phd in data science topics

37 Research Topics In Data Science To Stay On Top Of

• February 22, 2024

As a data scientist, staying on top of the latest research in your field is essential.

The data science landscape changes rapidly, and new techniques and tools are constantly being developed.

To keep up with the competition, you need to be aware of the latest trends and topics in data science research.

In this article, we will provide an overview of 37 hot research topics in data science.

We will discuss each topic in detail, including its significance and potential applications.

These topics could be an idea for a thesis or simply topics you can research independently.

Stay tuned – this is one blog post you don’t want to miss!

37 Research Topics in Data Science

1.) predictive modeling.

Predictive modeling is a significant portion of data science and a topic you must be aware of.

Simply put, it is the process of using historical data to build models that can predict future outcomes.

Predictive modeling has many applications, from marketing and sales to financial forecasting and risk management.

As businesses increasingly rely on data to make decisions, predictive modeling is becoming more and more important.

While it can be complex, predictive modeling is a powerful tool that gives businesses a competitive advantage.

2.) Big Data Analytics

These days, it seems like everyone is talking about big data.

And with good reason – organizations of all sizes are sitting on mountains of data, and they’re increasingly turning to data scientists to help them make sense of it all.

But what exactly is big data? And what does it mean for data science?

Simply put, big data is a term used to describe datasets that are too large and complex for traditional data processing techniques.

Big data typically refers to datasets of a few terabytes or more.

But size isn’t the only defining characteristic – big data is also characterized by its high Velocity (the speed at which data is generated), Variety (the different types of data), and Volume (the amount of the information).

Given the enormity of big data, it’s not surprising that organizations are struggling to make sense of it all.

That’s where data science comes in.

Data scientists use various methods to wrangle big data, including distributed computing and other decentralized technologies.

With the help of data science, organizations are beginning to unlock the hidden value in their big data.

By harnessing the power of big data analytics, they can improve their decision-making, better understand their customers, and develop new products and services.

3.) Auto Machine Learning

Auto machine learning is a research topic in data science concerned with developing algorithms that can automatically learn from data without intervention.

This area of research is vital because it allows data scientists to automate the process of writing code for every dataset.

This allows us to focus on other tasks, such as model selection and validation.

Auto machine learning algorithms can learn from data in a hands-off way for the data scientist – while still providing incredible insights.

This makes them a valuable tool for data scientists who either don’t have the skills to do their own analysis or are struggling.

4.) Text Mining

Text mining is a research topic in data science that deals with text data extraction.

This area of research is important because it allows us to get as much information as possible from the vast amount of text data available today.

Text mining techniques can extract information from text data, such as keywords, sentiments, and relationships.

This information can be used for various purposes, such as model building and predictive analytics.

5.) Natural Language Processing

Natural language processing is a data science research topic that analyzes human language data.

This area of research is important because it allows us to understand and make sense of the vast amount of text data available today.

Natural language processing techniques can build predictive and interactive models from any language data.

Natural Language processing is pretty broad, and recent advances like GPT-3 have pushed this topic to the forefront.

6.) Recommender Systems

Recommender systems are an exciting topic in data science because they allow us to make better products, services, and content recommendations.

Businesses can better understand their customers and their needs by using recommender systems.

This, in turn, allows them to develop better products and services that meet the needs of their customers.

Recommender systems are also used to recommend content to users.

This can be done on an individual level or at a group level.

Think about Netflix, for example, always knowing what you want to watch!

Recommender systems are a valuable tool for businesses and users alike.

7.) Deep Learning

Deep learning is a research topic in data science that deals with artificial neural networks.

These networks are composed of multiple layers, and each layer is formed from various nodes.

Deep learning networks can learn from data similarly to how humans learn, irrespective of the data distribution.

This makes them a valuable tool for data scientists looking to build models that can learn from data independently.

The deep learning network has become very popular in recent years because of its ability to achieve state-of-the-art results on various tasks.

There seems to be a new SOTA deep learning algorithm research paper on  https://arxiv.org/  every single day!

8.) Reinforcement Learning

Reinforcement learning is a research topic in data science that deals with algorithms that can learn on multiple levels from interactions with their environment.

This area of research is essential because it allows us to develop algorithms that can learn non-greedy approaches to decision-making, allowing businesses and companies to win in the long term compared to the short.

9.) Data Visualization

Data visualization is an excellent research topic in data science because it allows us to see our data in a way that is easy to understand.

Data visualization techniques can be used to create charts, graphs, and other visual representations of data.

This allows us to see the patterns and trends hidden in our data.

Data visualization is also used to communicate results to others.

This allows us to share our findings with others in a way that is easy to understand.

There are many ways to contribute to and learn about data visualization.

Some ways include attending conferences, reading papers, and contributing to open-source projects.

10.) Predictive Maintenance

Predictive maintenance is a hot topic in data science because it allows us to prevent failures before they happen.

This is done using data analytics to predict when a failure will occur.

This allows us to take corrective action before the failure actually happens.

While this sounds simple, avoiding false positives while keeping recall is challenging and an area wide open for advancement.

11.) Financial Analysis

Financial analysis is an older topic that has been around for a while but is still a great field where contributions can be felt.

Current researchers are focused on analyzing macroeconomic data to make better financial decisions.

This is done by analyzing the data to identify trends and patterns.

Financial analysts can use this information to make informed decisions about where to invest their money.

Financial analysis is also used to predict future economic trends.

This allows businesses and individuals to prepare for potential financial hardships and enable companies to be cash-heavy during good economic conditions.

Overall, financial analysis is a valuable tool for anyone looking to make better financial decisions.

12.) Image Recognition

Image recognition is one of the hottest topics in data science because it allows us to identify objects in images.

This is done using artificial intelligence algorithms that can learn from data and understand what objects you’re looking for.

This allows us to build models that can accurately recognize objects in images and video.

This is a valuable tool for businesses and individuals who want to be able to identify objects in images.

Think about security, identification, routing, traffic, etc.

Image Recognition has gained a ton of momentum recently – for a good reason.

13.) Fraud Detection

Fraud detection is a great topic in data science because it allows us to identify fraudulent activity before it happens.

This is done by analyzing data to look for patterns and trends that may be associated with the fraud.

Once our machine learning model recognizes some of these patterns in real time, it immediately detects fraud.

This allows us to take corrective action before the fraud actually happens.

Fraud detection is a valuable tool for anyone who wants to protect themselves from potential fraudulent activity.

14.) Web Scraping

Web scraping is a controversial topic in data science because it allows us to collect data from the web, which is usually data you do not own.

This is done by extracting data from websites using scraping tools that are usually custom-programmed.

This allows us to collect data that would otherwise be inaccessible.

For obvious reasons, web scraping is a unique tool – giving you data your competitors would have no chance of getting.

I think there is an excellent opportunity to create new and innovative ways to make scraping accessible for everyone, not just those who understand Selenium and Beautiful Soup.

15.) Social Media Analysis

Social media analysis is not new; many people have already created exciting and innovative algorithms to study this.

However, it is still a great data science research topic because it allows us to understand how people interact on social media.

This is done by analyzing data from social media platforms to look for insights, bots, and recent societal trends.

Once we understand these practices, we can use this information to improve our marketing efforts.

For example, if we know that a particular demographic prefers a specific type of content, we can create more content that appeals to them.

Social media analysis is also used to understand how people interact with brands on social media.

This allows businesses to understand better what their customers want and need.

Overall, social media analysis is valuable for anyone who wants to improve their marketing efforts or understand how customers interact with brands.

16.) GPU Computing

GPU computing is a fun new research topic in data science because it allows us to process data much faster than traditional CPUs .

Due to how GPUs are made, they’re incredibly proficient at intense matrix operations, outperforming traditional CPUs by very high margins.

While the computation is fast, the coding is still tricky.

There is an excellent research opportunity to bring these innovations to non-traditional modules, allowing data science to take advantage of GPU computing outside of deep learning.

17.) Quantum Computing

Quantum computing is a new research topic in data science and physics because it allows us to process data much faster than traditional computers.

It also opens the door to new types of data.

There are just some problems that can’t be solved utilizing outside of the classical computer.

For example, if you wanted to understand how a single atom moved around, a classical computer couldn’t handle this problem.

You’ll need to utilize a quantum computer to handle quantum mechanics problems.

This may be the “hottest” research topic on the planet right now, with some of the top researchers in computer science and physics worldwide working on it.

You could be too.

18.) Genomics

Genomics may be the only research topic that can compete with quantum computing regarding the “number of top researchers working on it.”

Genomics is a fantastic intersection of data science because it allows us to understand how genes work.

This is done by sequencing the DNA of different organisms to look for insights into our and other species.

Once we understand these patterns, we can use this information to improve our understanding of diseases and create new and innovative treatments for them.

Genomics is also used to study the evolution of different species.

Genomics is the future and a field begging for new and exciting research professionals to take it to the next step.

19.) Location-based services

Location-based services are an old and time-tested research topic in data science.

Since GPS and 4g cell phone reception became a thing, we’ve been trying to stay informed about how humans interact with their environment.

This is done by analyzing data from GPS tracking devices, cell phone towers, and Wi-Fi routers to look for insights into how humans interact.

Once we understand these practices, we can use this information to improve our geotargeting efforts, improve maps, find faster routes, and improve cohesion throughout a community.

Location-based services are used to understand the user, something every business could always use a little bit more of.

While a seemingly “stale” field, location-based services have seen a revival period with self-driving cars.

20.) Smart City Applications

Smart city applications are all the rage in data science research right now.

By harnessing the power of data, cities can become more efficient and sustainable.

But what exactly are smart city applications?

In short, they are systems that use data to improve city infrastructure and services.

This can include anything from traffic management and energy use to waste management and public safety.

Data is collected from various sources, including sensors, cameras, and social media.

It is then analyzed to identify tendencies and habits.

This information can make predictions about future needs and optimize city resources.

As more and more cities strive to become “smart,” the demand for data scientists with expertise in smart city applications is only growing.

21.) Internet Of Things (IoT)

The Internet of Things, or IoT, is exciting and new data science and sustainability research topic.

IoT is a network of physical objects embedded with sensors and connected to the internet.

These objects can include everything from alarm clocks to refrigerators; they’re all connected to the internet.

That means that they can share data with computers.

And that’s where data science comes in.

Data scientists are using IoT data to learn everything from how people use energy to how traffic flows through a city.

They’re also using IoT data to predict when an appliance will break down or when a road will be congested.

Really, the possibilities are endless.

With such a wide-open field, it’s easy to see why IoT is being researched by some of the top professionals in the world.

22.) Cybersecurity

Cybersecurity is a relatively new research topic in data science and in general, but it’s already garnering a lot of attention from businesses and organizations.

After all, with the increasing number of cyber attacks in recent years, it’s clear that we need to find better ways to protect our data.

While most of cybersecurity focuses on infrastructure, data scientists can leverage historical events to find potential exploits to protect their companies.

Sometimes, looking at a problem from a different angle helps, and that’s what data science brings to cybersecurity.

Also, data science can help to develop new security technologies and protocols.

As a result, cybersecurity is a crucial data science research area and one that will only become more important in the years to come.

23.) Blockchain

Blockchain is an incredible new research topic in data science for several reasons.

First, it is a distributed database technology that enables secure, transparent, and tamper-proof transactions.

Did someone say transmitting data?

This makes it an ideal platform for tracking data and transactions in various industries.

Second, blockchain is powered by cryptography, which not only makes it highly secure – but is a familiar foe for data scientists.

Finally, blockchain is still in its early stages of development, so there is much room for research and innovation.

As a result, blockchain is a great new research topic in data science that vows to revolutionize how we store, transmit and manage data.

24.) Sustainability

Sustainability is a relatively new research topic in data science, but it is gaining traction quickly.

To keep up with this demand, The Wharton School of the University of Pennsylvania has  started to offer an MBA in Sustainability .

This demand isn’t shocking, and some of the reasons include the following:

Sustainability is an important issue that is relevant to everyone.

Datasets on sustainability are constantly growing and changing, making it an exciting challenge for data scientists.

There hasn’t been a “set way” to approach sustainability from a data perspective, making it an excellent opportunity for interdisciplinary research.

As data science grows, sustainability will likely become an increasingly important research topic.

25.) Educational Data

Education has always been a great topic for research, and with the advent of big data, educational data has become an even richer source of information.

By studying educational data, researchers can gain insights into how students learn, what motivates them, and what barriers these students may face.

Besides, data science can be used to develop educational interventions tailored to individual students’ needs.

Imagine being the researcher that helps that high schooler pass mathematics; what an incredible feeling.

With the increasing availability of educational data, data science has enormous potential to improve the quality of education.

26.) Politics

As data science continues to evolve, so does the scope of its applications.

Originally used primarily for business intelligence and marketing, data science is now applied to various fields, including politics.

By analyzing large data sets, political scientists (data scientists with a cooler name) can gain valuable insights into voting patterns, campaign strategies, and more.

Further, data science can be used to forecast election results and understand the effects of political events on public opinion.

With the wealth of data available, there is no shortage of research opportunities in this field.

As data science evolves, so does our understanding of politics and its role in our world.

27.) Cloud Technologies

Cloud technologies are a great research topic.

It allows for the outsourcing and sharing of computer resources and applications all over the internet.

This lets organizations save money on hardware and maintenance costs while providing employees access to the latest and greatest software and applications.

I believe there is an argument that AWS could be the greatest and most technologically advanced business ever built (Yes, I know it’s only part of the company).

Besides, cloud technologies can help improve team members’ collaboration by allowing them to share files and work on projects together in real-time.

As more businesses adopt cloud technologies, data scientists must stay up-to-date on the latest trends in this area.

By researching cloud technologies, data scientists can help organizations to make the most of this new and exciting technology.

28.) Robotics

Robotics has recently become a household name, and it’s for a good reason.

First, robotics deals with controlling and planning physical systems, an inherently complex problem.

Second, robotics requires various sensors and actuators to interact with the world, making it an ideal application for machine learning techniques.

Finally, robotics is an interdisciplinary field that draws on various disciplines, such as computer science, mechanical engineering, and electrical engineering.

As a result, robotics is a rich source of research problems for data scientists.

29.) HealthCare

Healthcare is an industry that is ripe for data-driven innovation.

Hospitals, clinics, and health insurance companies generate a tremendous amount of data daily.

This data can be used to improve the quality of care and outcomes for patients.

This is perfect timing, as the healthcare industry is undergoing a significant shift towards value-based care, which means there is a greater need than ever for data-driven decision-making.

As a result, healthcare is an exciting new research topic for data scientists.

There are many different ways in which data can be used to improve healthcare, and there is a ton of room for newcomers to make discoveries.

30.) Remote Work

There’s no doubt that remote work is on the rise.

In today’s global economy, more and more businesses are allowing their employees to work from home or anywhere else they can get a stable internet connection.

But what does this mean for data science? Well, for one thing, it opens up a whole new field of research.

For example, how does remote work impact employee productivity?

What are the best ways to manage and collaborate on data science projects when team members are spread across the globe?

And what are the cybersecurity risks associated with working remotely?

These are just a few of the questions that data scientists will be able to answer with further research.

So if you’re looking for a new topic to sink your teeth into, remote work in data science is a great option.

31.) Data-Driven Journalism

Data-driven journalism is an exciting new field of research that combines the best of both worlds: the rigor of data science with the creativity of journalism.

By applying data analytics to large datasets, journalists can uncover stories that would otherwise be hidden.

And telling these stories compellingly can help people better understand the world around them.

Data-driven journalism is still in its infancy, but it has already had a major impact on how news is reported.

In the future, it will only become more important as data becomes increasingly fluid among journalists.

It is an exciting new topic and research field for data scientists to explore.

32.) Data Engineering

Data engineering is a staple in data science, focusing on efficiently managing data.

Data engineers are responsible for developing and maintaining the systems that collect, process, and store data.

In recent years, there has been an increasing demand for data engineers as the volume of data generated by businesses and organizations has grown exponentially.

Data engineers must be able to design and implement efficient data-processing pipelines and have the skills to optimize and troubleshoot existing systems.

If you are looking for a challenging research topic that would immediately impact you worldwide, then improving or innovating a new approach in data engineering would be a good start.

33.) Data Curation

Data curation has been a hot topic in the data science community for some time now.

Curating data involves organizing, managing, and preserving data so researchers can use it.

Data curation can help to ensure that data is accurate, reliable, and accessible.

It can also help to prevent research duplication and to facilitate the sharing of data between researchers.

Data curation is a vital part of data science. In recent years, there has been an increasing focus on data curation, as it has become clear that it is essential for ensuring data quality.

As a result, data curation is now a major research topic in data science.

There are numerous books and articles on the subject, and many universities offer courses on data curation.

Data curation is an integral part of data science and will only become more important in the future.

34.) Meta-Learning

Meta-learning is gaining a ton of steam in data science. It’s learning how to learn.

So, if you can learn how to learn, you can learn anything much faster.

Meta-learning is mainly used in deep learning, as applications outside of this are generally pretty hard.

In deep learning, many parameters need to be tuned for a good model, and there’s usually a lot of data.

You can save time and effort if you can automatically and quickly do this tuning.

In machine learning, meta-learning can improve models’ performance by sharing knowledge between different models.

For example, if you have a bunch of different models that all solve the same problem, then you can use meta-learning to share the knowledge between them to improve the cluster (groups) overall performance.

I don’t know how anyone looking for a research topic could stay away from this field; it’s what the  Terminator  warned us about!

35.) Data Warehousing

A data warehouse is a system used for data analysis and reporting.

It is a central data repository created by combining data from multiple sources.

Data warehouses are often used to store historical data, such as sales data, financial data, and customer data.

This data type can be used to create reports and perform statistical analysis.

Data warehouses also store data that the organization is not currently using.

This type of data can be used for future research projects.

Data warehousing is an incredible research topic in data science because it offers a variety of benefits.

Data warehouses help organizations to save time and money by reducing the need for manual data entry.

They also help to improve the accuracy of reports and provide a complete picture of the organization’s performance.

Data warehousing feels like one of the weakest parts of the Data Science Technology Stack; if you want a research topic that could have a monumental impact – data warehousing is an excellent place to look.

36.) Business Intelligence

Business intelligence aims to collect, process, and analyze data to help businesses make better decisions.

Business intelligence can improve marketing, sales, customer service, and operations.

It can also be used to identify new business opportunities and track competition.

BI is business and another tool in your company’s toolbox to continue dominating your area.

Data science is the perfect tool for business intelligence because it combines statistics, computer science, and machine learning.

Data scientists can use business intelligence to answer questions like, “What are our customers buying?” or “What are our competitors doing?” or “How can we increase sales?”

Business intelligence is a great way to improve your business’s bottom line and an excellent opportunity to dive deep into a well-respected research topic.

37.) Crowdsourcing

One of the newest areas of research in data science is crowdsourcing.

Crowdsourcing is a process of sourcing tasks or projects to a large group of people, typically via the internet.

This can be done for various purposes, such as gathering data, developing new algorithms, or even just for fun (think: online quizzes and surveys).

But what makes crowdsourcing so powerful is that it allows businesses and organizations to tap into a vast pool of talent and resources they wouldn’t otherwise have access to.

And with the rise of social media, it’s easier than ever to connect with potential crowdsource workers worldwide.

Imagine if you could effect that, finding innovative ways to improve how people work together.

That would have a huge effect.

Final Thoughts, Are These Research Topics In Data Science For You?

Thirty-seven different research topics in data science are a lot to take in, but we hope you found a research topic that interests you.

If not, don’t worry – there are plenty of other great topics to explore.

The important thing is to get started with your research and find ways to apply what you learn to real-world problems.

