big macs sold a year in the US.

At the end after mentioning your final answer, you could also provide more relevant information that could affect the value. For example, McDonald's has a 2 for $5 or $6 sale where the customers can purchase 2 big macs for $5/$6. This would increase sales of big macs. This shows you have knowledge about the product.

Sometimes during interviews, you are given an online board where you can draw and note down your thoughts. This may be a design you can choose to help you understand and explain your thoughts better!

Things to remember

• Remember you do not have to mention all the target audiences. Mention the important ones which take up most of the market and if you want, mention edge cases.
• When you get a final answer, do a sanity check. Check if your final answer is overvalued or undervalued and adjust values accordingly.
• Write down especially when using the layout of types of consumers, so you have a reference of what is connected to what
• Remember you can use any filter that sounds sensible, but do remember to mention how this relates to the actual solution.
• A key point to remember is to not use personal bias. Remember your social circle does not represent the entire population. Do not assume everyone thinks the same as you. Put yourself in other people’s shoes and see how they would view this problem.

Remember theory testing mainly tests to see how you understand the product and the target audience. There are 3 steps to follow to help answer these questions.

• Identify users affected
• Pros vs Cons of the change
• Data to prove/disprove the theory
• Metrics that will change

To help explaining these steps, we’ll use a question asked by Facebook “A PM wants to double the number of ads in Newsfeed, how would you figure out if this is a good idea or not?”

Users Affected

Imaging testing a hypothesis, first you need to do some background research. Understand what types of users will be affected by this change. List out the user groups affected. Some changes in a product will affect all users while some will only mainly affect a portion of the users. Are the users affected the target audience or other users?

With the Facebook example, general users of Facebook who scroll through their News Feed will be affected and influencers will also be affected since there will be more ads instead of their posts.

Pros vs Cons

Every change in product will include positives and negatives. It could either affect the user base or the company’s resources. List 2 pros and 2 cons of how the product will change if the change is implemented. Theory testing usually involves testing a theory about changing a product/service. A pros and cons list would help understand the change better and what data/metric to collect. The reason to identify the users affected is to incorporate on how the user group will be affected in the pros and cons list, since at the end the users are the people who will use the product.

Using the Facebook example:

• Increase in revenue for Facebook
• Businesses may choose to use Facebook as a solution for their marketing techniques due to an increase in ads
• Users might decrease time on app due to increase in ads
• Influencers might not post as much since their posts do not receive the recognition they used to get

Data to support theory

Sometimes a similar change could have been implemented before in the same company or another company. If you know if a similar change has been made, mention how the change has affected the product and what metrics have changed.

If there is no prior data, mention what metrics you think would change if the change is implemented. Remember to mention how you predict the metrics would change and the logic behind why you think this.

Using the Facebook example, we could see how users felt when Facebook first implemented ads into the newsfeed. This would give direct information on how it affected Facebook users. Metrics that could be tracked include Daily Active Users and number of influencers posts. DAU will help to show if users stop using the app after the change. If there is a decrease in the number of influencers posts, their followers will decrease in usage of the app.

At this point, you would have explained the initial part of the general framework and the specific question framework. Now you have to explain the edge cases and summarize your solution.

With any given business case interview question, every solution you give will not cover a certain edge case. The main reason for this could be due to time constraints during the interview. It is good to identify edge cases that your solution has not covered. Even if you can not explain how to solve the edge case, it is better to identify a possible edge case than to not identify it at all. A lot of interviewees will identify the majority of the solution, but to identify edge cases is what would help separate you from others. There is a reason to mention the edge cases after your specific question solution. This helps structure your solution so the interviewers will not get confused.

An example for an edge case can be seen with a Google interview question “How many cans of blue paint were sold in the United States last year?” Supposed 3 potential main target audiences were identified: residential buildings, corporate buildings, and car manufacturers. An edge case could be preschool and elementary schools which would buy paint for students. You as the interviewee could mention some rough estimates on how you would calculate how much blue paint might have been bought.

After providing your detailed solution, you should summarize your solution.

Remember to include:

• Assumptions made about the question
• Data/metrics collected and relevance to the solution
• Overview of specific approach to the solution using the data/metrics collected

The summary of your solution is an important part of your interview. It is important to reiterate the key points in your solution, but also it shows how you will communicate to investors and clients on how to solve these business problems.

