statistics for phd

Graduate Student Handbook (Coming Soon: New Graduate Student Handbook)

Phd program overview.

The PhD program prepares students for research careers in probability and statistics in academia and industry. Students admitted to the PhD program earn the MA and MPhil along the way. The first year of the program is spent on foundational courses in theoretical statistics, applied statistics, and probability. In the following years, students take advanced topics courses. Research toward the dissertation typically begins in the second year. Students also have opportunities to take part in a wide variety of projects involving applied probability or applications of statistics.

Students are expected to register continuously until they distribute and successfully defend their dissertation. Our core required and elective curricula in Statistics, Probability, and Machine Learning aim to provide our doctoral students with advanced learning that is both broad and focused. We expect our students to make Satisfactory Academic Progress in their advanced learning and research training by meeting the following program milestones through courseworks, independent research, and dissertation research:

By the end of year 1: passing the qualifying exams;

By the end of year 2: fulfilling all course requirements for the MA degree and finding a dissertation advisor;

By the end of year 3: passing the oral exam (dissertation prospectus) and fulfilling all requirements for the MPhil degree

By the end of year 5: distributing and defending the dissertation.

We believe in the Professional Development value of active participation in intellectual exchange and pedagogical practices for future statistical faculty and researchers. Students are required to serve as teaching assistants and present research during their training. In addition, each student is expected to attend seminars regularly and participate in Statistical Practicum activities before graduation.

We provide in the following sections a comprehensive collection of the PhD program requirements and milestones. Also included are policies that outline how these requirements will be enforced with ample flexibility. Questions on these requirements should be directed to ADAA Cindy Meekins at [email protected] and the DGS, Professor John Cunningham at [email protected] .

Applications for Admission

• Our students receive very solid training in all aspects of modern statistics. See Graduate Student Handbook for more information.
• Our students receive Fellowship and full financial support for the entire duration of their PhD. See more details here .
• Our students receive job offers from top academic and non-academic institutions .
• Our students can work with world-class faculty members from Statistics Department or the Data Science Institute .
• Our students have access to high-speed computer clusters for their ambitious, computationally demanding research.
• Our students benefit from a wide range of seminars, workshops, and Boot Camps organized by our department and the data science institute .
• Suggested Prerequisites: A student admitted to the PhD program normally has a background in linear algebra and real analysis, and has taken a few courses in statistics, probability, and programming. Students who are quantitatively trained or have substantial background/experience in other scientific disciplines are also encouraged to apply for admission.
• GRE requirement: Waived for Fall 2024.
• Language requirement: The English Proficiency Test requirement (TOEFL) is a Provost's requirement that cannot be waived.
• The Columbia GSAS minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS. To see if this requirement can be waived for you, please check the frequently asked questions below.
• Deadline: Jan 8, 2024 .
• Application process: Please apply by completing the Application for Admission to the Columbia University Graduate School of Arts & Sciences .
• Timeline: P.hD students begin the program in September only.  Admissions decisions are made in mid-March of each year for the Fall semester.