We wish you the best of luck as you begin your data science journey!

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Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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PhD in Data Science

Students conduct research on cutting edge problems alongside preeminent faculty at UChicago and explore the emerging field of Data Science. As an emerging discipline, Data Science addresses foundational problems across the entire data life cycle. Tackling issues of inequity, climate change, and sustainability will require cutting edge research in artificial intelligence and data usage combined with innovative educational programs to train students in the concepts of information systems. Students of Data Science will not only immerse themselves in a rapidly evolving field; they will help redefine it altogether.

Research Excellence:

As a PhD student in Data Science, you will learn from faculty who have developed research programs that span a wide variety of data science and AI topics, from theory to applications, with a focus on making a societal impact.

Research Topics:

• Artificial Intelligence
• Data, AI, and Society
• Data Systems
• Human-Centered Data Science
• Machine Learning and Statistics
• Use-Inspired Data Science

For more information, including a link to the application, see the Committee on Data Science website .

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PhD in Computing & Data Sciences

For more information and to get in touch, please visit the Faculty of Computing & Data Sciences website .

The PhD program in Computing & Data Sciences (CDS) at Boston University prepares its graduates to make significant contributions to the art, science, and engineering of computational and data-driven processes that are woven into all aspects of society, economy, and public discourse, leading to solution of problems and synthesis of knowledge related to the methodical, generalizable, and scalable extraction of insights from data as well as the design of new information systems and products that enable actionable use of those insights to advance scholarly as well as practical pursuits in a wide range of application domains.

Applicants to the PhD program in CDS are expected to have earned a bachelor’s or master’s degree in one of the methodological or applied disciplines relating to the computational and data-driven areas of scholarship in CDS. They are expected to possess basic mathematical and computational competencies, and demonstrable propensity for cross-disciplinary work. To accommodate a diversity of student backgrounds and preparations, a holistic admission review is utilized. As such, GRE tests and scores are not required, but could be optionally provided and considered as part of the applicant’s portfolio, which may also include evidence of prior, relevant preparation, including creative works, software code repositories, etc. Special attention will be paid to applicants from underrepresented minorities in computing and data science disciplines.

Completion of the PhD degree in CDS requires coursework covering breadth and depth topics spanning the foundational, applied, and sociotechnical dimensions of computing and data science; completion of research rotations that expose students to ongoing projects; completion of a cohort-based training on ethical and responsible computing; and successful proposal and defense of a doctoral thesis.

For their thesis work, and in preparation for careers in academia, industry, and government, CDS PhD students are expected to pursue theoretical, applied, or empirical studies leading to solution of new problems and synthesis of new knowledge in a topic area determined in consultation with their mentors and collaborators, which may include external researchers and practitioners in industrial and academic research laboratories.

Upon completion of the program, students will be prepared to pursue careers in which they lead independent cutting-edge research and development agendas, whether in academia (by teaching, mentoring, and supervising teams of students engaged in scholarly pursuits) or in industry (by collaborating, directing, and effectively managing diverse teams of practitioners working at the forefront of industrial R&D).

Learning Outcomes

The following learning outcomes explain what you will be able to do at the end of your time as a CDS PhD candidate, as a result of earning your degree.

• Exhibit a strong grasp of the principles governing the design and implementation of the methodological approaches for computational and data-driven inquiry.
• Identify the literature and demonstrate mastery of the compendium of works relevant to a well-defined area of research inquiry in computing and data sciences.
• Show capacity to engage meaningfully in and materially contribute to multidisciplinary research and development endeavors.
• Evidence a strong sense of social and professional responsibility for decisions related to the development and deployment of computational and data-driven technologies.
• Assess and argue the merits, limitations, and possibilities of new research work in a specialized area at the level commensurate with standards of scholarly venues in that area.
• Formulate and pursue a research agenda leading to solution of new problems and to synthesis of new knowledge shared through peer-reviewed publications.

Course Requirements

Sixteen semester courses (64 credits) are required for post-BA/BS students and 12 semester courses (48 credits) are required for post-MA/MS students. Students with prior graduate work (including master’s degrees) may be able to transfer up to two courses (8 credits) as long as these credits were not used to fulfill matriculation requirements, upon the recommendation of the student’s academic advisor, and subject to approval by the Associate Provost for CDS.

Of the 16 courses, up to 3 undergraduate courses (12 credits) may be counted as background courses, selected in consultation with the student’s academic advisor and subject to approval by the Associate Provost for CDS. Other than these remedial courses, all other courses must be graduate-level courses or directed studies offered by CDS or by other BU departments in order to satisfy the following degree requirements.

The methodology core requirement ensures that students possess foundational knowledge and competencies in a subset of the following eight methodological areas of CDS:

• Mathematical Foundations of Data Science
• Statistical Modeling and Inference
• Efficient and Scalable Algorithms
• Predictive Analytics and Machine Learning
• Combinatorial Optimization and Algorithms
• Computational Complexity
• Programming and Software Design
• Large-scale Data Management

A list of courses that can be used to satisfy these competencies will be maintained on the website for CDS. Students who start their PhD program in CDS are expected to satisfy at least six of these competencies. Students who complete the course requirement for the PhD program in a cognate discipline are expected to satisfy at least four of these competencies.

The subject core requirement ensures that students establish depth in one area of inquiry that is aligned with either the methodological or applied dimensions of CDS. Subject areas are defined by groups of CDS faculty members working in related disciplinary and/or interdisciplinary areas of research who expect their prospective students to have enough depth in the subset of topics to enable them to tackle doctoral-level research in these topics. The set of subject areas as well as a list of preapproved graduate-level courses offered in CDS or elsewhere at BU that can be used to satisfy each subject area will be maintained on the website for CDS.

During the first two years in the program, all PhD candidates in CDS must complete three cohort-based requirements; namely, a two-semester training course (4 credits) covering various aspects of the responsible and ethical conduct of computational and data-driven research, a two-semester doctoral seminar (4 credits) that introduces them to the research portfolios of CDS faculty members as well as to the skills and capacities needed for success as scholars, and at least two research or lab rotations (8 credits) that expose them to real-world computational and data-driven applications that must be tackled through effective multidisciplinary teamwork.

A cumulative GPA not less than 3.3 must be maintained for all non-Pass/Fail courses taken to satisfy the methodology core requirement and the subject core requirement of the degree, excluding any background courses and excluding any transferred credits. Students who receive grades of B– or lower in any three courses taken at BU will be withdrawn from the program.

Language Requirement

There is no foreign language requirement for the PhD degree in CDS.

Qualifying Examinations

No later than the end of the sixth semester (third year), all PhD candidates in CDS must pass a public oral examination administered by a committee of three faculty members, chaired by the student’s research (and presumptive thesis) advisor or coadvisors. The oral area exam is meant to establish the student mastery of a well-defined area of scholarship and preparedness to pursue original research in that area. The oral area examination may require completion of a survey paper or completion of a pilot project ahead of the examination. The scope as well as any additional requirements needed for the examination should be developed in consultation with and approval of the research advisor(s), at least one semester prior to the exam.

Dissertation and Final Oral Examination

Candidates shall demonstrate their abilities for independent study in a dissertation representing original research or creative scholarship. A prospectus for the dissertation must be successfully defended no later than the end of the eighth semester (fourth year) of study.

Candidates must undergo a final oral examination no later than the end of the 10th semester (fifth year) of study in which they defend their dissertation as a valuable contribution to knowledge in their field and demonstrate a mastery of their field of specialization in relation to their dissertation.

Both the prospectus and final dissertation must be administered by a dissertation committee of at least three readers (including the dissertation advisor or coadvisors) and chaired by a CDS faculty member who is not one of the readers.

Related Bulletin Pages

• Abbreviations and Symbols

Beyond the Bulletin

• Faculty of Computing & Data Sciences
• Data Science for Good
• Impact Labs & Co-Labs
• BS in Data Science
• MS in Data Science
• PhD in Computing & Data Sciences
• Minor in Data Science

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PhD in Data Science – Your Guide to Choosing a Doctorate Degree Program

Created by aasif.faizal

Professional opportunities in data science are growing incredibly fast. That’s great news for students looking to pursue a career as a data scientist. But it also means that there are a lot more options out there to investigate and understand before developing the best educational path for you.

A PhD is the most advanced data science degree you can get, reflecting a depth of knowledge and technical expertise that will put you at the top of your field.

This means that PhD programs are the most time-intensive degree option out there, typically requiring that students complete dissertations involving rigorous research. This means that PhDs are not for everyone. Indeed, many who work in the world of big data hold master’s degrees rather than PhDs, which tend to involve the same coursework as PhD programs without a dissertation component. However, for the right candidate, a PhD program is the perfect choice to become a true expert on your area of focus.

If you’ve concluded that a data science PhD is the right path for you, this guide is intended to help you choose the best program to suit your needs. It will walk through some of the key considerations while picking graduate data science programs and some of the nuts and bolts (like course load and tuition costs) that are part of the data science PhD decision-making process.

Data Science PhD vs. Masters: Choosing the right option for you

If you’re considering pursuing a data science PhD, it’s worth knowing that such an advanced degree isn’t strictly necessary in order to get good work opportunities. Many who work in the field of big data only hold master’s degrees, which is the level of education expected to be a competitive candidate for data science positions.

So why pursue a data science PhD?

Simply put, a PhD in data science will leave you qualified to enter the big data industry at a high level from the outset.

You’ll be eligible for advanced positions within companies, holding greater responsibilities, keeping more direct communication with leadership, and having more influence on important data-driven decisions. You’re also likely to receive greater compensation to match your rank.

However, PhDs are not for everyone. Dissertations require a great deal of time and an interest in intensive research. If you are eager to jumpstart a career quickly, a master’s program will give you the preparation you need to hit the ground running. PhDs are appropriate for those who want to commit their time and effort to schooling as a long-term investment in their professional trajectory.

For more information on the difference between data science PhD’s and master’s programs, take a look at our guide here.

Topics include:

• Can I get an Online Ph.D in Data Science?
• Overview of Ph.d Coursework

Preparing for a Doctorate Program

Building a solid track record of professional experience, things to consider when choosing a school.

• What Does it Cost to Get a Ph.D in Data Science?
• School Listings

Data Science PhD Programs, Historically

Historically, data science PhD programs were one of the main avenues to get a good data-related position in academia or industry. But, PhD programs are heavily research oriented and require a somewhat long term investment of time, money, and energy to obtain. The issue that some data science PhD holders are reporting, especially in industry settings, is that that the state of the art is moving so quickly, and that the data science industry is evolving so rapidly, that an abundance of research oriented expertise is not always what’s heavily sought after.

Instead, many companies are looking for candidates who are up to date with the latest data science techniques and technologies, and are willing to pivot to match emerging trends and practices.

One recent development that is making the data science graduate school decisions more complex is the introduction of specialty master’s degrees, that focus on rigorous but compact, professional training. Both students and companies are realizing the value of an intensive, more industry-focused degree that can provide sufficient enough training to manage complex projects and that are more client oriented, opposed to research oriented.

However, not all prospective data science PhD students are looking for jobs in industry. There are some pretty amazing research opportunities opening up across a variety of academic fields that are making use of new data collection and analysis tools. Experts that understand how to leverage data systems including statistics and computer science to analyze trends and build models will be in high demand.

Can You Get a PhD in Data Science Online?

While it is not common to get a data science Ph.D. online, there are currently two options for those looking to take advantage of the flexibility of an online program.

Indiana University Bloomington and Northcentral University both offer online Ph.D. programs with either a minor or specialization in data science.

Given the trend for schools to continue increasing online offerings, expect to see additional schools adding this option in the near future.

Overview of PhD Coursework

A PhD requires a lot of academic work, which generally requires between four and five years (sometimes longer) to complete.

Here are some of the high level factors to consider and evaluate when comparing data science graduate programs.

How many credits are required for a PhD in data science?

On average, it takes 71 credits to graduate with a PhD in data science — far longer (almost double) than traditional master’s degree programs. In addition to coursework, most PhD students also have research and teaching responsibilities that can be simultaneously demanding and really great career preparation.

What’s the core curriculum like?

In a data science doctoral program, you’ll be expected to learn many skills and also how to apply them across domains and disciplines. Core curriculums will vary from program to program, but almost all will have a core foundation of statistics.

All PhD candidates will have to take a qualifying exam. This can vary from university to university, but to give you some insight, it is broken up into three phases at Yale. They have a practical exam, a theory exam and an oral exam. The goal is to make sure doctoral students are developing the appropriate level of expertise.

Dissertation

One of the final steps of a PhD program involves presenting original research findings in a formal document called a dissertation. These will provide background and context, as well as findings and analysis, and can contribute to the understanding and evolution of data science. A dissertation idea most often provides the framework for how a PhD candidate’s graduate school experience will unfold, so it’s important to be thoughtful and deliberate while considering research opportunities.

Since data science is such a rapidly evolving field and because choosing the right PhD program is such an important factor in developing a successful career path, there are some steps that prospective doctoral students can take in advance to find the best-fitting opportunity.

Join professional associations

Even before being fully credentials, joining professional associations and organizations such as the Data Science Association and the American Association of Big Data Professionals is a good way to get exposure to the field. Many professional societies are welcoming to new members and even encourage student participation with things like discounted membership fees and awards and contest categories for student researchers. One of the biggest advantages to joining is that these professional associations bring together other data scientists for conference events, research-sharing opportunities, networking and continuing education opportunities.

Leverage your social network

Be on the lookout to make professional connections with professors, peers, and members of industry. There are a number of LinkedIn groups dedicated to data science. A well-maintained professional network is always useful to have when looking for advice or letters of recommendation while applying to graduate school and then later while applying for jobs and other career-related opportunities.

Kaggle competitions

Kaggle competitions provide the opportunity to solve real-world data science problems and win prizes. A list of data science problems can be found at Kaggle.com . Winning one of these competitions is a good way to demonstrate professional interest and experience.

Internships

Internships are a great way to get real-world experience in data science while also getting to work for top names in the world of business. For example, IBM offers a data science internship which would also help to stand out when applying for PhD programs, as well as in seeking employment in the future.

Demonstrating professional experience is not only important when looking for jobs, but it can also help while applying for graduate school. There are a number of ways for prospective students to gain exposure to the field and explore different facets of data science careers.

Get certified

There are a number of data-related certificate programs that are open to people with a variety of academic and professional experience. DeZyre has an excellent guide to different certifications, some of which might help provide good background for graduate school applications.

Conferences

Conferences are a great place to meet people presenting new and exciting research in the data science field and bounce ideas off of newfound connections. Like professional societies and organizations, discounted student rates are available to encourage student participation. In addition, some conferences will waive fees if you are presenting a poster or research at the conference, which is an extra incentive to present.

It can be hard to quantify what makes a good-fit when it comes to data science graduate school programs. There are easy to evaluate factors, such as cost and location, and then there are harder to evaluate criteria such as networking opportunities, accessibility to professors, and the up-to-dateness of the program’s curriculum.

Nevertheless, there are some key relevant considerations when applying to almost any data science graduate program.

What most schools will require when applying:

• All undergraduate and graduate transcripts
• A statement of intent for the program (reason for applying and future plans)
• Letters of reference
• Application fee
• Online application
• A curriculum vitae (outlining all of your academic and professional accomplishments)

What Does it Cost to Get a PhD in Data Science?

The great news is that many PhD data science programs are supported by fellowships and stipends. Some are completely funded, meaning the school will pay tuition and basic living expenses. Here are several examples of fully funded programs:

• University of Southern California
• University of Nevada, Reno
• Kennesaw State University
• Worcester Polytechnic Institute
• University of Maryland

For all other programs, the average range of tuition, depending on the school can range anywhere from $1,300 per credit hour to $2,000 amount per credit hour. Remember, typical PhD programs in data science are between 60 and 75 credit hours, meaning you could spend up to $150,000 over several years.

That’s why the financial aspects are so important to evaluate when assessing PhD programs, because some schools offer full stipends so that you are able to attend without having to find supplemental scholarships or tuition assistance.

Can I become a professor of data science with a PhD.? Yes! If you are interested in teaching at the college or graduate level, a PhD is the degree needed to establish the full expertise expected to be a professor. Some data scientists who hold PhDs start by entering the field of big data and pivot over to teaching after gaining a significant amount of work experience. If you’re driven to teach others or to pursue advanced research in data science, a PhD is the right degree for you.

Do I need a master’s in order to pursue a PhD.? No. Many who pursue PhDs in Data Science do not already hold advanced degrees, and many PhD programs include all the coursework of a master’s program in the first two years of school. For many students, this is the most time-effective option, allowing you to complete your education in a single pass rather than interrupting your studies after your master’s program.

Can I choose to pursue a PhD after already receiving my master’s? Yes. A master’s program can be an opportunity to get the lay of the land and determine the specific career path you’d like to forge in the world of big data. Some schools may allow you to simply extend your academic timeline after receiving your master’s degree, and it is also possible to return to school to receive a PhD if you have been working in the field for some time.

If a PhD. isn’t necessary, is it a waste of time? While not all students are candidates for PhDs, for the right students – who are keen on doing in-depth research, have the time to devote to many years of school, and potentially have an interest in continuing to work in academia – a PhD is a great choice. For more information on this question, take a look at our article Is a Data Science PhD. Worth It?

Complete List of Data Science PhD Programs

Below you will find the most comprehensive list of schools offering a doctorate in data science. Each school listing contains a link to the program specific page, GRE or a master’s degree requirements, and a link to a page with detailed course information.

Note that the listing only contains true data science programs. Other similar programs are often lumped together on other sites, but we have chosen to list programs such as data analytics and business intelligence on a separate section of the website.

Boise State University  – Boise, Idaho PhD in Computing – Data Science Concentration

The Data Science emphasis focuses on the development of mathematical and statistical algorithms, software, and computing systems to extract knowledge or insights from data.  

In 60 credits, students complete an Introduction to Graduate Studies, 12 credits of core courses, 6 credits of data science elective courses, 10 credits of other elective courses, a Doctoral Comprehensive Examination worth 1 credit, and a 30-credit dissertation.

Electives can be taken in focus areas such as Anthropology, Biometry, Ecology/Evolution and Behavior, Econometrics, Electrical Engineering, Earth Dynamics and Informatics, Geoscience, Geostatistics, Hydrology and Hydrogeology, Materials Science, and Transportation Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $7,236 total (Resident), $24,573 total (Non-resident)

View Course Offerings

Bowling Green State University  – Bowling Green, Ohio Ph.D. in Data Science

Data Science students at Bowling Green intertwine knowledge of computer science with statistics.

Students learn techniques in analyzing structured, unstructured, and dynamic datasets.

Courses train students to understand the principles of analytic methods and articulating the strengths and limitations of analytical methods.

The program requires 60 credit hours in the studies of Computer Science (6 credit hours), Statistics (6 credit hours), Data Science Exploration and Communication, Ethical Issues, Advanced Data Mining, and Applied Data Science Experience.

Students must also complete 21 credit hours of elective courses, a qualifying exam, a preliminary exam, and a dissertation.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,418 (Resident), $14,410 (Non-resident)

Brown University  – Providence, Rhode Island PhD in Computer Science – Concentration in Data Science

Brown University’s database group is a world leader in systems-oriented database research; they seek PhD candidates with strong system-building skills who are interested in researching TupleWare, MLbase, MDCC, Crowd DB, or PIQL.

In order to gain entrance, applicants should consider first doing a research internship at Brown with this group. Other ways to boost an application are to take and do well at massive open online courses, do an internship at a large company, and get involved in a large open-source software project.