Overview of steps to follow

• Clarify the goal of the question - Why are they asking this question - Understand how the product is related to the company’s goals - Trying to increase revenue, market share, or user engagement
• Understand the question - Breakdown keywords
• Summarize answer

Communicating your Solution

Remember that your final answer is not the deciding factor for a successful interview or not. You must be able to communicate your thought process in a detailed manner. Interviewers want to see how your thought process works and see if you covered all the important points when explaining your solution. An important part of the interview is how well you can communicate your solution.

There should be a clear understanding of the question and thought process between you and the interviewer. The input of the interviewer may be the most important part to include in your solution. If the interviewer gives instructions or asks questions that deviate from the framework, follow it. Never strictly follow the framework. You should refine your solution and the steps to your answer with respect to the interviewer’s comments. Remember the interviewer influences the decision of hiring you, so the better you can incorporate the interviewer’s comments the better your chances are.

Take time before responding

• As the interviewee, you are given some time to think about how to approach your solution. If you respond immediately, you may realize your solution has a major flaw, thus causing you to backtrack to correct the problem. To prevent this and miscommunication between you and the interviewer, take some time before responding. Interviewees usually take up to 30 seconds before responding.
• Don’t worry if it does take longer than 30 seconds to come up with a solution! At this point you could state your assumptions and how you are thinking on how to start your solution. This prevents any awkward silence and does not appear you are taking too much time to provide a solution.

Agree on goals/assumptions

• A key part in communicating with the interviewer is to have an understanding of how to go about your solution. The phrasing behind business case questions are ambiguous on purpose, so the interviewee can identify the phrases that need to be clarified. Similar to an actual discussion with a client, you need to agree upon the goals/assumptions before crafting a solution.

Mention technical term

• Since you are applying to a technical role, you should be using technical terms to separate yourself from others. It shows you have an understanding of where certain technical concepts should be applied.
• If you do not understand the technical concept entirely
• If the technical concept is an overkill or if there is a simpler solution to the question. Just because the term machine learning sells everywhere does not mean you should use it everywhere.
• There is no relevance between the technical concept and your solution
• Business case interview questions do not always require complex technical concepts, so do not worry if your solution seems straightforward. As long as your solution covers the important parts of the question, you are good.

New metrics/data

• Whenever introducing a new metric or collecting data, mention what the metric/data is.
• How to derive/collect the metric/data.
• How does the new metric/data help craft your solution?

Symantec asked “Suppose you have a coffee store, what do you do to increase the number of customers?” Suppose you are trying to increase the number of customers by posting an ad for a holiday sale on one of your most popular products. Mention you should collect a dataset on how many people buy every product. This could be collected through the transactions and what products were sold. Once you have collected the data, you can find which items are sold the most. Depending on the profit margins on the top selling products, you could temporarily reduce the price to increase the number of customers!

Getting stuck

• While providing your solution, if you ever get stuck in the middle of your solution, tell your interviewer you are stuck! Tell them how you are thinking about your solution and what you are trying to achieve in the next step. Tell the interviewer certain steps you thought about taking in your solution, but did not due to certain reasons (explain these reasons). Try to see if the interviewer gives any clues.

Mistake in your solution

• If you think your solution is not plausible or will definitely lead to an error, mention why your solution will not work. Take some more time to see if you can change your solution or if you have to backtrack your solution to where you feel most confident. Remember do not worry if you encounter a problem that your solution can not handle. Similar to the work environment, it is better to identify a problem instead of continuing on top of a broken base. When mentioning your new solution, remember to explicitly state how your new solution will avoid the issue.

Always communicate your solution out loud! Talk to your interviewer through your solution, so they can also understand your thought process. Remember to connect each step with the previous step and the overall goal. After a couple of steps, do a sanity check to make sure the interviewer understands your solution.

How to prepare for Business Case questions before the interview

Business case interview questions are another challenging part of the data science interviews . These questions are quite difficult to predict due to its diversity and seemingly random questions.

In respect to the 3 categories of Business Case questions: Applied Data, Sizing, and Theory Testing, there is a different way to prepare for each.

When preparing for Applied Data questions, first you must understand the type of business the company does. Is the company involved with a lot of B2B or B2C? Sometimes, with large corporations, such as Google, the company will mention what division you are applying for. Research what types of business this division does. Next you should understand the type of product the company produces, or even better the product the division you have applied for produces. Try to understand the business model around the product and what types of data they would use to help improve the product. If there is no public information on this, try to make your own assumptions! Look at the product from different angles with respect to the target audience and what data you would collect to understand how to improve the product. 