Frequently Asked Questions

• What is the application deadline? What is the deadline for financial aid? Our application deadline is January 5, 2024 .
• Can I meet with you in person or talk to you on the phone? Unfortunately given the high number of applications we receive, we are unable to meet or speak with our applicants.
• What are the required application materials? Specific admission requirements for our programs can be found here .
• Due to financial hardship, I cannot pay the application fee, can I still apply to your program? Yes. Many of our prospective students are eligible for fee waivers. The Graduate School of Arts and Sciences offers a variety of application fee waivers . If you have further questions regarding the waiver please contact  gsas-admissions@ columbia.edu .
• How many students do you admit each year? It varies year to year. We finalize our numbers between December - early February.
• What is the distribution of students currently enrolled in your program? (their background, GPA, standard tests, etc)? Unfortunately, we are unable to share this information.
• How many accepted students receive financial aid? All students in the PhD program receive, for up to five years, a funding package consisting of tuition, fees, and a stipend. These fellowships are awarded in recognition of academic achievement and in expectation of scholarly success; they are contingent upon the student remaining in good academic standing. Summer support, while not guaranteed, is generally provided. Teaching and research experience are considered important aspects of the training of graduate students. Thus, graduate fellowships include some teaching and research apprenticeship. PhD students are given funds to purchase a laptop PC, and additional computing resources are supplied for research projects as necessary. The Department also subsidizes travel expenses for up to two scientific meetings and/or conferences per year for those students selected to present. Additional matching funds from the Graduate School Arts and Sciences are available to students who have passed the oral qualifying exam.
• Can I contact the department with specific scores and get feedback on my competitiveness for the program? We receive more than 450 applications a year and there are many students in our applicant pool who are qualified for our program. However, we can only admit a few top students. Before seeing the entire applicant pool, we cannot comment on admission probabilities.
• What is the minimum GPA for admissions? While we don’t have a GPA threshold, we will carefully review applicants’ transcripts and grades obtained in individual courses.
• Is there a minimum GRE requirement? No. The general GRE exam is waived for the Fall 2024 admissions cycle. 
• Can I upload a copy of my GRE score to the application? Yes, but make sure you arrange for ETS to send the official score to the Graduate School of Arts and Sciences.
• Is the GRE math subject exam required? No, we do not require the GRE math subject exam.
• What is the minimum TOEFL or IELTS  requirement? The Columbia Graduate School of Arts and Sciences minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS
•  I took the TOEFL and IELTS more than two years ago; is my score valid? Scores more than two years old are not accepted. Applicants are strongly urged to make arrangements to take these examinations early in the fall and before completing their application.
• I am an international student and earned a master’s degree from a US university. Can I obtain a TOEFL or IELTS waiver? You may only request a waiver of the English proficiency requirement from the Graduate School of Arts and Sciences by submitting the English Proficiency Waiver Request form and if you meet any of the criteria described here . If you have further questions regarding the waiver please contact  gsas-admissions@ columbia.edu .
• My transcript is not in English. What should I do? You have to submit a notarized translated copy along with the original transcript.

Can I apply to more than one PhD program? You may not submit more than one PhD application to the Graduate School of Arts and Sciences. However, you may elect to have your application reviewed by a second program or department within the Graduate School of Arts and Sciences if you are not offered admission by your first-choice program. Please see the application instructions for a more detailed explanation of this policy and the various restrictions that apply to a second choice. You may apply concurrently to a program housed at the Graduate School of Arts and Sciences and to programs housed at other divisions of the University. However, since the Graduate School of Arts and Sciences does not share application materials with other divisions, you must complete the application requirements for each school.

How do I apply to a dual- or joint-degree program? The Graduate School of Arts and Sciences refers to these programs as dual-degree programs. Applicants must complete the application requirements for both schools. Application materials are not shared between schools. Students can only apply to an established dual-degree program and may not create their own.

With the sole exception of approved dual-degree programs , students may not pursue a degree in more than one Columbia program concurrently, and may not be registered in more than one degree program at any institution in the same semester. Enrollment in another degree program at Columbia or elsewhere while enrolled in a Graduate School of Arts and Sciences master's or doctoral program is strictly prohibited by the Graduate School. Violation of this policy will lead to the rescission of an offer of admission, or termination for a current student.

When will I receive a decision on my application? Notification of decisions for all PhD applicants generally takes place by the end of March.

Notification of MA decisions varies by department and application deadlines. Some MA decisions are sent out in early spring; others may be released as late as mid-August.

Can I apply to both MA Statistics and PhD statistics simultaneously?  For any given entry term, applicants may elect to apply to up to two programs—either one PhD program and one MA program, or two MA programs—by submitting a single (combined) application to the Graduate School of Arts and Sciences.  Applicants who attempt to submit more than one Graduate School of Arts and Sciences application for the same entry term will be required to withdraw one of the applications.