Coding well in C++ is preferred.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $62,680 total

Chapman University  – Irvine, California Doctorate in Computational and Data Sciences

Candidates for the doctorate in computational and data science at Chapman University begin by completing 13 core credits in basic methodologies and techniques of computational science.

Students complete 45 credits of electives, which are personalized to match the specific interests and research topics of the student.

Finally, students complete up to 12 credits in dissertation research.

Applicants must have completed courses in differential equations, data structures, and probability and statistics, or take specific foundation courses, before beginning coursework toward the PhD.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,538 per year

Clemson University / Medical University of South Carolina (MUSC) – Joint Program – Clemson, South Carolina & Charleston, South Carolina Doctor of Philosophy in Biomedical Data Science and Informatics – Clemson

The PhD in biomedical data science and informatics is a joint program co-authored by Clemson University and the Medical University of South Carolina (MUSC).

Students choose one of three tracks to pursue: precision medicine, population health, and clinical and translational informatics. Students complete 65-68 credit hours, and take courses in each of 5 areas: biomedical informatics foundations and applications; computing/math/statistics/engineering; population health, health systems, and policy; biomedical/medical domain; and lab rotations, seminars, and doctoral research.

Applicants must have a bachelor’s in health science, computing, mathematics, statistics, engineering, or a related field, and it is recommended to also have competency in a second of these areas.

Program requirements include a year of calculus and college biology, as well as experience in computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,858 total (South Carolina Resident), $22,566 total (Non-resident)

View Course Offerings – Clemson

George Mason University  – Fairfax, Virginia Doctor of Philosophy in Computational Sciences and Informatics – Emphasis in Data Science

George Mason’s PhD in computational sciences and informatics requires a minimum of 72 credit hours, though this can be reduced if a student has already completed a master’s. 48 credits are toward graduate coursework, and an additional 24 are for dissertation research.

Students choose an area of emphasis—either computer modeling and simulation or data science—and completed 18 credits of the coursework in this area. Students are expected to completed the coursework in 4-5 years.

Applicants to this program must have a bachelor’s degree in a natural science, mathematics, engineering, or computer science, and must have knowledge and experience with differential equations and computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $13,426 total (Virginia Resident), $35,377 total (Non-resident)

Harrisburg University of Science and Technology  – Harrisburg, Pennsylvania Doctor of Philosophy in Data Sciences

Harrisburg University’s PhD in data science is a 4-5 year program, the first 2 of which make up the Harrisburg master’s in analytics.

Beyond this, PhD candidates complete six milestones to obtain the degree, including 18 semester hours in doctoral-level courses, such as multivariate data analysis, graph theory, machine learning.

Following the completion of ANLY 760 Doctoral Research Seminar, students in the program complete their 12 hours of dissertation research bringing the total program hours to 36.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $14,940 total

Icahn School of Medicine at Mount Sinai  – New York, New York Genetics and Data Science, PhD

As part of the Biomedical Science PhD program, the Genetics and Data Science multidisciplinary training offers research opportunities that expand on genetic research and modern genomics. The training also integrates several disciplines of biomedical sciences with machine learning, network modeling, and big data analysis.

Students in the Genetics and Data Science program complete a predetermined course schedule with a total of 64 credits and 3 years of study.

Additional course requirements and electives include laboratory rotations, a thesis proposal exam and thesis defense, Computer Systems, Intro to Algorithms, Machine Learning for Biomedical Data Science, Translational Genomics, and Practical Analysis of a Personal Genome.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $31,303 total

Indiana University-Purdue University Indianapolis  – Indianapolis, Indiana PhD in Data Science PhD Minor in Applied Data Science

Doctoral candidates pursuing the PhD in data science at Indiana University-Purdue must display competency in research, data analytics, and at management and infrastructure to earn the degree.

The PhD is comprised of 24 credits of a data science core, 18 credits of methods courses, 18 credits of a specialization, written and oral qualifying exams, and 30 credits of dissertation research. All requirements must be completed within 7 years.

Applicants are generally expected to have a master’s in social science, health, data science, or computer science. 

Currently a majority of the PhD students at IUPUI are funded by faculty grants and two are funded by the federal government. None of the students are self funded.

IUPUI also offers a PhD Minor in Applied Data Science that is 12-18 credits. The minor is open to students enrolled at IUPUI or IU Bloomington in a doctoral program other than Data Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $9,228 per year (Indiana Resident), $25,368 per year (Non-resident)

Jackson State University – Jackson, Mississippi PhD Computational and Data-Enabled Science and Engineering

Jackson State University offers a PhD in computational and data-enabled science and engineering with 5 concentration areas: computational biology and bioinformatics, computational science and engineering, computational physical science, computation public health, and computational mathematics and social science.

Students complete 12 credits of common core courses, 12 credits in the specialization, 24 credits of electives, and 24 credits in dissertation research.

Students may complete the doctoral program in as little as 5 years and no more than 8 years.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,270 total

Kennesaw State University  – Kennesaw, Georgia PhD in Analytics and Data Science

Students pursuing a PhD in analytics and data science at Kennesaw State University must complete 78 credit hours: 48 course hours and 6 electives (spread over 4 years of study), a minimum 12 credit hours for dissertation research, and a minimum 12 credit-hour internship.

Prior to dissertation research, the comprehensive examination will cover material from the three areas of study: computer science, mathematics, and statistics.

Successful applicants will have a master’s degree in a computational field, calculus I and II, programming experience, modeling experience, and are encouraged to have a base SAS certification.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,328 total (Georgia Resident), $19,188 total (Non-resident)

New Jersey Institute of Technology  – Newark, New Jersey PhD in Business Data Science

Students may enter the PhD program in business data science at the New Jersey Institute of Technology with either a relevant bachelor’s or master’s degree. Students with bachelor’s degrees begin with 36 credits of advanced courses, and those with master’s take 18 credits before moving on to credits in dissertation research.

Core courses include business research methods, data mining and analysis, data management system design, statistical computing with SAS and R, and regression analysis.

Students take qualifying examinations at the end of years 1 and 2, and must defend their dissertations successfully by the end of year 6.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $21,932 total (New Jersey Resident), $32,426 total (Non-resident)

New York University  – New York, New York PhD in Data Science

Doctoral candidates in data science at New York University must complete 72 credit hours, pass a comprehensive and qualifying exam, and defend a dissertation with 10 years of entering the program.

Required courses include an introduction to data science, probability and statistics for data science, machine learning and computational statistics, big data, and inference and representation.

Applicants must have an undergraduate or master’s degree in fields such as mathematics, statistics, computer science, engineering, or other scientific disciplines. Experience with calculus, probability, statistics, and computer programming is also required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,332 per year

View Course Offering

Northcentral University  – San Diego, California PhD in Data Science-TIM

Northcentral University offers a PhD in technology and innovation management with a specialization in data science.

The program requires 60 credit hours, including 6-7 core courses, 3 in research, a PhD portfolio, and 4 dissertation courses.

The data science specialization requires 6 courses: data mining, knowledge management, quantitative methods for data analytics and business intelligence, data visualization, predicting the future, and big data integration.

Applicants must have a master’s already.

Delivery Method: Online GRE: Required 2022-2023 Tuition: $16,794 total

Stevens Institute of Technology – Hoboken, New Jersey Ph.D. in Data Science

Stevens Institute of Technology has developed a data science Ph.D. program geared to help graduates become innovators in the space.

The rigorous curriculum emphasizes mathematical and statistical modeling, machine learning, computational systems and data management.

The program is directed by Dr. Ted Stohr, a recognized thought leader in the information systems, operations and business process management arenas.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $39,408 per year

University at Buffalo – Buffalo, New York PhD Computational and Data-Enabled Science and Engineering

The curriculum for the University of Buffalo’s PhD in computational and data-enabled science and engineering centers around three areas: data science, applied mathematics and numerical methods, and high performance and data intensive computing. 9 credit course of courses must be completed in each of these three areas. Altogether, the program consists of 72 credit hours, and should be completed in 4-5 years. A master’s degree is required for admission; courses taken during the master’s may be able to count toward some of the core coursework requirements.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,310 per year (New York Resident), $23,100 per year (Non-resident)

University of Colorado Denver – Denver, Colorado PhD in Big Data Science and Engineering

The University of Colorado – Denver offers a unique program for those students who have already received admission to the computer science and information systems PhD program.

The Big Data Science and Engineering (BDSE) program is a PhD fellowship program that allows selected students to pursue research in the area of big data science and engineering. This new fellowship program was created to train more computer scientists in data science application fields such as health informatics, geosciences, precision and personalized medicine, business analytics, and smart cities and cybersecurity.

Students in the doctoral program must complete 30 credit hours of computer science classes beyond a master’s level, and 30 credit hours of dissertation research.

The BDSE fellowship requires students to have an advisor both in the core disciplines (either computer science or mathematics and statistics) as well as an advisor in the application discipline (medicine and public health, business, or geosciences).

In addition, the fellowship covers full stipend, tuition, and fees up to ~50k for BDSE fellows annually. Important eligibility requirements can be found here.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $55,260 total

University of Marylan d  – College Park, Maryland PhD in Information Studies

Data science is a potential research area for doctoral candidates in information studies at the University of Maryland – College Park. This includes big data, data analytics, and data mining.

Applicants for the PhD must have taken the following courses in undergraduate studies: programming languages, data structures, design and analysis of computer algorithms, calculus I and II, and linear algebra.

Students must complete 6 qualifying courses, 2 elective graduate courses, and at least 12 credit hours of dissertation research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $16,238 total (Maryland Resident), $35,388 total (Non-resident)

University of Massachusetts Boston  – Boston, Massachusetts PhD in Business Administration – Information Systems for Data Science Track

The University of Massachusetts – Boston offers a PhD in information systems for data science. As this is a business degree, students must complete coursework in their first two years with a focus on data for business; for example, taking courses such as business in context: markets, technologies, and societies.

Students must take and pass qualifying exams at the end of year 1, comprehensive exams at the end of year 2, and defend their theses at the end of year 4.

Those with a degree in statistics, economics, math, computer science, management sciences, information systems, and other related fields are especially encouraged, though a quantitative degree is not necessary.

Students accepted by the program are ordinarily offered full tuition credits and a stipend ($25,000 per year) to cover educational expenses and help defray living costs for up to three years of study.

During the first two years of coursework, they are assigned to a faculty member as a research assistant; for the third year students will be engaged in instructional activities. Funding for the fourth year is merit-based from a limited pool of program funds

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $18,894 total (in-state), $36,879 (out-of-state)

University of Nevada Reno – Reno, Nevada PhD in Statistics and Data Science

The University of Nevada – Reno’s doctoral program in statistics and data science is comprised of 72 credit hours to be completed over the course of 4-5 years. Coursework is all within the scope of statistics, with titles such as statistical theory, probability theory, linear models, multivariate analysis, statistical learning, statistical computing, time series analysis.

The completion of a Master’s degree in mathematics or statistics prior to enrollment in the doctoral program is strongly recommended, but not required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,814 total (in-state), $22,356 (out-of-state)

University of Southern California – Los Angles, California PhD in Data Sciences & Operations

USC Marshall School of Business offers a PhD in data sciences and operations to be completed in 5 years.

Students can choose either a track in operations management or in statistics. Both tracks require 4 courses in fall and spring of the first 2 years, as well as a research paper and courses during the summers. Year 3 is devoted to dissertation preparation and year 4 and/or 5 to dissertation defense.

A bachelor’s degree is necessary for application, but no field or further experience is required.

Students should complete 60 units of coursework. If the students are admitted with Advanced Standing (e.g., Master’s Degree in appropriate field), this requirement may be reduced to 40 credits.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $63,468 total

University of Tennessee-Knoxville  – Knoxville, Tennessee The Data Science and Engineering PhD

The data science and engineering PhD at the University of Tennessee – Knoxville requires 36 hours of coursework and 36 hours of dissertation research. For those entering with an MS degree, only 24 hours of course work is required.

The core curriculum includes work in statistics, machine learning, and scripting languages and is enhanced by 6 hours in courses that focus either on policy issues related to data, or technology entrepreneurship.

Students must also choose a knowledge specialization in one of these fields: health and biological sciences, advanced manufacturing, materials science, environmental and climate science, transportation science, national security, urban systems science, and advanced data science.

Applicants must have a bachelor’s or master’s degree in engineering or a scientific field. 

All students that are admitted will be supported by a research fellowship and tuition will be included.

Many students will perform research with scientists from Oak Ridge national lab, which is located about 30 minutes drive from campus.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,468 total (Tennessee Resident), $29,656 total (Non-resident)

University of Vermont – Burlington, Vermont Complex Systems and Data Science (CSDS), PhD

Through the College of Engineering and Mathematical Sciences, the Complex Systems and Data Science (CSDS) PhD program is pan-disciplinary and provides computational and theoretical training. Students may customize the program depending on their chosen area of focus.

Students in this program work in research groups across campus.

Core courses include Data Science, Principles of Complex Systems and Modeling Complex Systems. Elective courses include Machine Learning, Complex Networks, Evolutionary Computation, Human/Computer Interaction, and Data Mining.

The program requires at least 75 credits to graduate with approval by the student graduate studies committee.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $12,204 total (Vermont Resident), $30,960 total (Non-resident)

University of Washington Seattle Campus – Seattle, Washington PhD in Big Data and Data Science

The University of Washington’s PhD program in data science has 2 key goals: training of new data scientists and cyberinfrastructure development, i.e., development of open-source tools and services that scientists around the world can use for big data analysis.

Students must take core courses in data management, machine learning, data visualization, and statistics.

Students are also required to complete at least one internship that covers practical work in big data.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $17,004 per year (Washington resident), $30,477 (non-resident)

University of Wisconsin-Madison – Madison, Wisconsin PhD in Biomedical Data Science

The PhD program in Biomedical Data Science offered by the Department of Biostatistics and Medical Informatics at UW-Madison is unique, in blending the best of statistics and computer science, biostatistics and biomedical informatics. 

Students complete three year-long course sequences in biostatistics theory and methods, computer science/informatics, and a specialized sequence to fit their interests.

Students also complete three research rotations within their first two years in the program, to both expand their breadth of knowledge and assist in identifying a research advisor.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,728 total (in-state), $24,054 total (out-of-state)

Vanderbilt University – Nashville, Tennessee Data Science Track of the BMI PhD Program

The PhD in biomedical informatics at Vanderbilt has the option of a data science track.

Students complete courses in the areas of biomedical informatics (3 courses), computer science (4 courses), statistical methods (4 courses), and biomedical science (2 courses). Students are expected to complete core courses and defend their dissertations within 5 years of beginning the program.

Applicants must have a bachelor’s degree in computer science, engineering, biology, biochemistry, nursing, mathematics, statistics, physics, information management, or some other health-related field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $53,160 per year

Washington University in St. Louis – St. Louis, Missouri Doctorate in Computational & Data Sciences

Washington University now offers an interdisciplinary Ph.D. in Computational & Data Sciences where students can choose from one of four tracks (Computational Methodologies, Political Science, Psychological & Brain Sciences, or Social Work & Public Health).

Students are fully funded and will receive a stipend for at least five years contingent on making sufficient progress in the program.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $59,420 total

Worcester Polytechnic Institute – Worcester, Massachusetts PhD in Data Science

The PhD in data science at Worcester Polytechnic Institute focuses on 5 areas: integrative data science, business intelligence and case studies, data access and management, data analytics and mining, and mathematical analysis.

Students first complete a master’s in data science, and then complete 60 credit hours beyond the master’s, including 30 credit hours of research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $28,980 per year

Yale University – New Haven, Connecticut PhD Program – Department of Stats and Data Science

The PhD in statistics and data science at Yale University offers broad training in the areas of statistical theory, probability theory, stochastic processes, asymptotics, information theory, machine learning, data analysis, statistical computing, and graphical methods. Students complete 12 courses in the first year in these topics.

Students are required to teach one course each semester of their third and fourth years.

Most students complete and defend their dissertations in their fifth year.

Applicants should have an educational background in statistics, with an undergraduate major in statistics, mathematics, computer science, or similar field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $46,900 total

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PhD in Data Science

First Year Requirements

The standard first-year program requires students to complete nine courses: four required courses (1-4 below); one elective either in mathematical foundations or scalability and computing (pick from either 5 or 6); and finally four other electives that can come from proposed courses in data science or existing graduate courses in Computer Science or Statistics. Some students, after consulting with the committee graduate advisor, might decide to take the nine courses over the first two years.

Required courses:

• Foundations of Machine Learning and AI Part 1
• Responsible Use of Data and Algorithms
• Data Interaction
• Systems for Data and Computers/Data Design
• Foundations of Machine Learning and AI Part 2 
• Data Engineering and Scalable Computing

Synthesis project

Students will take courses during the first two years after which they focus primarily on their research. A milestone in this transition is completion of a synthesis project before the end of the second year in the program. Thesis projects can be done in partnership with any of DSI affiliates, and aims to meaningfully connect PhD students to their chosen focus areas.

Thesis Advisor and Dissertation Committee

Students typically select a thesis advisor by the beginning of their second year. By the end of the third year, each PhD student, after consultation with their advisor, shall establish a thesis committee of at least three faculty members, including the advisor, with at least half of the members coming from the Committee on Data Science.

Proposal Presentation and Admission to Candidacy

By the end of the third year, students should have scheduled and completed a proposal presentation to their committee, in order to be advanced to candidacy. The proposal presentation is typically an hourlong meeting that begins with a 30-minute presentation by the student, followed by a question and discussion period with the committee.

Dissertation Defense

The PhD degree will be awarded following a successful defense and the electronic submission of the final version of the dissertation to the University’s Dissertation Office.

DEPARTMENT OF STATISTICS AND DATA SCIENCE

Phd program, phd program overview.

The doctoral program in Statistics and Data Science is designed to provide students with comprehensive training in theory and methodology in statistics and data science, and their applications to problems in a wide range of fields. The program is flexible and may be arranged to reflect students' interests and career goals. Cross-disciplinary work is encouraged. The PhD program prepares students for careers as university teachers and researchers and as research statisticians or data scientists in industry, government and the non-profit sector.

Requirements

Students are required to fulfill the Department requirements in addition to those specified by The Graduate School (TGS).

From the Graduate School’s webpage outlining the general requirements for a PhD :

In order to receive a doctoral degree, students must:

• Complete all required coursework. .
• Gain admittance to candidacy.
• Submit a prospectus to be approved by a faculty committee.
• Present a dissertation with original research. Review the Dissertation Publication page for more information.
• Complete the necessary teaching requirement
• Submit necessary forms to file for graduation
• Complete degree requirements within the approved timeline

PhD degrees must be approved by the student's academic program. Consult with your program directly regarding specific degree requirements.

The Department requires that students in the Statistics and Data Science PhD program:

• Meet the department minimum residency requirement of 2 years
• STAT 344-0 Statistical Computing
• STAT 350-0 Regression Analysis
• STAT 353-0 Advanced Regression (new 2021-22)
• STAT 415-0 I ntroduction to Machine Learning
• STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3
• STAT 430-1, STAT 430-2, STAT 440 (new courses in 2022-23 on probability and stochastic processes for statistics students)
• STAT 457-0 Applied Bayesian Inference

Students generally complete the required coursework during their first two years in the PhD program. *note that required courses changed in the 2021-22 academic year, previous required courses can be found at the end of this page.

• Pass the Qualifying Exam. This comprehensive examination covers basic topics in statistics and is typically taken in fall quarter of the second year.

Pass the Prospectus presentation/examination and be admitted for PhD candidacy by the end of year 3 . The statistics department requires that students must complete their Prospectus (proposal of dissertation topic) before the end of year 3, which is earlier than The Graduate School deadline of the end of year 4. The prospectus must be approved by a faculty committee comprised of a committee chair and a minimum of 2 other faculty members. Students usually first find an adviser through independent studies who will then typically serve as the committee chair. When necessary, exceptions may be made upon the approval of the committee chair and the director of graduate studies, to extend the due date of the prospectus exam until the end of year 4.