Sizing questions are unfortunately more random and sometimes the company will ask a question that may seem unrelated to the company’s business model. Example: Google asked “How many cans of blue paint were sold in the United States last year?” For these types of questions, it requires constant practice with other sizing questions.

Preparing for theory testing is sort of the next step to the applied data question, since you have found out how to apply the data to improve the product. How to test if this would actually work or not? Think about what may occur if this solution is applied. To what extent the users will be affected and how certain metrics will increase, decrease, or remain constant. Think about what type of problems your potential employer faces! These problems could be solved using a data science approach, so think how you would solve the problem!

Try to research the product's competitors and understand if other competing companies have changed their product similarly to how the company you are applying for is trying to mention how it worked out for the competing company! Similar to sizing questions, theory testing requires practice with multiple questions!

Also, check out our post " data science interview questions " to find questions from other categories as well.

Helpful Links for Preparation

• Facebook released a data science interview prep video, which gives the answer to one of the questions in this article!

https://vimeo.com/385283671/ec3432147b → 2:25 - 12:15

• Another helpful video to answer a business case question asked by Spotify ​
• Mock Product Manager Interview (LinkedIn PM): Improve Spotify's Social Features ​
• Uber Product Manager Mock Interview: Estimate Drivers in SF ​
• Google Product Manager Estimation Interview: Paint Market ​
• Mock Product Manager Interview (Google PM): Estimate Pixel Phone Storage ​
• https://www.tryexponent.com/questions/812/pm-instagram-stories-increase-expiration-time-stories ​
• https://www.tryexponent.com/questions/439/facebook-signup-upload-profile-picture-remove-requirement ​
• https://www.tryexponent.com/questions/814/facebook-split-news-feed-two-what-metrics-validate-decision ​

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Before we move into the article let’s try to understand what topics we are going to cover in this article and how you will benefit from this article. First thing we will look into what hackathons are . How can you look for Hackathons ? Why participate in a hackathon ? How can hackathons help in my career growth and, a lot more things

Table of Content

What is a Hackathon?

Types of hackathons, where can i find hackathons, why to participate in hackathons, what do i need to know to participate in hackathons, advantages of participating in hackathons, how to ace hackathons, how to get ready for hackathons, case study: national level hackathon winning experience.

The word hackathon is a portmanteau of the words hacker , which means clever programmer. It is a social coding event where people can participate and solve some set of given problems based on particular themes . Most of the time these themes are predefined and the problem statements are disclosed at the time of the event. There people can participate individually or with a team depending upon the rules of the event and find out the unique solution to the given problem statements . Then again depending on the event’s requirements , theme , and judges the most unique and efficient solution may be declared as the winner.

Online hackathons

Online hackathons or virtual hackathons have the potential to be one of the most efficient methods especially for finding and recruiting talented individuals from other countries. Programmers from around the world compete in virtual hackathons just to see who can top the competition scoreboard. This allows recruiters to get a sense of the potential new hire’s abilities. This ‘hack’ is especially helpful for smaller teams who don’t have the funds or other means to sell their brand worldwide or set up recruitment departments in other countries.

Offline Hackathon

An offline hackathon is a collaborative event where participants gather in a physical location to work on coding and programming projects. This personalized format allows for face-to-face interaction, networking, and real-time problem-solving. Offline hackathons often last several days and can be organized by companies, universities, or community groups

This process is pretty easy; there are so many ways to find out hackathons , but the main thing we need to decide is whether we want to participate in them in an offline mode or in an online mode. Hackathons are available in both modes nowadays. It’s always better to participate in the hackathons in an offline mode. We can visit websites to figure out where these hackathons. Hackathons are also organized by universities and colleges where we can participate. One of the very famous hackathons we can look for is the Smart India Hackathon (SIH) organized by the government of India, with a grand prize of 1 Lac amount. There are many others you can find on the internet

Aspects of Hackathons

There are a lots of benifits for participating in hackathons

• Participating in hackathons will help us to get a chance to meet the community of your field.
• It will help you to learn and develop new technologies and thus there will be a lot of learning benefits .
• You will learn how to work under pressure and time constraints .
• It will force you to come out of your comfort zone that is your chosen field and you have to work in other fields as well.
• Definitely, if you win a hackathon apart from the prize it will be a huge mark in your career as well as your resume .
• Free goodies , you will get free goodies depending upon the event.
• Even if you didn’t perform well and unfortunately didn’t win, you will get the certificate of participation which is also a good thing.
• Winning a national-level hackathon showcases your skills to whoever looking into your resume and has a great impact .
• You will get to meet people from different fields and will learn a lot

You can find good number of hackathons on unstop , Devfolio and even on GeeksForGeeks (Online hackathons)

It is important to have a complete overview of the event you are trying to participate into. Based on the theme of the particular event you need to have the right skill. If the event or domain you are participating is all about software then having the knowledge of web development and application development is the best choice.