• Successfully complete and defend a doctoral dissertation. After the prospectus is approved, students begin work on the doctoral dissertation, which must demonstrate an original contribution to a chosen area of specialization. A final examination (thesis defense) is given based on the dissertation. Students typically complete the PhD program in 5 years.
• Attend all seminars in the department and participate in other research activities . In addition to these academic requirements, students are expected to participate in other research activities and attend all department seminars every year they are in the program.

Optional MS degree en route to PhD

Students admitted to the Statistics and Data Science PhD program can obtain an optional MS (Master of Science) degree en route to their PhD. The MS degree requires 12 courses: STAT 350-0 Regression Analysis, STAT 353 Advanced Regression, STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3, STAT 415-0 I ntroduction to Machine Learning , and at least 6 more courses approved by the department of which two must be 400 level STAT elective courses, no more than 3 can be non-STAT courses. For the optional MS degree, students must also pass the qualifying exam offered at the beginning of the second year at the MS level.

*Prior to 2021-2022, the course requirements for the PhD were:

• STAT 351-0 Design and Analysis of Experiments
• STAT 425 Sampling Theory and Applications
• MATH 450-1,2 Probability 1, 2 or MATH 450-1 Probability 1 and IEMS 460-1,2 Stochastic Processes 1, 2
• Six additional 300/400 graduate-level Statistics courses, at least two must be 400 -level

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PhD Program

Requirements for doctor of philosophy (ph.d.) in data science.

The goal of the doctoral program is to create leaders in the field of Data Science who will lay the foundation and expand the boundaries of knowledge in the field. The doctoral program aims to provide a research-oriented education to students, teaching them knowledge, skills and awareness required to perform data driven research, and enabling them to, using this shared background, carry out research that expands the boundaries of knowledge in Data Science. The doctoral program spans from foundational aspects, including computational methods, machine learning, mathematical models and statistical analysis, to applications in data science.

Course Requirements

https://datascience.ucsd.edu/graduate/phd-program/phd-course-requirements/ 

Research Rotation Program

https://datascience.ucsd.edu/graduate/phd-program/research-rotation/

Preliminary Assessment Examination

The goal of the preliminary assessment examination is to assess students’ preparation for pursuing a PhD in data science, in terms of core knowledge and readiness for conducting research. The preliminary assessment is an advisory examination.

The preliminary assessment is an oral presentation that must be completed before the end of Spring quarter of the second academic year. Students must have a GPA of 3.0 or above to qualify for the assessment and have completed three of four core required courses . The student will choose a committee consisting of three members, one of which will be the HDSI academic advisor of the student. The other two committee members must be HDSI faculty members with  0% or more appointments; we encourage the student to select the second faculty member based on compatibility of research interests and topic of the presentation. The student is responsible for scheduling the meeting and making a room reservation. 

The student may choose to be evaluated based on (A) a scientific literature survey and data analysis or (B) based on a previous rotation project. The student will propose the topic of the presentation. 

• If the student chooses the survey theme, they should select a broad area that is well represented among HDSI faculty members, such as causal inference, responsible AI, optimization, etc. The student should survey at least 10 peer-reviewed conference or journal papers representative of the last (at least) 5 years of the field. The student should present a novel and rigorous original analysis using publicly available data from the surveyed literature: this analysis may aim to answer a related or new research question.
•  If the student chooses the rotation project theme, they should prepare to discuss the motivation for the project, the analysis undertaken, and the outcome of the rotation. 

For both themes, the student will describe their topic to the committee by writing a 1-2 page proposal that must be then approved by the committee. We emphasize that this is not a research proposal. The student will have 50 minutes to give an oral presentation which should include a comprehensive overview of previous work, motivation for the presented work or state-of-the-art studies, a critical assessment of previous work and of their own work, and a future outlook including logical next steps or unanswered questions. The presentation will then be followed by a Q&A session by the committee members; the entire exam is expected to finish within two hours. 

The committee will assess both the oral presentation as well as the student’s academic performance so far (especially in the required core courses). The committee will evaluate preparedness, technical skills, comprehension, critical thinking, and research readiness. Students who do not receive a satisfactory evaluation will receive a recommendation from the Graduate Program Committee regarding ways to remedy the lacking preparation or an opportunity to receive a terminal MS in Data Science degree provided the student can meet the degree requirements of the MS program . If the lack of preparation is course-based, the committee can require that additional course(s) be taken to pass the exam. If the lack of preparation is research-based, the committee can require an evaluation after another quarter of research with an HDSI faculty member; the faculty member will provide this evaluation. The preliminary assessment must be successfully completed no later than completion of two years (or sixth quarter enrollment) in the Ph.D. program. 

The oral presentation must be completed in-person. We recommend the following timeline so that students can plan their preliminary assessments:

• Middle of winter quarter of second year: Student selects committee and proposes preliminary exam topic.  
• Beginning of spring quarter of second year: Scheduling of exam is completed. 
• End of spring quarter of second year: Exam. 

Research Qualifying Examination and Advancing to Candidacy

A research qualifying examination (UQE) is conducted by the dissertation committee consisting of five or more members approved by the graduate division as per senate regulation 715(D). One senate faculty member must have a primary appointment in the department outside of HDSI. Faculty with 25% or less partial appointment in HDSI may be considered for meeting this requirement on an exceptional basis upon approval from the graduate division.

The goal of UQE is to assess the ability of the candidate to perform independent critical research as evidenced by a presentation and writing a technical report at the level of a peer-reviewed journal or conference publication. The examination is taken after the student and his or her adviser have identified a topic for the dissertation and an initial demonstration of feasible progress has been made. The candidate is expected to describe his or her accomplishments to date as well as future work. The research qualifying examination must be completed no later than fourth year or 12 quarters from the start of the degree program; the UQE is tantamount to the advancement to PhD candidacy exam.

A petition to the Graduate Committee is required for students who take UQE after the required 12 quarters deadline. Students who fail the research qualifying examination may file a petition to retake it; if the petition is approved, they will be allowed to retake it one (and only one) more time. Students who fail UQE may also petition to transition to a MS in Data Science track.

Dissertation Defense Examination and Thesis Requirements

Students must successfully complete a final dissertation defense oral presentation and examination to the Dissertation Committee consisting of five or more members approved by the graduate division as per senate regulation 715(D).  One senate faculty member in the Dissertation Committee must have a primary appointment in a department outside of HDSI. Partially appointed faculty in HDSI (at 25% or less) are acceptable in meeting this outside-department requirement as long as their main (lead) department is not HDSI.

A dissertation in the scope of Data Science is required of every candidate for the PhD degree. HDSI PhD program thesis requirements must meet Regulation 715(D) requirements. The final form of the dissertation document must comply with published guidelines by the Graduate Division.

The dissertation topic will be selected by the student, under the advice and guidance of Thesis Adviser and the Dissertation Committee. The dissertation must contain an original contribution of quality that would be acceptable for publication in the academic literature that either extends the theory or methodology of data science, or uses data science methods to solve a scientific problem in applied disciplines.

The entire dissertation committee will conduct a final oral examination, which will deal primarily with questions arising out of the relationship of the dissertation to the field of Data Science. The final examination will be conducted in two parts. The first part consists of a presentation by the candidate followed by a brief period of questions pertaining to the presentation; this part of the examination is open to the public. The second part of the examination will immediately follow the first part; this is a closed session between the student and the committee and will consist of a period of questioning by the committee members.

Special Requirements: Generalization, Reproducibility and Responsibility A candidate for doctoral degree in data science is expected to demonstrate evidence of generalization skills as well as evidence of reproducibility in research results. Evidence of generalization skills may be in the form of — but not limited to — generalization of results arrived at across domains, or across applications within a domain, generalization of applicability of method(s) proposed, or generalization of thesis conclusions rooted in formal or mathematical proof or quantitative reasoning supported by robust statistical measures. Reproducibility requirement may be satisfied by additional supplementary material consisting of code and data repository. The dissertation will also be reviewed for responsible use of data.

Special Requirements: Professional Training and Communications

All graduate students in the doctoral program are required to complete at least one quarter of experience in the classroom as teaching assistants regardless of their eventual career goals. Effective communications and ability to explain deep technical subjects is considered a key measure of a well-rounded doctoral education. Thus, Ph.D. students are also required to take a 1-unit DSC 295 (Academia Survival Skills) course for a Satisfactory grade.

Obtaining an MS in Data Science

PhD students may obtain an MS Degree in Data Science along the way or a terminal MS degree, provided they complete the requirements for the MS degree.

Doctor of Philosophy in Data Science

Developing future pioneers in data science

The School of Data Science at the University of Virginia is committed to educating the next generation of data science leaders. The Ph.D. in Data Science is designed to impart the skills and knowledge necessary to enable research and discovery in data science methods. Because the end goal is to extract knowledge and enable discovery from complex data, the program also boasts robust applied training that is geared toward interdisciplinary collaboration. Doctoral candidates will master the computational and mathematical foundations of data science, and develop competencies in data engineering, software development, data policy and ethics. 

Doctoral students in our program apprentice with faculty and pursue advanced research in an interdisciplinary, collaborative environment that is often focused on scientific discovery via data science methods. By serving as teaching assistants for the School’s undergraduate and graduate programs, they learn to be adroit educators and hone their critical thinking and communication skills.

LEARNING OUTCOMES

Pursuing a Ph.D. in Data Science will prepare you to become an expert in the field and work at the cutting edge of a new discipline. According to LinkedIn’s most recent Emerging Jobs Report, data science is booming and data scientist is one of the top three fastest growing jobs. A Ph.D. in Data Science from the University of Virginia opens career paths in academia, industry or government. Graduates of our program will:

• Understand data as a generic concept, and how data encodes and captures information
• Be fluent in modern data engineering techniques, and work with complex and large data sets
• Recognize ethical and legal issues relevant to data analytics and their impact on society 
• Develop innovative computational algorithms and novel statistical methods that transform data into knowledge
• Collaborate with research teams from a wide array of scientific fields 
• Effectively communicate methods and results to a variety of audiences and stakeholders
• Recognize the broad applicability of data science methods and models 

Graduates of the Ph.D. in Data Science will have contributed novel methodological research to the field of data science, demonstrated their work has impactful interdisciplinary applications and defended their methods in an open forum.

A Week in the Life: First-Year Ph.D. Student

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• Doing a PhD in Data Science

What Is a PhD in Data Science?

If you have always been fascinated by science, especially if you are interested in statistics and the scientific method, then a PhD in Data Science might be for you.

Data science is a field of study dedicated to applying the science of statistics to the problem areas of data visualisation, data science and machine learning. In this field, the challenge is to use data analysis and mathematical formulas to predict data patterns and draw conclusions from them.

Data science has become popular because it covers a wide range of topics, including the use of statistical methods for analysing and interpreting data. The primary goal of the discipline is to explain the way data enters the scientific community and influences decisions. Data is analysed to find patterns and connections, and then possible solutions are explored. With big data and new statistical computing methods, patterns can be uncovered, and relationships can be tested.

As more and more industries rely on information generated by computers, data science will be one of the key players in the future.

Browse PhDs in Data Science

Application of artificial intelligence to multiphysics problems in materials design, study of the human-vehicle interactions by a high-end dynamic driving simulator, physical layer algorithm design in 6g non-terrestrial communications, machine learning for autonomous robot exploration, detecting subtle but clinically significant cognitive change in an ageing population, what does a phd in data science focus on.

The primary focus for a PhD in Data Science is statistical methods. This means that you would study statistics in all its forms at the macroscopic and microscopic level, including statistical computer science, theory and applied mathematics. The advantage is that you get an insight into how large-scale data works. Thus, a position in a company where you are analysing large amounts of project data can be made available through a PhD.

PhD programs in data science provide university students with a thorough grounding in the theoretical aspects , but they are also taught the practical aspects of the discipline. PhD students are taught how to conduct proper experiments and interpret the results of scientific studies.

The importance of data and its interpretation is of paramount importance in all fields, and a PhD programme in data science addresses this topic, with some institutions also offering taught modules that doctoral students can use to deepen their knowledge.

Within a data science field, there are several areas of focus. One of them is the analysis of large databases and their effective interpretation. With this doctoral qualification, you could conduct statistical analysis, research studies and even exploratory data analysis. You could see what kinds of relationships exist between variables. You can explore areas such as Databases, Human Resource Management Machine Learning, or Information Technology during your studies.

Entry Requirements for A PhD in Data Science

A PhD in Data Science involves conducting original research in this area; therefore, applicants must have a good knowledge of statistical methods, computing, probability calculation, statistics and other related topics.

Basic requirements are typically a strong Master’s degree in mathematics, computer science or statistics from an accredited university. International students will also need to meet several minimum English language requirements set by the university, usually as part of a TOEFL or IELTS exam.

Although there are many advantages to obtaining a PhD in Data Science, it requires hard work and perseverance to master the techniques of analysis; to become an effective researcher, you will need strong mathematical and logical skills.

If you are interested in a PhD in Data Science but are unsure whether you have the background or resources available, consider taking a Master’s degree in this subject, or if you are a prospective student, contact the department you are interested in to see if they have any advice for you.

Duration and Programme Types

You can earn a PhD in data science in as little as 3 years full-time or 6 years part-time at a leading university. There are also online courses; many universities offer online PhD programmes which allow you to complete your entire doctoral programme from home. You still need to meet your course requirements by attending lectures and doing laboratory work, but your work can be completed at your own pace and off-campus.

Costs and Funding

The cost of a PhD in Data Science will depend on the university you study with, but average tuition fee is £4000-£6000 per academic year for UK/EU students and £16,000-£19,000 per academic year for international students.

Due to the popularity of Data Science PhD projects and the increasing demand for individuals who can elaborately analyse large data sets , it is not difficult to obtain PhD funding in this area. In many cases, funding for full-time research can be obtained from the university’s Centre for Doctoral Training (CDT), covering tuition fees and living costs.

Available Career Paths

A PhD in Data Science will enhance your data analysis skills and allow you to specialise in areas not available to others. A PhD offers many opportunities for those interested in statistics; you could become an engineer, statistician, consultant or academic lecturer. There are even PhDs in Data Science that offer internships in financial institutions or government agencies. Upon completing your doctorate, you can enter the workforce in many areas depending on your aptitude and experience.

A PhD in Data Science can lead to a wide range of jobs in many fields. If you are interested in working for a company that uses data one way or another, a PhD would be the perfect choice for you. If you are interested in independent research and studying various scientific methods and data, you will do well with a PhD. You could also spend your time teaching or doing your own research.

A person who has a PhD in data science can work in many industry-related positions. For example, you may work in the financial industry as an analyst for mergers and acquisitions, in healthcare, as a statistician, or as an information systems administrator. You can even get a job as an IT analyst, project manager, and software designer.

You can use your knowledge in the workplace to start up your own small business. Many small businesses today are founded on the back of a PhD. In fact, many Fortune 500 companies started as a result of a doctor trying to solve a problem or answer a long-standing question plaguing their industry.

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Ph.D. Specialization in Data Science

The ph.d. specialization in data science is an option within the applied mathematics, computer science, electrical engineering, industrial engineering and operations research, and statistics departments..

Only students already enrolled in one of these doctoral programs at Columbia are eligible to participate in this specialization. Students should fulfill the requirements below in addition to those of their respective department's Ph.D. program. Students should discuss this specialization option with their Ph.D. advisor and their department's director for graduate studies.

Applied Mathematics Doctoral Program

Computer Science Doctoral Program

Decision, Risk, and Operations (DRO) Program

Electrical Engineering Doctoral Program

Industrial Engineering and Operations Research Doctoral Program

Statistics Doctoral Program

The specialization consists of either five (5) courses from the lists below, or four (4) courses plus one (1) additional course approved by the curriculum committee. All courses must be taken for a letter grade and students must pass with a B+ or above. At least three (3) of the courses should come from outside the student’s home department. At least one (1) course has to come from each of the three (3) thematic areas listed below.

Specialization Requirements

• COMS 4231 Analysis of Algorithms I
• COMS 6232 Analysis of Algorithms II
• COMS 4111 Introduction to Databases
• COMS 4113 Distributed Systems Fundamentals
• EECS 6720 Bayesian Models for Machine Learning
• COMS 4771 Machine Learning
• COMS 4772 Advanced Machine Learning
• IEOR E6613 Optimization I
• IEOR E6614 Optimization II
• IEOR E6711 Stochastic Modeling I
• EEOR E6616 Convex Optimization
• STAT 6301 Probability Theory I
• STAT 6201 Theoretical Statistics I
• STAT 6101 Applied Statistics I
• STAT 6104 Computational Statistics
• STAT 5224 Bayesian Statistics
• STCS 6701 Foundations of Graphical Models (joint with Computer Science) 

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Ph.d. specialization committee.

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• Faculty of Arts and Sciences Professor of Statistics
• The Fu Foundation School of Engineering and Applied Science Professor of Computer Science

Richard A. Davis

• Faculty of Arts and Sciences Howard Levene Professor of Statistics

Vineet Goyal

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Industrial Engineering and Operations Research

Garud N. Iyengar

• The Fu Foundation School of Engineering and Applied Science Vice Dean of Research
• Tang Family Professor of Industrial Engineering and Operations Research

Gail Kaiser

Rocco a. servedio, clifford stein.

• Data Science Institute Interim Director
• The Fu Foundation School of Engineering and Applied Science Wai T. Chang Professor of Industrial Engineering and Operations Research and Professor of Computer Science

John Wright

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Electrical Engineering
• Data Science Institute Associate Director for Academic Affairs

NYU Center for Data Science

Harnessing Data’s Potential for the World

PhD in Data Science

An NRT-sponsored program in Data Science

• Areas & Faculty
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• NRT FUTURE Program

Degree Requirements

Degree requirements for the PhD in Data Science can be found in the NYU bulletin –  Doctor of Philosophy in Data Science .

To be awarded the Ph.D. in Data Science, students must, within 10 years of first enrolling:

• Complete 72 credit hours while maintaining a cumulative grade point average of 3.0 (out of 4.0) each semester.
• Complete the teaching requirement  (for incoming students Fall 2020 and later) .
• Pass a Comprehensive Exam.
• Pass the Depth Qualifying Exam (DQE) by May 15 of their fourth semester.
• Complete all the steps for approval of their Ph.D. dissertation.

For more information on the Ph.D.  curriculum and requirements please visit the Ph.D. Student Handbook . Please note you will only be able to access the handbook through your NYU email address.

Required Course Information

Students must successfully complete the following courses by the end of their third semester unless otherwise stated or show evidence that they have taken equivalent coursework elsewhere. Recent course pages are linked below. Course descriptions can be found in NYU’s  Albert Course Search .

• DS-GA 2003 – Introduction to Data Science for PhD Students
• DS- GA 1002 – Probability and Statistics for Data Science
• DS-GA 1003 – Machine Learning
• DS-GA 1004 – Big Data
• DS-GA 1005 – Inference and Representation
• A research rotation is a semester-long guided research experience in which the student will have an opportunity to design and carry out original research in a collaborative setting. The idea is to help students identify research interests. Ph.D. students take this course 6 times.

39 credit hours of elective courses  (for incoming students starting Fall 2020 and later)

Students must successfully complete 39 credit hours of elective courses. Faculty at the Center for Data Science are experts in a broad range of data science topics, and the Center’s course offerings reflect that diversity. For example, students will be able to take courses in Deep Learning, Optimization, and Natural Language Processing.