If you are participating with your team its better to decide each and everyone’s role in the team, like for presenter , front end developer , backend developer , team lead etc…

• Step 1: Identify the program/event and the theme you are willing to participate.
• Step 2: Create your team member according to the team size constraint for that particular event
• Step 3: Identify your team mates roles according to the theme
• Step 4: Choose your problem statement and start working according to the roles
• Networking: Hackathons are excellent locations to network, whether you are an entrepreneur trying to expand your team or a job seeker looking to broaden your professional connections. You can find tech-savvy people who are brimming with enthusiasm just about anywhere.
• Teamwork: The vast majority of educational establishments are unable to provide their pupils with conditions in which they may learn to collaborate on real-world tasks. Students are allowed to work together constructively during an event known as hackathon.
• Prizes and funding: The majority of hackathons do not award prizes with a significant monetary value to prevent luring participants who might not be appropriate. However, despite this, the awards are still significant because they can be used as a means of enhancing one’s reputation.
• Exciting and enjoyable activities: There are people out there that are addicted to hackathons and attend events like these because they want to get their dose of adrenaline. There is something exhilarating about beginning with nothing and building something from the ground up. In addition, the welcoming atmosphere of hackathons, which typically include hundreds of participants, is really exciting.

There is no straight away answer to this question but here we can look into some tips that will definitely help you to perform in the best way.

1. Be organized and prepared

Always take time and book some workshops with the team before the main competition. Try to predict what your needs will be and plan carefully. We spent a lot of time mastering R on-site rather than focusing on the actual implementation. Additionally, try to get everyone aligned with what you are trying to achieve and try out some brainstorming sessions to get the most out of people. There are different techniques out there for this, so please feel free to exercise.

2. Choose a team leader

Be it Machine Learning or any other topic, having a subject matter expert on the team gets you a long way ahead. Make sure the team leader knows the team and the project you are trying to implement and can delegate tasks to the appropriate person.

3. Assemble a strong and varied team

Having a mix of skills in the team and making it fit together is a game changer. Don’t try to do everything yourself and delegate to the team member who has the knowledge to bring that task to a successful end, as time is not on your side. Our team was composed of a Business Analyst, 2 BI experts, and one data scientist who completed each other wonderfully.

4. Choose a real-life problem with an impactful and original solution

This can be a great source of motivation and can weigh a lot in the jury’s decision. The problems tackled at the hackathon were very diverse, ranging from classical insurance fraud detection solutions to more extraordinary solutions, such as bird song classification based on wave files. We chose two real-life challenges based on the experiences of one of our clients in the insurance industry, which were related to predicting the number of patients on a certain drug cluster and determining their loyalty.

5. Take time to do pair programming

Get yourself out of that chair, stay behind another team member, and try to tackle a problem together. It gets you way ahead in implementing something correct and fast from the very start.

6. Get some rest –  before, during, and after the hackathon

Your mind is your greatest asset – if it does not function at an optimum level, you will get the feeling that everyone else is far ahead and you cannot make progress, which can discourage you. Giving yourself short pauses to rest is much better than continuously working and pumping caffeine and energizers.

7. Get your data straight

If you have a data problem, as we did for the ML hackathon, then you definitely need to spend time modeling the data well before the competition starts. Otherwise, the pressure of not seeing good results due to time spent identifying trends will get your spirits down.

1. Find the Right Team Members

The first thing that is unquestionable is to look for potential teammates. Certain hackathons call for applications to be submitted as a team and others grant teams application preference. This can save time for the hackathon organizers who are responsible for grouping participants. And in general, teams that have already been created have higher working performance and overall synergy.

2. Hype Them Up!

Having the technical skills necessary to develop a product is only half of the battle when it comes to winning a hackathon. If you’re not tech savvy but still want to participate in a hackathon, then your excitement and energy will be the gasoline that keeps the team going for the full 24 to 48 hours. In addition, the presentation that you give at the very end is a significant factor in determining your final score. If you can’t get your crew enthused about your proposal, then you can’t expect judges to get thrilled about it, either. In conclusion, the best advice we can give is to get amped up!