Some of the electives offered at the Center for Data Science are below. Please see NYU’s  Albert Course Search  for course descriptions.

• Deep Learning (DS-GA 1008)
• Practical Training for Data Science (DS-GA 1009):  Practical Training offers course credit for the academically relevant internship experience. This is an integral part of the Ph.D. Program curriculum and facilitates students with academic and professional development. The course allows students to apply their academic and research knowledge to real-world problems.
• Independent Study (DS-GA 1010)
• Natural Language Processing with Representation Learning (DS-GA 1011)
• Natural Language Understanding and Computational Semantics (DS-GA 1012)
• Mathematical Tools for Data Science (DS-GA 1013)
• Optimization and Computational Linear Algebra (DS-GA 1014)
• Text as Data (DS-GA 1015)
• Computational Cognitive Modeling (DS-GA 1016)
• Responsible Data Science (DS-GA 1017)
• Probabilistic Time Series Analysis (DS-GA 1018)
• Communication Skills (DS-GA 2002)

Students can take electives outside of the Center of Data Science with permission from the Director of Graduate Studies (DGS).

Typical Schedule (Incoming Students Fall 2020 and later)

Typically, a student’s first 3 years will follow a schedule like the one outlined below. The student’s remaining years will consist of electives and work on his or her research and dissertation.

• DS-GA 2003 Introduction to Data Science for PhD Students
• DS-GA 1002 Probability and Statistics for Data Science
• DS-GA-2001 Research Rotation
• DS-GA 1003 Machine Learning
• DS-GA 1004 Big Data
• DS-GA 2001 Research Rotation
• DS-GA 1005 Inference and Representation
• Approved elective
• Approved Elective

Teaching Requirement  (for incoming students starting Fall 2020 and later)

By the end of the fourth year of study, each student must have served as a section leader or instructor for at least two courses at the Center for Data Science (for students starting the program in Fall 2023 or later). For students who started the program between Fall 2020 – Fall 2022, the requirement is at least one course at the Center for Data Science.

Courses on related topics outside the Center may also be used to satisfy this requirement subject to approval by the DGS. The student must also participate in the Center’s teacher training session at or prior to the semester in which they teach. In certain circumstances, the DGS may allow the student to satisfy this requirement by serving as a course assistant or as a grader.  These exceptions will be determined by the DGS based on the availability of suitable recitations.

Comprehensive Exam

The comprehensive exam is designed to determine whether the candidate displays the requisite data science knowledge to pursue their research.

For students starting the program in Fall 2024 and later: To fulfill this requirement, students will submit a 4-page report describing their work during their first year and a plan of their future research at the end of their second semester. The student will also give a 10-minute presentation in front of a pre-committee of three faculty (which will include their research advisors). The committee will determine whether the student is progressing adequately based on their academic performance (including grades and feedback from course instructors), the presentation, and the report.

For students who started the program prior to Fall 2024: The comprehensive exam consists of material from DS-GA 1003 Machine Learning and DS-GA 1004 Big Data. To fulfill this requirement, students must receive an A- or above as their final grade for each of the courses above  (for students starting Fall 2020 – Fall 2023) . Students are expected to complete this requirement by the end of their second semester.

Depth Qualifying Exam (DQE)

No later than the end of the third semester, each student must:

• Agree with a research advisor. The student is responsible for finding a research advisor, obtaining an agreement to advise the student, and informing the Director of Graduate Studies (DGS) of the agreement. Students must reach an agreement with the DGS and the Manager of Academic Affairs if they wish to change research advisors. If a research advisor determines that he or she no longer wishes to advise a student, the research advisor informs the DGS who will begin working with the student to find another research advisor.
• Agree with his or her research advisor on a research project, an exam topic, and a Depth Qualifying Exam (DQE) committee.
• Obtain the approval of the DGS on the research project, exam topic, and DQE committee, as well as the date of the DQE exam.

No later than the end of his fourth semester, the student must pass the depth qualifying exam (DQE). The exam may be taken no more than twice. The content of the exam is defined by the student’s DQE Committee, which must present a syllabus to the student at least 2 months before the date of the exam.

For incoming students Fall 2020 and later, the exam itself consists of a presentation by the student on original research carried out independently or in collaboration with faculty, research staff, or other students. This can include research done in the research rotations or other research conducted by the student in their area of interest. The goal of the DQE is to confirm the student’s knowledge of research in their area of interest.

Ph.D. Dissertation

Dissertation proposal approval.

CDS PhD students are encouraged to identify their dissertation proposal committee by the end of their second year. Students should consult with their advisor and/or the DGS. The student works with their research advisor to select a dissertation proposal approval committee, obtains approval of this committee from the DGS, submits a written dissertation proposal to the committee, and obtains the approval of the committee. The committee consists of at least three members, which may consist of individuals with similar standing outside of CDS. At least one member must be a CDS faculty member (CDS joint faculty member, member of the CDS PhD Advisory Group, or CDS affiliated (see the Areas & Faculty page ). Students should have their dissertation proposal approved no later than the end of their third year. However, this is a guideline. Students are encouraged to identify timing of the dissertation proposal in consultation with their advisor and/or the DGS.

DISSERTATION APPROVAL

A successful defense is required for award of the PhD. 

The PhD defense committee must have at least 5 members, including the advisor(s), three of whom must be CDS faculty (CDS joint faculty member, member of the CDS PhD Advisory Group, or CDS affiliated (see Areas & Faculty page ), and 1 external member (in related area from another NYU department or from an area institution, with approval from DGS). The membership of the defense committee is proposed by the student and approved by the DGS.

In addition, students must comply with all of the procedures of  NYU’s Graduate of School of Arts and Science related to the submission of their dissertation.

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Data Science PhD Program

Program details.

Become a thought leader in some of the hottest topics in business and science

A unique blueprint for aspiring data scientists

Given the rapid evolution of emerging A.I. concepts like machine learning and language processing, Stevens worked with industry to develop four curricular threads that ensure mastery of the most important principles in this discipline. These areas represent the greatest needs for tomorrow’s data scientist.

Machine learning and artificial intelligence. Explores statistical learning, A.I., machine learning and financial analytics.

Mathematical and statistical modeling. Covers multivariate analytics, financial time series and dynamic programming techniques.

Computational systems. Explores advanced algorithm design, distributed systems and cloud technologies.

Data management at scale. A deeper dive into data technologies, mobile systems and data management.

At Stevens, the interdisciplinary Ph.D. in Data Science prepares inquisitive students to become pioneers in this space through a rigorous curriculum emphasizing mathematical and statistical modeling, machine learning, computational systems and data management. The program is administered through both the Schaefer School of Engineering and Science and the School of Business at Stevens, ensuring a diverse curriculum that responds to demand for data scientists with extensive knowledge of the theories, techniques and applications associated with data and artificial intelligence. Graduates become research leaders in academia or industry, where they lead the organization’s forays through the data revolution and into the age of A.I. and machine learning.

Who should apply

The Ph.D. in Data Science is a full-time program offered on the Stevens campus in Hoboken, NJ. Applicants must have technical backgrounds — either a master’s degree in a field like computer science or business analytics, or relevant work experience. The program has a strong practical research component, so students will need the intellectual curiosity to do important research alongside Stevens faculty who are breaking new ground in theory and application of data science.

Admission requirements

Admission to the Ph.D. in Data Science is a highly competitive. Classes and research projects explore high-tech concepts in great depth, so only technically oriented students with the highest academic credentials will be admitted.

A list of Stevens admissions criteria  is available at Graduate Admissions. Some specialized requirements for entry into this program include: 

An excellent GMAT or GRE score.

Prerequisite courses in calculus, statistics, probability, algebra and database management.

Fluency in at least one programming language, like C++ or Java.

For international students: An excellent TOEFL or IELTS score.

A master's program in a technical discipline is a requirement for the program, although outstanding candidates with bachelor's degrees will be considered, as well. At Stevens, degree programs like those in  Business Intelligence & Analytics ,  Computer Science ,  Financial Analytics ,  Financial Engineering ,  Biomedical Engineering  and  Chemical Biology  are excellent preparation for the Data Science doctoral program. 

Relevant work experience will be factored into an admissions decision, but is not a requirement for entry to this program.

Curriculum includes:

mathematical and statistical modeling

machine learning and artificial intelligence

data management at scale

computational systems

concentrations in either financial services or life sciences

> More info on curriculum

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Best Doctorates in Data Science: Top PhD Programs, Career Paths, and Salaries

If you are tech-savvy and like to stay up-to-date on the latest developments in the computing field, you might be interested in getting a data science job. The industry is interdisciplinary, with those working within it proficient in statistics, computer science, and operations research.

As such, this career isn’t pursued casually, as extensive education is required to become a data scientist and enter the industry. While some jobs can be obtained with a master’s degree, earning one of the best PhDs in Data Science is a much better option if you want the freedom to be able to conduct your own research.

Find your bootcamp match

Within your data science PhD studies, you will spend the latter two years of the four-year program doing your own unique research and writing a dissertation on your findings, preparing you to do the same in the real world. Those with a master’s degree, by contrast, don’t have as many creative liberties and don’t usually develop their own research, but rather analyze existing studies.

With the amount of schooling and skills required to work within the field, you might be wondering what a PhD in Data Science Salary looks like. Below, we’ll discuss the top schools for getting a PhD in Data Science, as well as the career outlook once you get your degree.

What Is a PhD in Data Science?

A PhD in Data Science is a four- or five-year, full-time degree pursued after a bachelor’s or master’s degree. Faculty in university PhD programs often like students to have a prior master’s degree, but if not, they might offer integrated master’s and PhD studies.

Within a PhD in Data Science program, you’ll spend the first two years of your program learning foundational knowledge, taking advanced courses in statistics, computer programming, data mining, research methodology, and so much more. The latter two years of the degree involve conducting your own unique research. You will then record your findings in a dissertation and defend your research before a committee to get your doctorate.

How to Get Into a Data Science PhD Program: Admission Requirements

You can get into a Data Science PhD program by meeting a university’s admission requirements, which will differ between each school. Typically, the minimum educational requirement is a bachelor’s degree in a related STEM degree, but most programs prefer a prior master’s degree.

If you don’t have a master’s degree, it is highly recommended to at least be proficient in a coding language and to have taken classes in calculus, statistics, and engineering. You will also need to have a minimum 3.0 GPA across your postsecondary studies and send the school your academic transcripts. There are also supplemental materials you would need for an application. These include a statement of purpose, two or three letters of recommendation, GRE scores, and a professional resume.

PhD in Data Science Admission Requirements

• A postsecondary degree in a related field
• Academic transcripts
• Graduate record examination (GRE) scores
• Coursework in data structures, algorithms, calculus, and linear algebra
• A background in a programming language
• Letters of recommendation
• Admission essays
• Personal statement

Data Science PhD Acceptance Rates: How Hard Is It to Get Into a PhD Program in Data Science?

It can be hard to get into a PhD program in Data Science. PhD programs within universities are very exclusive. While they receive a sea of applications, most schools only accept about a dozen of them. For example, Yale, one of the best schools in the country, had over 300 applicants but only made around 13 offers. As such, it is wise to apply for multiple PhD programs in order to increase your chances of getting an offer of admission.

How to Get Into the Best Universities

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Best PhDs in Data Science: In Brief

Best Universities for Data Science PhDs: Where to Get a PhD in Data Science

Since data science is a new field of scientific inquiry, it can be difficult to find the best universities for getting a data science PhD. In order to help you on your educational path, we’ve listed the 10 best schools for a data science PhD, below.

Arizona State University (ASU) was ranked the nation's most innovative university by US News & World Report. Originally founded in 1885, ASU has grown to now offer more than 160 programs at the graduate level in everything from art to engineering. The graduate school is well-known for its research work.

PhD in Data Science, Analytics, and Engineering

This program is geared toward those who want to work in either the data science industry, academia, or government to solve real-world problems through data-informed methods. The 12 credits of core courses within this program include data mining, statistics, security and assurance of information, and database management. 

If you want to focus on engineering, you’ll take nine credits across cloud computing, database systems, and databases for web and other multimedia. If you want to focus on analytics, you'll take Machine Learning Statistics, Regression Analytics, and Data Visualization. 

As a culmination of your studies, you’d produce a thesis. This requires you to propose a topic of study to the dissertation supervisory committee, and upon passing their comprehensive exam, you can begin your research. Ten days before you are to defend your dissertation, which must come less than a year after completing your 60th credit, you’ll submit a version of it to the committee for review.

PhD in Data Science, Analytics, and Engineering Overview

• Program Length: 4-6 years
• Acceptance Rate: 88% (school acceptance rate)
• Tuition: $11,720/year (in state); $23,544/year (out of state)
• PhD Funding Opportunities: Awards, fellowships, and scholarships

PhD in Data Science, Analytics, and Engineering Admission Requirements

• Application
• Application fee
• Official transcripts 
• Three letters of recommendation 
• Letter of intent  
• GRE scores 

Bowling Green State University (BGSU) offers more than 20 PhD programs in a variety of disciplines including engineering, psychology, business, and music. Since its beginnings in 1910, BGSU has been noted for its engineering and scientific research, being one of eight universities in the nation with the Carnegie Foundation’s Community Engagement Classification.

PhD in Data Science

This research-oriented program is interdisciplinary, incorporating teachings from applied statistics, operations research, and computer science. A unique aspect of BGSU’s program is that you’ll need to take ethics classes in order to understand the moral ramifications of gathering data and in communications to learn to effectively present their findings. Before beginning your 16- to 30-credit dissertation, you will need to pass the qualifying examination that involves oral and written sections. 

PhD in Data Science Overview

• Program Length: 4-5 years
• Acceptance Rate: 79% (school acceptance rate)
• Tuition and Fees: $523/credit (in state); $856/credit (out of state)
• PhD Funding Opportunities: Assistantships, scholarships, fellowship 
• Payment of $45 application fee
• Minimum GPA of 3.0
• Official or unofficial transcripts from previous institutions 
• Graduate Record Examination (GRE) scores
• Graduate Management Admission Test (GMAT) scores
• Three letters of recommendation
• Resume 

Chapman offers a variety of graduate programs, with 66 master’s and seven doctoral degrees in disciplines like business, law, education, and health sciences. It was founded in 1861 and is known for its research, with more than 31,000 citations from its 5,283 publications. Chapman University is also known for its strong alumni network, which can help graduates find jobs and networking opportunities.

PhD in Computational and Data Sciences

The PhD in computational and data sciences is designed for students who want to work in fields like population genetics, earth systems, biotechnology, bioinformatics, and economic science. The curriculum includes coursework in mathematical modeling, mining data, data analysis, and computational science, as well as research and thesis guidance from faculty. 

The program is structured so that students can specialize in an area of computational science that interests them, such as scientific computing, data science, or machine learning, allowing students to also choose their dissertation topic. Before becoming doctoral candidates, students take qualifying exams for their core curriculum and do presentations on their elective courses. 

PhD in Computational  Data Sciences Overview

• Acceptance Rate: 60% (school acceptance rate)
• Tuition and Fees: $32,400 tuition
• PhD Funding Opportunities: Assistantships, work-study, loans 

PhD in Computational and Data Sciences Admission Requirements

• Proof of satisfactory coursework in computer programming, differential equations, and statistics
• $60 application fee
• Two letters of recommendation 
• 750-word statement of interest

This circa 1889 school offers more than 130 programs at the graduate level, with 1,687 students currently taking on one of its 50-plus doctoral programs. The school has various innovation clusters of research types, including those related to environmental sustainability, innovations in health, data science and cyberinfrastructure, transportation, and advanced manufacturing. 

PhD in Biomedical Data Science and Informatics

This joint program through the college and the Medical University of South Carolina (MUSC) aims to teach students how to remedy issues in medicine through the combined study of information and computer sciences. Courses within the program will cover statistical theory, data management, machine learning, and bioinformatics. 

Students spend the first two years doing coursework, the third year completing professional development training and research electives, and the fourth year solely researching. Research, seminars, and lab rotations will consist of 24 credit hours. Before completion of the program you’ll need to take a qualifying exam alongside proposing, writing, and defending your dissertation. 

PhD in Biomedical Data Science and Informatics Overview

• Acceptance Rate: 49% (school acceptance rate)
• Tuition and Fees: $691/credit (in state); $1491/credit (out of state) 
• PhD Funding Opportunities: Assistantships, scholarships and fellowships

 PhD in Biomedical Data Science and Informatics Admission Requirements

• Bachelor’s degree in a STEM field, with one year of calculus and biology classes
• Graduate record examination (GRE) scores 
• Prior computer programming work experience or coursework
• Work or research experience (recommended) 
• Personal essay
• Two or three letters of recommendation 

This university provides more than 30 doctoral programs just within the College of Science. Something unique to the circa 1949 school—known for its research in physics, immunology, molecular medicine, and biodiversity—is that its staff encourages research teams to incorporate members across various disciplines, bringing the insight and strengths of those respective fields together.

PhD in Computational Sciences and Informatics

Throughout this 72-credit program in the Department of Computational and Data Sciences, you’ll choose two out of four core courses in statistical and scientific visualization, advanced computing, databases, or numerical methods, and then choose from a rotating list of emphasis courses. Emphasis classes might cover topics like knowledge mining, statistical inference, or Bayesian inference decision theory. 

By the end of your first year, you’ll need to obtain a research advisor, then get your proposal approved by the department committee to be considered a candidate for a PhD. A month before defending their dissertations, students conduct a pre-defense to get final revision recommendations. 

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PhD in Computational Sciences and Informatics Overview

• Acceptance Rate: 91% (school acceptance rate)
• Tuition and Fees: $12,594/year (in state); $33,906/year (out of state)
• PhD Funding Opportunities: Fellowships, assistantships, lecturer positions, faculty grants, scholarships, work-study 

PhD in Computational Sciences and Informatics Admission Requirements

• Mathematics background
• Knowledge of programming languages such as C, C++, and Python
• Personal statement 

Though Harrisburg only joined the Commonwealth of Pennsylvania in 2001, the private, not-for-profit university has grown to have an enrollment of 4,000 students from over 100 countries. While it only offers three PhD degree programs in data science, computational science, and information systems engineering, it is known for its impressive research in supercomputer datamining, aquaponics, and virtual reality. 

PhD in Data Sciences 

This program strives to teach PhD candidates diverse methods of data science and train them to be able to apply their analytical knowledge across disciplines beyond data science. The first two years of the program are Harrisbug's Analytics Master's Degree, and students apply for the actual PhD in the final semester of that program. 

If, however, students have a prior master’s degree from Harrisburg in computer science, they can transfer those credits and complete this four-to-five year doctoral degree in a shorter period. After completing the classwork portion of the degree, taking labs, seminars, classes, and doing fieldwork, they’ll begin their dissertation research. The defense of their dissertation will function as their final exam.

PhD in Data Sciences Overview

• Acceptance Rate: N/A
• Tuition and Fees : $800/credit hour (in state); $4,800/credit hour (out of state)
• PhD Funding Opportunities: Scholarships, grants, loans, work-study

PhD in Data Sciences Admission Requirements

• GRE/GMAT (strongly recommended)
• Essay on career goals
• Proof of research potential (courses or projects) 
• Minimum master’s degree GPA of 3.3
• A letter of intent 

The 1969-founded Indiana University-Purdue University Indianapolis (IUPUI) is an eponymous merger between the two schools and offers 550 programs across all levels. Of those, 57 are PhDs, covering everything from American studies and economics to addiction neuroscience and epidemiology. Some of their latest research breakthroughs were in the fields of informatics and computing, cardiology, nanosystems, and artificial intelligence. 

With data science being a field in its infancy, IUPU’s School of Informatics and Computing strives to have its graduates be leaders within this ever-evolving industry. Students will take classes in system analysis and design, monitoring social media, and data mining and visualization. 

PhD candidates can collaborate with professors on groundbreaking research in information infrastructures, Android science, computer security, machine learning, dataset integration, and computational social science. After earning this interdisciplinary degree, doctoral graduates will be ready to work in academia, health care, or even business intelligence. 

• Acceptance Rate: 84% (school acceptance rate)
• Tuition and Fees: $425/credit (in state) ; $1,350.00/credit (out of state)
• PhD Funding Opportunities: Faculty grants, work-study, loans, internal funding, foundation or corporate funding, funding agencies
• GPA of 3.5 or higher 
• GRE scores in the 70th percentile or higher for all sections
• Bachelor’s degree (master’s degree preferred)
• Completed classes in computer programming, statistics theory, linear algebra, and multivariable calculus
• Online application
• 500- to 750-word statement of purpose

Founded in 1966, Kennesaw provides more than 170 programs to its 40,000-plus students. Its 11 doctoral programs include studies in computer science, education, engineering, international diplomacy, business administration, and more. The core of the university's studies relates to technology and computing, medicine, human well-being and development, and sustainability. 

Doctoral Degree in Analytics and Data Science 

This interdisciplinary program combines business, math, stats, and computer science to make for well-rounded PhD candidates. Furthering that mission, the school also teaches written and oral communication skills to help graduates thrive in business or research fields. 

In the 78-credit program, students will take classes on machine learning, mining data, analyzing big data, and graph theory in their first year. This is followed by 21 credits of electives in their second year. Though students often participate in research projects during their first two years, the latter two of their programs will involve independently-led studies for their dissertation. 

Doctoral Degree in Analytics and Data Science Overview

• Acceptance Rate: 82% (school acceptance rate)
• Tuition and Fees: Qualified students are given a research stipend and waived tuition
• PhD Funding Opportunities: Foundations and institutes, corporate programs, scholarships, grants, loans, clearinghouses, coalitions, research stipends

Doctoral Degree in Analytics and Data Science Admission Requirements

• Master’s degree in computational-related discipline
• If no master’s degree, apply to the combined Master’s Degree in Applied Statistics or Computer Science program
• Strong proficiency in a programming language like Python
• Online application and $60 application fee
• Official transcripts from previous colleges or universities 
• The graduate record examination (GRE) scores 
• Statement of purpose 
• Completion of math courses through Calculus II
• SAS Certification (preferred) 

This 1874-founded school has 150 graduate programs, with 50-plus PhD programs in disciplines like medicine, physics, economics, and chemical physics. They have research programs in more than just pure and applied mathematics, as they also perform studies on wildfires, disinfectants, and autoregulation. 

PhD in Statistics and Data Science 

Prospective employees in academia, business, or government should consider this interdisciplinary research-based program in the Department of Mathematics and  Statistics in the College of Science. The 72 credit hours of this degree are broken up into 48 hours of classwork covering topics like linear models, statistical theory and computing, and quantitative methods, 30 of which should be at the 700-level. There are 24 dissertation credits and 24 master’s classes from a previously finished graduate degree. 

In order to continue within the PhD program after the third year, candidate hopefuls will need to pass a written qualifying test. After the qualifying test, students need to score highly on an oral exam in their chosen concentration before submitting and defending their dissertations.

PhD in Statistics and Data Science Overview

• Program Length: 4-6 years (8 years max)
• Acceptance Rate: 88% for overall school
• Tuition and Fees: $305.50/credit 
• PhD Funding Opportunities: Work-study, assistantships, scholarships, stipends, tuition waiver, subsidized medical plan

PhD in Statistics and Data Science Admission Requirements

• Online application 
• Bachelor’s and master’s degree transcripts
• Mathematics test scores (recommended)
• Financial aid application

Founded in 1701, Yale University is one of the oldest universities in the United States and is ranked fifth-best school in the nation by US News & World Report. Yale has 12 different professional schools and 73 different graduate degree programs. The university is especially well-known for its research in the humanities, environmental science, social sciences, and biotechnology. 

PhD program in Statistics and Data Science 

Students entering this degree program will focus on probability, statistics, information theory, data mining, machine learning, neural networks, and more as their foundational studies. After that, students take elective classes on one-off special topics classes that change between semesters. 

Those in the program need to take an oral and practical exam in their first year and begin their dissertation work in either their second or third year. This is usually a five-year program, with students getting a dissertation fellowship in their fifth year. Yale is a very exclusive school, and last year only made between 12 and 14 offers to the 300 applicants it received. As such, applying to other schools, in addition to Yale, would be a wise choice. 

PhD program in Statistics and Data Science Overview

• Program Length: 5 years
• Acceptance Rate: 5% (school acceptance rate)
• Tuition and Fees: $45,700/year (waived through provided fellowship)
• PhD Funding Opportunities : PhD students get a fellowship that covers all tuition through first five years in addition to an annual stipend of $36,000, Teaching fellowships, stipends, and health care benefits

PhD program in Statistics and Data Science Admission Requirements

• Graduate record examination (GRE) scores (optional)
• Strong mathematical background 
• Unofficial transcripts from previous colleges

Can You Get a PhD in Data Science Online?

Yes, you can get a PhD in Data Science online. There are a few fully-online PhD programs in data science provided by schools like Northcentral University. If you wish to pursue your PhD online but haven’t been accepted into a program for data science, you can consider a computer science program that has a concentration in data science. Since data science is a subset of computer science, you would learn the same foundational skills in either program.

Best Online PhD Programs in Data Science

How Long Does It Take to Get a PhD in Data Science?

It typically takes four to five years to complete a PhD in Data Science. While four years is the standard for most schools, some programs take a fifth year to complete due to the exhaustive research conducted. Most of the programs we’ve covered above require students to complete between 70 to 80 credits.

While that only requires between eight and 10 credits per semester, students’ schedules are filled with doing research, being a teacher’s assistant, and completing a fellowship. The amount of coursework required, the research component, and the dissertation are all factors that can affect the time it takes to earn a PhD in Data Science.

Is a PhD in Data Science Hard?

Yes, a PhD in Data Science is hard as it involves taking incredibly technical classes and conducting your own novel research within data science. The academic discipline is the merging of computer science, statistics, operations research, and more, meaning that successful students must be proficient in a wide range of technical skills.

How Much Does It Cost to Get a PhD in Data Science?

It costs, on average, $19,314 per year to get a PhD in Data Science , according to the National Center of Education Statistics. The cost will change depending on the type of school a student attends. If a doctoral student studies at a public university, the tuition is only $12,171, on average. By contrast, if a doctoral student studies at a private institution, tuition costs about $14,208 at for-profit universities, and $27,776 for nonprofit universities.

How to Pay for a PhD in Data Science: PhD Funding Options

There are many different avenues students can look into to pay for a PhD in Data Science. Some schools, such as Yale and Kennesaw State University, waive tuition for eligible students, and might even give students a yearly stipend. Another common option is to do an assistantship, in which you’d work within your data science department by teaching or doing research.

Students can also apply for various scholarships or grants to help cut down the cost of tuition. While scholarships for undergraduate students are typically merit-based, PhD funding is achieved through a student’s specific field, supporting their research and cutting tuition costs.

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What Is the Difference Between a Data Science Master’s Degree and PhD?

The difference between a data science master’s degree and PhD is that the former program only takes about two years to complete, while the latter is the educational step past a master’s degree that takes at least four years to complete. In fact, the first two years of a PhD program are usually a master’s degree program.

As such, some schools prefer applicants to have master’s degrees to cut down on the length of time. A master’s degree, and the first two years of a PhD program, are more so classroom-based. For PhD students, this is when students learn the foundations they’ll need to conduct their own research in the final two years of their program. 

Master’s vs PhD in Data Science Job Outlook

The job outlook for people with a Master’s or PhD in Data Science is very positive. Data science is a new scientific field, so workers within its industries are in high demand. For example, computer and information research scientists , which have a minimum requirement of a master’s degree, should see their careers grow by 22 percent between 2020 and 2030, according to the US Bureau of Labor Statistics (BLS).

Medical scientists , which have a minimum educational requirement of a PhD, should see job growth of 17 percent between 2020 and 2030. While The PhD job outlook is lower in this instance, a PhD is highly desirable, which is evident by the salary discrepancy below.

Difference in Salary for Data Science Master’s vs PhD

A PhD typically leads to a higher salary than a master’s degree. For example, the US Bureau of Labor Statistics reports that computer and information research scientists average $131,490 per year, while medical scientists in the scientific research and development services industry make $129,800. By contrast, those with a master’s degree tend to earn an average salary of $106,000 , according to PayScale.

While the BLS states that the minimum educational requirement for that job is a master’s degree, typically those with master’s degrees work in analyzing existing data. With a PhD, you can conduct research within this innovative field.

Related Data Science Degrees

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Why Should You Get a PhD in Data Science?

You should get a PhD in Data Science because you will be one of the pioneering leaders in this budding field. While a master’s program teaches students how to analyze data, a PhD program empowers students to do their own research. Read below for why you should get a PhD in Data Science.

Reasons for Getting a PhD in Data Science

• Lead the Field. Data science is a new field, so those that get a doctoral degree will be at the forefront of new developments. As you’ll be analyzing data, it’s incredibly exciting to think that your PhD research will be groundbreaking.
• Ever-evolving job: Technology is constantly advancing at incredible speeds, so being the one to learn about these advancements will never get old. With artificial intelligence technologies on the rise, one can only imagine in just 10 years’ time how different our understanding of data science will be.
• Specialize in interest. As students go along their educational paths, they go from learning foundational knowledge to increasingly specific information. Thus, if you’re passionate about a subset of data science but didn’t get to focus on it in your bachelor’s or master’s degree program, a PhD is the perfect opportunity to study, research, and work within your interests.
• High salaries. As the field of data science grows, the need for data science experts will also increase. PhD graduates will be uniquely equipped for the industry’s changing landscape and will be highly sought-after.
• Research opportunities. While this is an enriching hands-on experience, it lays the groundwork for you to be able to conduct your own studies in the latter two years of your program. You will be able to follow your passions rather than just helping a faculty member succeed in their work.
• Job Market. The BLS projects that job openings in computer and information research sciences will grow by 22 percent from 2020 to 2030. In getting a PhD, you will be a more competitive applicant than those with a lesser degree. It’s likely that you can even negotiate higher salaries because of your specialties.

Getting a PhD in Data Science: Data Science PhD Coursework

Data science is an interdisciplinary field, involving bioinformatics, computer science, statistics, and operations research. As such, the coursework PhD students undertake is diverse, including data mining, bioinformatics, ethics, and data visualization. Below, we’ll discuss some of the common classes found throughout most PhD programs in data science.

Introduction to Data Science

This class will teach you the baseline information you’ll need to know to advance in your data science career. Since you’re in an advanced degree program, you’ll likely be working with real data from case studies. You’ll take that information and learn how to build and manage databases, visualize data, and run statistical analyses.

Data Mining

Raw data, though important, isn’t useful until it can be contextualized and analyzed. Data mining is also called “knowledge discovery,” meaning that mining is the process of digging through mounds of data to learn information. Students will code, select and visualize data, use machine learning, and clean information to make novel findings.

Bioinformatics

As the combination of terms implies, bioinformatics is where biology and informatics meet and involves the study of biological data. This field of study is essential for those that want to go into medicine as data scientists. If you haven’t yet completed your bachelor’s degree, pursuing one of the best undergraduate degrees in bioinformatics is a wise choice.

Ethics of Data Science

Since data science involves collecting and storing information, mostly on people, there are possible moral ramifications to this. Within an ethics class, you’ll learn the proper methodology for conducting research to assure that your work meets codes of conduct.

Data Visualization

An important aspect of conducting research is being able to articulate your discoveries. Through visualization in programming languages like R, you’ll learn how to plot data and make reports. This process helps you organize your findings as well as snuff out any errors made during computation.

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How to Get a PhD in Data Science: Doctoral Program Requirements

You can get a PhD in Data Science by meeting your chosen university’s degree requirements. Though these can vary, there are commonalities across different schools, such as completing a set number of credits, taking exams, and crafting a dissertation. We’ll now go into more detail about these common components of the doctoral degree.

Data Science PhD programs typically require the completion of 70 and 80 credit hours. This is often split down the middle, with the first half of credits being done in a classroom, and the latter half being done through your research and dissertation. 

While full-time undergraduate students take 15 or so credits per semester, the number of PhD students is lower as they conduct work outside of classroom hours through assistantships. Candidates typically complete this degree studying full-time for four to five years, taking between eight and 10 credits per semester. Most schools have a cap on the maximum number of years a student has to complete their PhD. For example, University of Nevada’s maximum allowance is eight years.

Before entering a PhD program, students already have a bachelor’s or master’s degree in a relevant STEM discipline. With the basics in coding languages, a statistical method, calculus, and engineering out of the way, doctorate students can take a deep dive into more difficult and focused courses. Some examples of classes PhD students will take include machine learning, data visualization, and bioinformatics. 

Often students will be able to choose a specialization to narrow the focus of their research. This allows them to take more niche classes on topics like asymptotics, stochastic processes, and Bayes theorem. After completing two years of classwork, students then begin their dissertation studies.

In order to be considered candidates for a PhD, students will need to pass exams between the first and third year of their degree program. The tests, which often consist of a verbal exam and written assessment, determine what the candidate-hopefuls have learned so far and whether they will be effective researchers with the school’s department.

After completing two years of coursework and passing their qualifying exams, PhD candidates begin research for their eventual dissertations. Candidates collaborate with a chosen faculty member to help guide them in their approved topic of study. Students then write about their findings in a dissertation or thesis, which they will need to defend in front of a committee before being considered doctors. 

Potential Careers With an Data Science Degree

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PhD in Data Science Salary and Job Outlook

The salary and job outlook for those with a PhD in Data Science is very positive. The field of study is fairly new, and while there might not be many jobs that specifically require a PhD, having a terminal degree will make you a competitive applicant for prospective employers.

Data science PhD holders can work as medical scientists in the scientific research and development services industry or as information and computer research scientists. For the former industry, which requires a PhD, there will be a 17-percent increase in job openings between 2020 and 2030, per the US Bureau of Labor Statistics (BLS). The US BLS reports that computational and informational research sciences will see a 22-percent increase in job openings during that decade. Average salaries for those professionals are $116,430 and $131,490, respectively.

Though the salary for those in computational and informational research sciences is higher and typically only requires a master’s degree, those with a PhD are more likely to work in those positions. This is because PhD holders often conduct research, having done so in their doctoral programs, while former graduate students often analyze existing findings instead.

What Can You Do With a PhD in Data Science?

With a PhD in Data Science, there are a plethora of jobs within reach . The field of study is interdisciplinary, meaning that you’d be equipped with the skills to thrive in careers relating to computer science, bioinformatics, engineering, data management, and so much more. Let’s discuss further some of the highest-paying jobs that you can get with a PhD in Data Science.

Best Jobs with a PhD in Data Science

• Computer and Information Research Scientist
• Mathematicians/Statistician
• Medical Scientist
• Machine Learning Engineer
• Data Scientist

What Is the Average Salary for a PhD in Data Science?

The average salary for a PhD in Data Science is around $131,000. Payscale reports this is the average salary for those with a PhD in Computer Science, and since data science is a specialization of computer science, one can infer the salaries would be similar.

While $131,000 can be the expected salary for those with a Doctorate in Computer Science—and data science, by extension—the average salary you might earn will depend on a few variables. These include the amount of work experience you have, the industry you are working in, the organization you are working for, and the region of the country you are working in.

Highest-Paying Data Science Jobs for PhD Grads

Best Data Science Jobs with a Doctorate

The best data science jobs with a doctorate are as a computer and information research scientist, mathematician or statistician, medical scientist, machine learning engineer, or data scientist. All of the above careers earn over $100,000 per year, but the actual salary a job might offer can differ.

These professionals are found across health care, corporate, and scientific fields and work to optimize the computer systems for their organization. This is done through distilling overly-complicated algorithms, troubleshooting issues with other engineers, and conducting research into developing new electronic programs. 

Though these jobs usually have a minimum education requirement of a master’s degree, those with a PhD are likely to also populate this sector and will likely be given preference by employers. This is because those with a PhD conduct research more frequently than those with a master’s degree, who usually analyze existing data. 

• Salary with a Data Science PhD: $131,490
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Texas, Arizona, Washington, Massachusetts

While both statisticians and data scientists analyze sets of information, a difference between the two is that while the field of computational statistics can be broad, data science is more focused on computer science and machine learning. They do, however, use the same methodology for analysis, so with your PhD in Data Science, you’d be equipped to be a statistician in a variety of industries.

According to the US Bureau of Labor Statistics, statisticians and mathematicians that work in technical, professional, and scientific services make on average $129,800, and those that work in life, engineering, and physical science development and research earn $114,770 per year. 

• Salary with a Data Science PhD: $129,800
• Job Outlook: 33% job growth from 2020 to 2030
• Number of Jobs: 44,800
• Highest-Paying States: Connecticut, New York, Massachusetts, Wyoming, and California  

Working in what the BLS calls the “ scientific research and development services industry ,” you could use your data science know-how in the medical field, especially if you concentrated or did your dissertation in health care. You’d likely data mine through patient information, analyzing it to then make recommendations to those in your health system organization. 

• Salary with a Data Science PhD: $116,430
• Job Outlook: 17% job growth from 2020 to 2030
• Number of Jobs: 133,900
• Highest-Paying States: Maine, New Jersey, Tennessee, Connecticut, Delaware

Artificial intelligence is growing alongside the data science field. Pursuing this career would allow you to be able to help foster artificial intelligence (AI) programs. You’d code your own AI system, teaching it how to analyze large amounts of data and how the system should respond to it. 

• Salary with a Data Science PhD: $112,709
• Job Outlook: 22% job growth from 2020 to 2030 
• Number of Jobs: 33,000 (for computer and information research scientists) 
• Highest-Paying States: Oregon, Texas, Arizona, Washington, Massachusetts (for computer and information research scientists) 

Those that work within this field combine their knowledge of informatics, computer programming, data mining and management, and more to conduct research by studying data sets. Data Scientists can work in business, devising avenues to optimize profits by looking at reports.health care, presenting their studies to guide decisions bettering the medical system; and academia, working to innovate this budding field of study. 

• Salary with a Data Science PhD: $108,660
• Number of Jobs: 105,980
• Highest-Paying States: New Jersey, New York, Delaware, Washington, California

Is a PhD in Data Science Worth It?

Yes, a PhD in Data Science is much worth it. Though not all data science jobs require a PhD, with some upper-level careers only requiring a master’s degree, you would have an advantage over others with lower levels of education. You’d have experience conducting your own research method, which would prepare you for running your own studies in the real world.

Most with master’s degrees don’t actually develop their own studies, rather, they analyze existing information. A PhD would give you a competitive edge, making you a more impressive candidate to prospective employers. You’d be more likely to get hired, and more plausibly able to negotiate a higher salary.

Data science is a new field of inquiry, so by having a doctorate in it, you would be at the forefront of the technological advancements within the industry. You would likely make at least $100,000 yearly in data science and have the interdisciplinary skills to work in other industries if you desire.

Additional Reading About Data Science

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PhD in Data Science FAQ

Yes, many data scientists have PhDs, but it is not a requirement for many jobs in the industry. Some require only master’s degrees instead, but there are advantages to having a PhD. With a PhD, you’ll have conducted your own research to get your doctorate, allowing you to more easily create your own studies in the real world. Those with a master’s degree, by contrast, usually only analyze existing studies, giving you less creative liberties with what you work on.