3. Presentation is Crucial

Your group has the potential to create the most sophisticated program that has ever been developed at a hackathon. It is possible that there are no bugs, it works well, and is virtually perfect; yet, if your team is unable to sell the idea effectively, none of this matters.

In the actual world, marketing is responsible for approximately 50 percent of the overall success. Your product will not sell if you are unable to promote it in a manner that is understandable to the people who will ultimately be using it. The same thinking should be used for hackathons. For this reason, in addition to concentrating on putting together your amazing projects, you should put your attention toward developing a compelling presentation.

4. Know the Rules and Judges

Once you visit the website for a hackathon, you would then typically find several resources designed to assist developers in getting ready for the hackathon. These resources may include tracks, prizes, and ideas, in addition to the two most crucial components of research, which are the rules and the judging criteria.

Before you do anything further, make sure you have a crystal clear knowledge of the types of projects the organizer of the hackathon is hoping to see, those who will be judging them, and what they’ll be looking for. Only then should you move on to the next step.

5. Brainstorm your Ideas

The question statements for the hackathon may be released in advance of the hack day by the organizers of the hackathon. The reason that they do this is to give participants more time to research the various contest subjects. Sometimes, the subjects are genuinely the issues for the companies and that is why they sponsor hackathons to assist them in identifying solutions to their problems.

6. Stay Confident

At some point in life, every individual will hear the expression, “Confidence is the key” (or some variation of this statement). And, this is correct too! Having confidence in yourself helps drive you to tackle tasks and gives you the ability to do so, which is especially helpful during hackathons.

7. Imposter Syndrome

It is extremely common in the area of technology and you will likely experience it at some point in your career. Just keep in mind that you’re participating in the hackathon for a specific cause and that regardless of where you are in the process, there is something of significance that you can offer to it!

Hackathon : HACKSRM 5.O Organised By: SRM AP University. Duration: 24 Hours. Theme : Open to all

Our Project

Our project for HACKSRM 5.0 was a comprehensive mental health application aimed at providing support and resources to individuals in need. Developed using Dart and Flutter, the application offered a wide range of features to address various aspects of mental well-being. Utilizing Firebase for databases, we ensured seamless connectivity and real-time interaction between users and counselors.

Key Features of our project included

• 24/7 Counseling Services: Users could book counseling sessions online and engage in live audio and video calls with trained professionals.
• AI-enabled Mental Health Chatbot: Developed using Python and APIs, the chatbot provided immediate support and guidance to users, enhancing accessibility to mental health resources.
• Book Recommendations: The application recommended relevant books to users based on their preferences and needs, fostering a holistic approach to mental well-being.
• Machine Learning-based Daily Activity Suggestions: Leveraging machine learning algorithms, our application provided personalized daily activity suggestions to promote mental wellness.
• Website for Location-based Alerts: Developed using the MERN stack, the website served as a valuable tool for users, offering reminders to parents when their children ventured into unfamiliar locations and providing a database of nearby doctor addresses.

Evaluation Rounds

The evaluation process for HACKSRM 5.0 comprised three rigorous rounds:

• UI/UX Evaluation: In the initial round, our project underwent meticulous evaluation of its user interface and user experience components. Judges assessed the design, layout, and accessibility of our application.
• Progress Review: In the second round, we showcased the progress of our project. We discussed the datasets powering our machine learning models, the efficiency of our algorithms, and the overall development process.
• Functional Testing and Feedback: The final round involved comprehensive testing of our application’s functionality. Judges examined its usability, performance, and market potential. We received valuable feedback and engaged in discussions about market segmentation and future enhancements.

Final Results

After intense competition and thorough evaluation, our project emerged victorious in HACKSRM 5.0. Winning the hackathon was a testament to our team’s innovation, dedication, and commitment to addressing critical societal issues. We are proud to have developed a solution that not only showcases technical prowess but also makes a meaningful impact on mental health awareness and support.

Tech Stack Used:

• Dart and Flutter for application development
• Firebase for database management
• Python for AI-enabled chatbot development
• MERN stack for website development

Also Read Smart India Hackathon Experience CODEX-24 Hackathon Experience CodaTrix Hackathon Experience

Hackathons offer invaluable opportunities for collaboration, innovation, and skill development in the tech community. Participants engage in social coding events, tackling predefined problems to showcase their talents, solve real-world issues, and network with like-minded individuals. With their dynamic nature and focus on creativity, hackathons remain essential for fostering innovation and driving technological advancement.

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