There are many career options for those with a PhD in Data Science, as the course of study is interdisciplinary. This means that those with the degree are taught technical skills that can apply to multiple different industries. Often, those with a data science degree go on to work in either business, medicine, or academia.

The US Bureau of Labor Statistics (BLS) reports that the median annual salary for data scientists is $131,490 . This report states that those within this job often have a master’s degree, meaning that with your PhD, you’d be a more impressive candidate and be able to negotiate a higher salary. Additionally, the BLS estimates that demand for data scientists will grow by 22 percent from 2020 to 2030, a much higher rate than the national average of eight percent.

A PhD is the best degree to become a data scientist if you want to conduct your own studies. Many within data science have lesser degrees, usually a master’s and sometimes a bachelor’s, which in turn gives them less responsibility within the field. If you have a scientific inquiry that you are passionate about and want the freedom to study what you want, a PhD is the best degree to obtain.

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Table of Contents

What is a phd in data science, phd vs master's degree in data science, why earn a phd in data science, phd benefits, phd disadvantages, careers for data science phd holders, phd in data science curriculum, considerations when choosing a phd in data science program or college, phd in data science preparation courses, how much does a phd in data science cost, phd in data science: a guide to choose a doctoral program.

Throughout the world, data is revolutionizing and propelling enterprises. Nearly all industries require data scientists . To excel in data science, aspirants can consider getting a PhD in data science.

This article discusses everything one needs to know about PhD in data science.

A PhD in Data Science is the highest qualification for an aspiring data scientist. For anyone who wants to be at the highest point of their field, getting a PhD is the answer. It demonstrates to employers and fellow professionals that you are an expert in data science. This degree highly relies on research work.

Do you need a PhD in Data Science or a Master’s is enough?

• PhD in Data Science: A PhD in data science emphasizes on conducting deep research on a particular specialized topic. Normally, it takes four to five years to finish, though it often takes longer based on a variety of individual reasons.
• Master’s: Master's in data science program focuses a strong emphasis on giving students the practical skill sets they need to get ready for the workforce. Normally, it takes one to two years to finish.

A Doctorate in data science is perefct for anyone who wants to excel in their career as a data scientist. A PhD is your ideal match if you want to conduct research in specific specialization. Along with studying and researching, you could also work as an instructor in an institution.

These are the benefits of getting a PhD in Data Science:

• It will improve your data analysis abilities and allow you to specialize in areas where no one else can.
• It gets you ready for some of the most innovative research.
• It might aid in finding solutions to genuine issues.
• A data scientist with this degree is highly competent.

These are the drawbacks of getting a PhD in Data Science:

• If you cannot locate or own a means of funding it, it will be pricey.
• It involves reading and writing for long periods alone.
• Time-consuming is a possibility.

PhD programs in data science offer a wide range of professional options:

Data Scientist

• Research Scientist
• Data Engineer
• Data Analyst
• Database Administrator
• Machine Learning Engineer
• Data Architect
• Statistician
• Business Analyst

Become a Data Science & Business Analytics Professional

• 28% Annual Job Growth by 2026
• 11.5 M Expected New Data Science Jobs by 2026
• $86K - $157K Average Annual Salary

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• Earn a program completion certificate from Caltech CTME
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The basic curriculum of PhD in Data Science includes:

• Complete over 70 credits while keeping your cumulative grade point average at 3.0 each semester.
• For electives, complete almost 40 credits.
• Fulfill the prerequisites for teaching.
• Ace the comprehensive test.
• All requirements for the PhD dissertation's approval must be met.

When applying to practically any graduate school in data science, there are a few important factors to know.

• Admission Requirements: Generally, students should have at least a bachelor's degree; however, some schools require master's degree holders.
• Dissertation: The dissertation proposal must be approved by the faculty.
• Staff: PhD candidates should take into account the qualifications, standing, and diversity of the professors.
• Cost: For many students, choosing a school is heavily influenced by cost and financial aid.

Before writing a research proposal for your PhD program, considering a data science preparation course can be helpful.

• Simplilearn’s PGP in Data Science : Before continuing to the more complex concepts in data science, students who want to begin and complete the basic portion of the curriculum should choose this course.
• Praxis Business School’s PGP in Data Science: Via in-class lectures, assignments, and projects, the curriculum provides the students with skills in business, technology, and other fields.
• Data Science Specialization by John Hopkins University: Across the whole data science workflow, this program covers the ideas and technologies you'll need.
• IBM Data Science Course: The nine courses in this program will provide you with the most recent job-ready methods and skills in a wide range of fields of data science.

The cost of a Doctorate in data science will vary depending on the institution you attend. The average cost of a Doctorate is roughly $30,000. The cost of four years of schooling would be $120,000.

A Doctorate will put you in a good position to pursue this if you want to take part in data science workflow, which involves not only using libraries and ideas but also generating them. You can go for a data science boot camp if you don't want to do a PhD Simplilearn’s Data Science Bootcamp is the best boot camp for data science students.

1. Can You Get a PhD in Data Science Online?

Yes, there are many institutions that offer data science courses online. You need to research thoroughly before choosing the best PhD online program.

2. What Does it Cost to Get a PhD in Data Science?

A PhD in Data Science can be costly if you do not have the right funds. However, you can anticipate spending over $100,000 on this course.

3. Should you really get a PhD in Data Science?

For experts who have already made a name for themselves, a data science PhD may not be required. Yet, this is the greatest certification that can be earned, and it will boost your reputation in the field.

4. What is the length of a data science PhD?

PhD in data science generally takes up to five years.

5. What is the scope of a PhD in data science?

You may develop your career and become prepared for research in the area with a Doctorate in data science. But, your individual objectives and financial capacity will determine if you should pursue one.

6. Which PhD is best for data science?

There are various data science PhD specializations: Statistics, Machine Learning, Data Analytics, etc.

7. Is PhD in data science difficult?

A Doctorate can be challenging and frustrating. Writing up a Doctorate can require several months of diligent labor.

8. What is a good salary after PhD?

After earning a Doctorate, the average pay in data science might reach $141,400 annually according to Glassdoor. That could be increased to $227K or more with extra training.

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

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Data Science PhD Programs

A PhD in data science is a research-intensive degree that relies heavily on mathematics and computation to extract information from large data sets to make deductions or spot patterns and trends. 

Typically, a PhD in data science degree is interdisciplinary and is mainly offered as a part of a STEM program in computer science, engineering, mathematics, or statistics. A PhD in a specific data science-related area can be a good option for curious people who want to learn independently. 

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The field of data science and data science careers can be found in almost every industry, and the role of a data scientist continues to grow and evolve.

The actual job of a data scientist looks different for different organizations, and there is much more to it than the knowledge of software tools and the domain in which it is applied. 

To be successful as a data scientist , you need to have the right kind of data to solve your problem, the ability to understand business problems and the skills to apply the right kind of process to solve the problem. These skills get honed with practice and experience. 

Before we get into more details about getting a PhD in data science, one thing to know is that it is not mandatory to get a PhD to get most data science-related jobs.

Whether to get a PhD or not in data science depends on the kind of data science roles you are pursuing. Check out the master’s in data science guide for more details .

However, some roles are more research-oriented or need niche expertise, such as natural language processing (NLP), linguistics, speech recognition, etc.

At times, these roles might need you to create and develop algorithms from scratch. This requires research experience, and a PhD becomes relevant to the role of a data scientist. 

Through a PhD, individuals learn many skills to prepare for the commercial and academic world. This includes code, formulating questions, researching, creating technical documents, and solving problems. 

Trending Research Areas in Data Science

Insights into the research trends that have been going on in data science can be seen from the proceedings of well-regarded research conferences. For curious people, we have listed below some of the research areas in the field of data science and artificial intelligence trending today:

• Big Data on the Cloud
• Use of Augmented Analytics
• Focus on Edge Intelligence
• Automation of Data Cleaning
• Responsible AI
• Data-centric AI
• Increase in Use of Natural Language Processing
• Generative AI for Deepfake and Synthetic Data

Best PhD in Data Science Programs for 2024

Boise state university, capitol technology university, chapman university, clemson university, colorado technical university, florida atlantic university, george mason university, harrisburg university of science and technology, icahn school of medicine at mount sinai, indiana university.

These rankings were compiled from data accessed in December 2023 from Integrated Post-Secondary Education Data System (IPEDS) and College Navigator (both services National Center for Education Statistics). Tuition data was pulled from individual university websites and is current as of December 2023. If available, we also use additional criteria such as accreditation or designations by outside organizations or agencies.

Data Science PhD Program Overview and Curriculum

Here is a walkthrough of what a journey into a PhD looks like from beginning to end:

Admission Process

In general, admission requirements for most of the institutions include:

• Undergraduate and graduate transcripts
• GRE scores (may or may not be optional)
• TOEFL (English as a foreign language test, which may or may not be optional)
• A statement of intent for the program (reason for applying and plans)
• Letters of reference from undergrad professors or work supervisors if already working.
• Application fee (may be waived or reduced)
• Online application
• A curriculum vitae or resume (outlining all of your academic and professional accomplishments)

Post-Admission Process

• Every PhD program requires a student to complete a minimum number of credits to fulfill eligibility criteria. These credits can be a test of your knowledge, either at a foundational or advanced level. 
• Working on research projects over the first one or two years of the program will prepare you to frame the right questions, work on real-world data issues, and develop the necessary skill set required in the chosen data science-related topic.
• A qualifying exam is mandatory for every PhD program. These exams are designed to assess the candidate’s ability to meet the prerequisite standards/eligibility criteria. The assessment analyzes theoretical and practical understanding of the subject needed/required to work on your research project.
• Teaching undergraduate classes provides you with opportunities and experiences that will set you up for a future in academia.
• The dissertation proposal contains the hypothesis of your research that should meet the standards of publications in data analytics. The committee/faculty members need to approve the proposal before any proceedings to work on it.
• Students are expected to present their original work on the dissertation proposal. They are supposed to have expertise in their topic of dissertation. This is a crucial aspect of obtaining a doctoral degree in data science. It denotes that the student has mastered all of the necessary skills to undertake independent research that will contribute to the advancement of the field after completing their degree.
• Besides credits and qualifying exams, attending or presenting your research work at conferences, seminars, conventions, and summits provides you with an opportunity to network and form connections to boost your career. PhD can be fun as well as stressful. You can consider this degree as a marathon race that requires you to focus while testing your endurance for four to five years and finally providing you with the experience of a lifetime.

Online PhD Data Science Degree Programs

An online version of a PhD program allows individuals who have other work or family responsibilities to continue their education, albeit taking away some opportunities to network in person and in traditional ways. Students should verify the authenticity of online PhD programs by validating the institution’s accreditation before deciding to enroll. 

Traditionally, not many universities have offered completely online data science PhD programs. Mainly this reflects the hands-on nature of the degree that requires teaching, collaboration, and research. 

However, due to the COVID-19 pandemic, many traditional education programs have shifted courses and learning opportunities to digital platforms.

Because of technological advances in the EdTech industry, universities are becoming more comfortable hosting online classes and communication whenever needed. This might mean that some data science PhD work can be done online or remotely.

The best practice would be to contact the PhD programs you are interested in and inquire about online options. It is also a good idea to reach out to professors that would act as PhD advisors and be sure that they are willing and able to support long-term online learning and research.

Most universities provide funding for PhD students and programs in stipends, research, and teaching assistantships. If your preferred choice of university does not provide funding, it is a good idea to look into external financial assistance such as scholarships and fellowships.

Prior admission tuition fees must be considered when enrolling in a PhD program.

Career Paths and Outcomes

A PhD in data science may be offered from different departments at the university, such as statistics, computer science, mathematics, business, or even medical sciences.

Due to the interdisciplinary nature of data science PhD programs, career tracks, and research opportunities are numerous and diverse.

Academic Outcomes

• With a PhD, one can get hired in academia as a postdoctoral researcher or a fellow to advance their experience further. They can also begin their career as an associate professor in a university.

Industry Outcomes

• A data scientist ’s main focus or mission is to assist companies, organizations, and research efforts to resolve data-related problems. These problems range from user behavior to security risk factors and understanding consumer sentiment concerning the company’s products and services.
• A research scientist/quantitative researcher will be a part of the R&D team of the company or industry. A research scientist is responsible for developing hypotheses, conducting research, and building profitable business outcomes. A typical example would be handling a survey that identifies the latest trends and patterns of consumers’ lifestyle, income, and expenditures, then building/ improving a product or a service to generate profits.
• A chief data officer is the head of the organization’s data operations. A CDO brings their expertise to lead strategies and the ability to create models that use data to transform business strategies. A typical day as a CDO involves formulating data governance and management frameworks. Other tasks revolve around building data warehouses as a central repository for all information within a corporation.

Frequently Asked Questions

A doctoral degree is the training expected at many top universities for professors, researchers, and principal investigators in academia. A doctoral degree provides an edge where professional duties are research-oriented in corporate offices. According to Stitchdata, over 79 percent of data scientists who list their education data have earned a graduate degree. Of those, 38 percent have earned a PhD.

Regardless of how much funding the institution provides, it is advisable to always look for external financing and scholarships. The reason is that funding could change year-to-year at the institution or program level, so having a backup plan is always a good idea because doctoral studies usually take four to five years to complete.

An educational background in a quantitative field with a strong research interest is preferred by most schools. Since data science is interdisciplinary, universities may have prerequisites other than a background in mathematics or statistics.

Like other PhD programs, the GPA (which reflects previous academic experience) carries significant weight during the admissions process. However, since the PhD admissions process can vary greatly from program to program, it is good to check with the university about what factors factor into its admissions decisions.

PhD in Data Science Program List

Bowling green state university, indiana university-purdue university indianapolis, jackson state university, kennesaw state university, new jersey institute of technology, new york university, northcentral university, stevens institute of technology, university of massachusetts boston, university of nevada reno, university of tennessee-knoxville, university of vermont, washington university in st. louis, worcester polytechnic institute, yale university, related resources.

• PhD in Data Analytics Programs
• PhD Programs in Business Analytics
• How to Become a Data Scientist
• Find Your Data Science Certification
• Data Science and Data Scientist Jobs

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Data Science Principles

Are you prepared for our data-driven world.

Data Science Principles is a Harvard Online course that gives you an overview of data science with a code- and math-free introduction to prediction, causality, data wrangling, privacy, and ethics.

What You'll Learn

What is data science, and how can it help you make sense of the infinite data, metrics, and tools that are available today? 

Data science is at the core of any growing modern business, from health care to government to advertising and more. Insights gathered from data science collection and analysis practices have the potential to increase quality, effectiveness, and efficiency of work output in professional and personal situations. 

Data Science Principles makes the foundational topics in data science approachable and relevant by using real-world examples that prompt you to think critically about applying these understandings to your workplace. Get an overview of data science with a nearly code- and math-free introduction to prediction, causality, visualization, data wrangling, privacy, and ethics. 

Data Science Principles is an introduction to data science course for anyone who wants to positively impact outcomes and understand insights from their company’s data collection and analysis efforts. This online certificate course will prepare you to speak the language of data science and contribute to data-oriented discussions within your company and daily life. This is a course for beginners and managers to better understand what data science is and how to work with data scientists.

Data Science Principles is part of our Harvard on Digital Learning Path.

The Harvard on Digital course series provides the frameworks and methodologies to turn data into insight, technologies into strategy, and opportunities into value and responsibility to lead with data-driven decision making.

Explore More Courses in this Learning Path

The course is part of the Harvard on Digital Learning Path and will be delivered via  HBS Online’s course platform .  Learners will be immersed in real-world examples from experts at industry-leading organizations.  By the end of the course, participants will be able to:

• Understand the modern data science landscape and technical terminology for a data-driven world
• Recognize major concepts and tools in the field of data science and determine where they can be appropriately applied
• Appreciate the importance of curating, organizing, and wrangling data
• Explain uncertainty, causality, and data quality—and the ways they relate to each other
• Predict the consequences of data use and misuse and know when more data may be needed or when to change approaches

Your Instructor

Dustin Tingley  is a data scientist at Harvard University. He is Professor of Government and Deputy Vice Provost for Advances in Learning and helps to direct Harvard's education focused data science and technology team. Professor Tingley has helped a variety of organizations use the tools of data science and he has helped to develop machine learning algorithms and accompanying software for the social sciences. He has written on a variety of topics using data science techniques, including education, politics, and economics.

Real World Case Studies

Affiliations are listed for identification purposes only.

Mauricio Santillana

Listen to Harvard Professor and faculty member at Boston Children’s Hospital analyze Google Flu, its failures, and lessons learned.

Latanya Sweeney

Explore the difficulties faced in keeping data anonymous and private with Harvard Professor and Director of the Data Privacy Lab in IQSS at Harvard.

Dan Restuccia

Learn how Burning Glass Technologies uses text analysis to recommend job openings, skill development, and labor market trends.

Available Discounts and Benefits for Groups and Individuals

Experience Harvard Online by utilizing our wide variety of discount programs for individuals and groups. 

Past participant discounts.

Learners who have enrolled in at least one qualifying Harvard Online program hosted on the HBS Online platform are eligible to receive a 30% discount on this course, regardless of completion or certificate status in the first purchased program. Past Participant Discounts are automatically applied to the Program Fee upon time of payment.  Learn more here .

Learners who have earned a verified certificate for a HarvardX course hosted on the  edX platform  are eligible to receive a 30% discount on this course using a discount code. Discounts are not available after you've submitted payment, so if you think you are eligible for a discount on a registration, please check your email for a code or contact us .

Nonprofit, Government, Military, and Education Discounts

For this course we offer a 30% discount for learners who work in the nonprofit, government, military, or education fields. 

Eligibility is determined by a prospective learner’s email address, ending in .org, .gov, .mil, or .edu. Interested learners can apply below for the discount and, if eligible, will receive a promo code to enter when completing payment information to enroll in a Harvard Online program. Click here to apply for these discounts.

Gather your team to experience Data Science Principles and other Harvard Online courses to enjoy the benefits of learning together: 

• Single invoicing for groups of 10 or more
• Tiered discounts and pricing available with up to 50% off
• Growth reports on your team's progress
• Flexible course and partnership plans 

Learn more and enroll your team ! 

Who Will Benefit

Students and Recent Graduates

Prepare for your career by building a foundation of the essential concepts, vocabulary, skills, and intuition necessary for business.

Early- and Mid-Career Professionals

Recognize how data is changing industries and think critically about how to develop a data-driven mindset to prepare you for your next opportunity.

Marketing and Project Management Professionals

Learn how data science techniques can be essential to your industry and how to contribute to cross-functional, data-oriented discussions.

Learner Testimonials

"This is a topic that people in any industry should have at least basic knowledge of in order to create more efficient and competitive businesses, tools, and resources."

Carlos E. Sapene CEO, Chief Strategy Officer

"I found value in the real-world examples in Data Science Principles. With complicated topics and new terms, it's especially beneficial for learnings to be able to tie back new or abstract concepts to ideas that we understand. This course helped me understand data in this context and what algorithms are actually trying to solve."

Alejandro D. Financial Services Analyst

"Data Science Principles applies to many aspects of our daily lives. The course helps guide people in everyday life through decision making and process thinking."

Jared D. Senior Director of Sales

"The way this complicated topic was presented and the reflection it caused was impressive. I enjoyed the way I could dive into a whole new world of expertise in such an engaging way with all these various tools such as videos, peer discussions, polls, and quizzes."

Sonja Schwetje Managing Director/Editor-in-Chief, ntv

Data Science Principles makes the fundamental topics in data science approachable and relevant by using real-world examples and prompts learners to think critically about applying these new understandings to their own workplace. Get an overview of data science with a nearly code- and math-free introduction to prediction, causality, visualization, data wrangling, privacy, and ethics.

Download Full Syllabus

• Study a flu detection case study alongside Professor Dustin Tingley and Mauricio Santillana , Assistant Professor at Harvard’s T.H. Chan School of Public Health.
• Explain why data collection is important.
• Identify factors that may affect data quality.
• Recognize that not all data is numerical.
• Explain how the organization of data can affect the information you are able to extract from it.
• Study a predicting sepsis case alongside Craig Umscheid , Vice President and Chief Quality and Innovation Office, University of Chicago Medicine.
• Understand the basic structure of a predictive algorithm.
• Identify where human decisions shape predictive systems.
• Evaluate the success of a predictive system.  
• Study The Google Tax Case. 
• Explain why it is important to establish causal relationships.
• Identify barriers to establishing causal relationships in a variety of settings.
• Identify why randomization can help establish a causal relationship but also create other problems.  
• Explore a privacy and facial recognition case study with Latanya Sweeney , Professor of the Practice of Government and Technology at the Harvard Kennedy School and Sciences, director and founder of the Public Interest Tech Lab , and director and founder of the Data Privacy Lab .
• Explain why data privacy is important.
• Describe what can constitute a violation of privacy.
• Critique existing privacy policies.
• Create a set of ethical tenets to guide data work at their own organizations.  
• Study the Burning Glass and Text Data case.
• Identify sources of non-numerical data.
• Explain why it would be useful to use non-numerical data.
• Describe the differences in approach for supervised and unsupervised learning.
• Identify use cases for neural networks.  
• Explore a case study on reducing food waste with Shelf Engine.
• Describe some algorithms commonly used in data science.
• Understand basic workhorse algorithms in data science such as regression.
• Explain why and how such tools are made substantially more complex.
• Explain the crucial role humans have in overseeing and maintaining algorithms.
• Explain some of the trade-offs between more sophisticated algorithms, including the costs of running and evaluating their success.
• Learn about the Harvard Link case study.
• Explain the importance of data transformation and wrangling.
• List the common technologies used within data science ecosystems.
• Describe the connection between data science tasks, software tools, and hardware tools.
• Identify potential sources of bottlenecks in the data science process.  
• Work on a health care prioritization case study.
• Recognize a problem that an algorithm might be able to solve.
• Recognize the challenges created by using data science tools in ways outside their intended use.
• Identify steps within the data science process that need auditing.  

Earn Your Certificate

Enroll today in Harvard Online's Data Science Principles course.

Still Have Questions?

Are there discounts available for this course? What are the learning requirements? How do I list my certificate on my resume? Learn the answers to these and more in our FAQs.

Explore and connect to our courses via articles, webinars, and more.

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How can data science benefit your business decisions? By combining knowledge and analysis of data with business acumen, modern companies can become experts in data science execution.

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Watch a webinar about how to rethink your business strategy for data-driven decisions.

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We spoke with Professor Tingley to discuss his mixed career path, his upcoming book, and his data-driven outlook on the future of technology and our world.

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List of Best Research and Thesis Topic Ideas for Data Science in 2022

In an era driven by digital and technological transformation, businesses actively seek skilled and talented data science potentials capable of leveraging data insights to enhance business productivity and achieve organizational objectives. In keeping with an increasing demand for data science professionals, universities offer various data science and big data courses to prepare students for the tech industry. Research projects are a crucial part of these programs and a well- executed data science project can make your CV appear more robust and compelling. A  broad range of data science topics exist that offer exciting possibilities for research but choosing data science research topics can be a real challenge for students . After all, a good research project relies first and foremost on data analytics research topics that draw upon both mono-disciplinary and multi-disciplinary research to explore endless possibilities for real –world applications.

As one of the top-most masters and PhD online dissertation writing services , we are geared to assist students in the entire research process right from the initial conception to the final execution to ensure that you have a truly fulfilling and enriching research experience. These resources are also helpful for those students who are taking online classes .

By taking advantage of our best digital marketing research topics in data science you can be assured of producing an innovative research project that will impress your research professors and make a huge difference in attracting the right employers.

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Data science thesis topics

We have compiled a list of data science research topics for students studying data science that can be utilized in data science projects in 2022. our team of professional data experts have brought together master or MBA thesis topics in data science  that cater to core areas  driving the field of data science and big data that will relieve all your research anxieties and  provide a solid grounding for  an interesting research projects . The article will feature data science thesis ideas that can be immensely beneficial for students as they cover a broad research agenda for future data science . These ideas have been drawn from the 8 v’s of big data namely Volume, Value, Veracity, Visualization, Variety, Velocity, Viscosity, and Virility that provide interesting and challenging research areas for prospective researches  in their masters or PhD thesis . Overall, the general big data research topics can be divided into distinct categories to facilitate the research topic selection process.

• Security and privacy issues
• Cloud Computing Platforms for Big Data Adoption and Analytics
• Real-time data analytics for processing of image , video and text
• Modeling uncertainty

How “The Research Guardian” Can Help You A lot!

Our top thesis writing experts are available 24/7 to assist you the right university projects. Whether its critical literature reviews to complete your PhD. or Master Levels thesis.

DATA SCIENCE PHD RESEARCH TOPICS

The article will also guide students engaged in doctoral research by introducing them to an outstanding list of data science thesis topics that can lead to major real-time applications of big data analytics in your research projects.

• Intelligent traffic control ; Gathering and monitoring traffic information using CCTV images.
• Asymmetric protected storage methodology over multi-cloud service providers in Big data.
• Leveraging disseminated data over big data analytics environment.
• Internet of Things.
• Large-scale data system and anomaly detection.

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Now, we’ll proceed to cover specific research problems encompassing both data analytics research topics and big data thesis topics that have applications across multiple domains.

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Multi-modal Transfer Learning for Cross-Modal Information Retrieval

Aim and objectives.

The research aims to examine and explore the use of CMR approach in bringing about a flexible retrieval experience by combining data across different modalities to ensure abundant multimedia data.

• Develop methods to enable learning across different modalities in shared cross modal spaces comprising texts and images as well as consider the limitations of existing cross –modal retrieval algorithms.
• Investigate the presence and effects of bias in cross modal transfer learning and suggesting strategies for bias detection and mitigation.
• Develop a tool with query expansion and relevance feedback capabilities to facilitate search and retrieval of multi-modal data.
• Investigate the methods of multi modal learning and elaborate on the importance of multi-modal deep learning to provide a comprehensive learning experience.

The Role of Machine Learning in Facilitating the Implication of the Scientific Computing and Software Engineering

• Evaluate how machine learning leads to improvements in computational APA reference generator tools and thus aids in  the implementation of scientific computing
• Evaluating the effectiveness of machine learning in solving complex problems and improving the efficiency of scientific computing and software engineering processes.
• Assessing the potential benefits and challenges of using machine learning in these fields, including factors such as cost, accuracy, and scalability.
• Examining the ethical and social implications of using machine learning in scientific computing and software engineering, such as issues related to bias, transparency, and accountability.

Trustworthy AI

The research aims to explore the crucial role of data science in advancing scientific goals and solving problems as well as the implications involved in use of AI systems especially with respect to ethical concerns.

• Investigate the value of digital infrastructures  available through open data   in  aiding sharing  and inter linking of data for enhanced global collaborative research efforts
• Provide explanations of the outcomes of a machine learning model  for a meaningful interpretation to build trust among users about the reliability and authenticity of data
• Investigate how formal models can be used to verify and establish the efficacy of the results derived from probabilistic model.
• Review the concept of Trustworthy computing as a relevant framework for addressing the ethical concerns associated with AI systems.

The Implementation of Data Science and their impact on the management environment and sustainability

The aim of the research is to demonstrate how data science and analytics can be leveraged in achieving sustainable development.

• To examine the implementation of data science using data-driven decision-making tools
• To evaluate the impact of modern information technology on management environment and sustainability.
• To examine the use of  data science in achieving more effective and efficient environment management
• Explore how data science and analytics can be used to achieve sustainability goals across three dimensions of economic, social and environmental.

Big data analytics in healthcare systems

The aim of the research is to examine the application of creating smart healthcare systems and   how it can   lead to more efficient, accessible and cost –effective health care.

• Identify the potential Areas or opportunities in big data to transform the healthcare system such as for diagnosis, treatment planning, or drug development.
• Assessing the potential benefits and challenges of using AI and deep learning in healthcare, including factors such as cost, efficiency, and accessibility
• Evaluating the effectiveness of AI and deep learning in improving patient outcomes, such as reducing morbidity and mortality rates, improving accuracy and speed of diagnoses, or reducing medical errors
• Examining the ethical and social implications of using AI and deep learning in healthcare, such as issues related to bias, privacy, and autonomy.

Large-Scale Data-Driven Financial Risk Assessment

The research aims to explore the possibility offered by big data in a consistent and real time assessment of financial risks.

• Investigate how the use of big data can help to identify and forecast risks that can harm a business.
• Categories the types of financial risks faced by companies.
• Describe the importance of financial risk management for companies in business terms.
• Train a machine learning model to classify transactions as fraudulent or genuine.

Scalable Architectures for Parallel Data Processing

Big data has exposed us to an ever –growing volume of data which cannot be handled through traditional data management and analysis systems. This has given rise to the use of scalable system architectures to efficiently process big data and exploit its true value. The research aims to analyses the current state of practice in scalable architectures and identify common patterns and techniques to design scalable architectures for parallel data processing.

• To design and implement a prototype scalable architecture for parallel data processing
• To evaluate the performance and scalability of the prototype architecture using benchmarks and real-world datasets
• To compare the prototype architecture with existing solutions and identify its strengths and weaknesses
• To evaluate the trade-offs and limitations of different scalable architectures for parallel data processing
• To provide recommendations for the use of the prototype architecture in different scenarios, such as batch processing, stream processing, and interactive querying

Robotic manipulation modelling

The aim of this research is to develop and validate a model-based control approach for robotic manipulation of small, precise objects.

• Develop a mathematical model of the robotic system that captures the dynamics of the manipulator and the grasped object.
• Design a control algorithm that uses the developed model to achieve stable and accurate grasping of the object.
• Test the proposed approach in simulation and validate the results through experiments with a physical robotic system.
• Evaluate the performance of the proposed approach in terms of stability, accuracy, and robustness to uncertainties and perturbations.
• Identify potential applications and areas for future work in the field of robotic manipulation for precision tasks.

Big data analytics and its impacts on marketing strategy

The aim of this research is to investigate the impact of big data analytics on marketing strategy and to identify best practices for leveraging this technology to inform decision-making.

• Review the literature on big data analytics and marketing strategy to identify key trends and challenges
• Conduct a case study analysis of companies that have successfully integrated big data analytics into their marketing strategies
• Identify the key factors that contribute to the effectiveness of big data analytics in marketing decision-making
• Develop a framework for integrating big data analytics into marketing strategy.
• Investigate the ethical implications of big data analytics in marketing and suggest best practices for responsible use of this technology.

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Platforms for large scale data computing: big data analysis and acceptance

To investigate the performance and scalability of different large-scale data computing platforms.

• To compare the features and capabilities of different platforms and determine which is most suitable for a given use case.
• To identify best practices for using these platforms, including considerations for data management, security, and cost.
• To explore the potential for integrating these platforms with other technologies and tools for data analysis and visualization.
• To develop case studies or practical examples of how these platforms have been used to solve real-world data analysis challenges.

Distributed data clustering

Distributed data clustering can be a useful approach for analyzing and understanding complex datasets, as it allows for the identification of patterns and relationships that may not be immediately apparent.

To develop and evaluate new algorithms for distributed data clustering that is efficient and scalable.

• To compare the performance and accuracy of different distributed data clustering algorithms on a variety of datasets.
• To investigate the impact of different parameters and settings on the performance of distributed data clustering algorithms.
• To explore the potential for integrating distributed data clustering with other machine learning and data analysis techniques.
• To apply distributed data clustering to real-world problems and evaluate its effectiveness.

Analyzing and predicting urbanization patterns using GIS and data mining techniques".

The aim of this project is to use GIS and data mining techniques to analyze and predict urbanization patterns in a specific region.

• To collect and process relevant data on urbanization patterns, including population density, land use, and infrastructure development, using GIS tools.
• To apply data mining techniques, such as clustering and regression analysis, to identify trends and patterns in the data.
• To use the results of the data analysis to develop a predictive model for urbanization patterns in the region.
• To present the results of the analysis and the predictive model in a clear and visually appealing way, using GIS maps and other visualization techniques.

Use of big data and IOT in the media industry

Big data and the Internet of Things (IoT) are emerging technologies that are transforming the way that information is collected, analyzed, and disseminated in the media sector. The aim of the research is to understand how big data and IoT re used to dictate information flow in the media industry

• Identifying the key ways in which big data and IoT are being used in the media sector, such as for content creation, audience engagement, or advertising.
• Analyzing the benefits and challenges of using big data and IoT in the media industry, including factors such as cost, efficiency, and effectiveness.
• Examining the ethical and social implications of using big data and IoT in the media sector, including issues such as privacy, security, and bias.
• Determining the potential impact of big data and IoT on the media landscape and the role of traditional media in an increasingly digital world.

Exigency computer systems for meteorology and disaster prevention

The research aims to explore the role of exigency computer systems to detect weather and other hazards for disaster prevention and response

• Identifying the key components and features of exigency computer systems for meteorology and disaster prevention, such as data sources, analytics tools, and communication channels.
• Evaluating the effectiveness of exigency computer systems in providing accurate and timely information about weather and other hazards.
• Assessing the impact of exigency computer systems on the ability of decision makers to prepare for and respond to disasters.
• Examining the challenges and limitations of using exigency computer systems, such as the need for reliable data sources, the complexity of the systems, or the potential for human error.

Network security and cryptography

Overall, the goal of research is to improve our understanding of how to protect communication and information in the digital age, and to develop practical solutions for addressing the complex and evolving security challenges faced by individuals, organizations, and societies.

• Developing new algorithms and protocols for securing communication over networks, such as for data confidentiality, data integrity, and authentication
• Investigating the security of existing cryptographic primitives, such as encryption and hashing algorithms, and identifying vulnerabilities that could be exploited by attackers.
• Evaluating the effectiveness of different network security technologies and protocols, such as firewalls, intrusion detection systems, and virtual private networks (VPNs), in protecting against different types of attacks.
• Exploring the use of cryptography in emerging areas, such as cloud computing, the Internet of Things (IoT), and blockchain, and identifying the unique security challenges and opportunities presented by these domains.
• Investigating the trade-offs between security and other factors, such as performance, usability, and cost, and developing strategies for balancing these conflicting priorities.

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Best Master’s in Data Science for 2024

It’s no secret that the need for data experts is growing due to the exponential amount of data being generated every day. One of the best ways to gain the in-demand skills to be able to harness, analyze, and create value from data is pursuing a master’s degree. This ranking was last updated February 2024.

UC Berkeley’s Master’s in Data Science — Online

Syracuse University MS in Applied Data Science Online

1. Harvard University

• ACCEPTANCE RATE, 2023-24
• AVERAGE UNDERGRADUATE GPA, 2023-24 ENROLLEES
• FALL TERM ENROLLMENT, 2022–23
• GRADUATION RATE, 2022-23
• NUMBER OF APPLICANTS IN 2023-24
• ONE-YEAR RETENTION RATE, 2022-23

2. University of North Texas

3. New York University

Earn Your Master’s in Data Science Online From SMU

4. University of Michigan–Ann Arbor

5. Carnegie Mellon University

6. University of California–Irvine

7. University of Rochester

8. Indiana University–Bloomington

Maryville University Master of Science in Data Science | Online

9. University of Arizona

10. University of Delaware

11. Appalachian State University

12. University of Minnesota

13. Oklahoma State University

14. University of Missouri

15. Georgia State University

16. Maryville University

17. University of Michigan–Dearborn

18. New York Institute of Technology

19. University of San Francisco

20. DePaul University

21. Marquette University

22. Willamette University

23. Rochester Institute of Technology

24. Texas Tech University

25. Worcester Polytechnic Institute

26. University of St. Thomas

27. American University

28. University of Maryland

29. CUNY Graduate Center

Frequently Asked Questions

Data science is one of the fastest growing fields—job openings are expected to grow by 35% by 2023, according to the U.S. Bureau of Labor Statistics . And students graduating with a master’s in data science often land six figure salaries. The reason it’s a fast growing field, with high paying jobs, is because companies across all industries want data-savvy professionals in this era of digitization. Data provides companies and organizations with the resources they need to make better decisions—and in turn, they need professionals with data science skills who know how to understand and analyze data. 

The GPA you’ll need to get accepted into a master’s program for data science varies by school. For all of the programs ranked by Fortune for 2024, the average undergraduate GPA for enrollees was 3.27. Students at Harvard and New York University had the highest GPA, with 3.87 and 3.75, respectively. Marquette University enrollees had the lowest reported GPA—at 3.01.

Master’s degree programs in data science can be offered in person, online or in a hybrid format—and that might be the difference in what the “best program” for you means. Fortune ranks the top five in-person programs for 2024 as: Harvard University, the University of North Texas, New York University, University of Michigan—Ann Arbor, and Carnegie Mellon University. Additionally, our ranking of the top five online programs in 2023 include: University of Southern California, UC—Berkeley, Bay Path University, New Jersey Institute of Technology, and Clemson University.

On average, it takes about one-and-a-half to two years to complete a master’s degree program in data science—with most programs requiring roughly anywhere from 25 to 60 credits to graduate. So it does depend on each individual program and whether you choose to be a full-time or part-time student. That said, thanks to a boost in salary and expanded career options, many students find it worthwhile to obtain a master’s degree in data science—and Gen Z considers the role of data scientist to be one of the most satisfying occupations .

A master’s degree in data science will teach you how to understand and analyze data. But because it’s a recently defined career path, how it’s applied can vary significantly. As Maurizio Porfiri, a New York University professor, told Fortune: “It’s a weird thing because it’s very vague. I discovered after a while that I had become a data scientist : people just started to refer to me as such.” But sometimes the first step to finding your place in the world of data science is picking a specialization—what type of problem you want to solve by using data. And a master’s degree can either help you find that specialization, or if you’ve already got the answer, will teach you the skills to pursue it.

Fortune compiled a list of seven universities that offer free online data science courses , which offers prospective students an opportunity to learn more about this field. Each university—Harvard University, the University of Michigan, UC Irvine, John Hopkins University, Columbia University, MIT, and Duke University—offers a different course, from linear regression to data science ethics to data science in real life. However, the common goal of these free courses is to give people an inside look into the field.

In 2022, data scientists earned median salaries of $103,500, according to the U.S. Bureau of Labor Statistics . But a degree from a top program might mean even more money; New York University’s (ranked third on Fortune’s best in-person data science programs) 2022-23 graduates with a master’s in data science earned an average salary of $143,000 four months after graduation, according to data provided by the university.

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Stanford Online

Databases: relational databases and sql.

SOE-YDATABASES0005

Stanford School of Engineering

This course is one of five self-paced courses on the topic of Databases, originating as one of Stanford's three inaugural massive open online courses released in the fall of 2011. The original "Databases" courses are now all available on edx.org. This course provides an introduction to relational databases and comprehensive coverage of SQL, the long-accepted standard query language for relational database systems. Databases: Advanced Topics in SQL and Databases: OLAP and Recursion are follow-on courses to this course and can be taken in either order. Advanced Topics is a broad and practical course covering indexes, transactions, constraints, triggers, views, and authorization, while OLAP and Recursion is recommended for learners with specific interest in these topics.

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