The Graduate School of Arts and Sciences permits applicants to be reviewed by a second program if they do not receive an offer of admission from their first-choice program, with the following restrictions:

• This option is only available for fall-term applicants.
• Applicants will be able to view and opt for a second choice (if applicable) after selecting their first choice. Applicants should not submit a second application. (Note: Selecting a second choice will not affect the consideration of your application by your first choice.)
• Applicants must upload a separate Statement of Purpose and submit any additional supporting materials required by the second program. Transcripts, letters, and test scores should only be submitted once.
• An application will be forwarded to the second-choice program only after the first-choice program has completed its review and rendered its decision. An application file will not be reviewed concurrently by both programs.
• Programs may stop considering second-choice applications at any time during the season; Graduate School of Arts and Sciences cannot guarantee that your application will receive a second review.
• What is the mailing address for your PhD admission office? Students are encouraged to apply online . Please note: Materials should not be mailed to the Graduate School of Arts and Sciences unless specifically requested by the Office of Admissions. Unofficial transcripts and other supplemental application materials should be uploaded through the online application system. Graduate School of Arts and Sciences Office of Admissions Columbia University  107 Low Library, MC 4303 535 West 116th Street  New York, NY 10027
• How many years does it take to pursue a PhD degree in your program? Our students usually graduate in 4‐6 years.
• Can the PhD be pursued part-time? No, all of our students are full-time students. We do not offer a part-time option.
• One of the requirements is to have knowledge of linear algebra (through the level of MATH V2020 at Columbia) and advanced calculus (through the level of MATH V1201). I studied these topics; how do I know if I meet the knowledge content requirement? We interview our top candidates and based on the information on your transcripts and your grades, if we are not sure about what you covered in your courses we will ask you during the interview.
• Can I contact faculty members to learn more about their research and hopefully gain their support? Yes, you are more than welcome to contact faculty members and discuss your research interests with them. However, please note that all the applications are processed by a central admission committee, and individual faculty members cannot and will not guarantee admission to our program.
• How do I find out which professors are taking on new students to mentor this year?  Applications are evaluated through a central admissions committee. Openings in individual faculty groups are not considered during the admissions process. Therefore, we suggest contacting the faculty members you would like to work with and asking if they are planning to take on new students.

For more information please contact us at [email protected] .

For more information please contact us at  [email protected]

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Cornell University does not offer a separate Masters of Science (MS) degree program in the field of Statistics. Applicants interested in obtaining a masters-level degree in statistics should consider applying to Cornell's MPS Program in Applied Statistics.

Choosing a Field of Study

There are many graduate fields of study at Cornell University. The best choice of graduate field in which to pursue a degree depends on your major interests. Statistics is a subject that lies at the interface of theory, applications, and computing. Statisticians must therefore possess a broad spectrum of skills, including expertise in statistical theory, study design, data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and methods to their collaborators, and assist them in properly communicating their results. If the study of statistics is your major interest then you should seriously consider applying to the Field of Statistics.

There are also several related fields that may fit even better with your interests and career goals. For example, if you are mainly interested in mathematics and computation as they relate to modeling genetics and other biological processes (e.g, protein structure and function, computational neuroscience, biomechanics, population genetics, high throughput genetic scanning), you might consider the Field of Computational Biology . You may wish to consider applying to the Field of Electrical and Computer Engineering if you are interested in the applications of probability and statistics to signal processing, data compression, information theory, and image processing. Those with a background in the social sciences might wish to consider the Field of Industrial and Labor Relations with a major or minor in the subject of Economic and Social Statistics. Strong interest and training in mathematics or probability might lead you to choose the Field of Mathematics . Lastly, if you have a strong mathematics background and an interest in general problem-solving techniques (e.g., optimization and simulation) or applied stochastic processes (e.g., mathematical finance, queuing theory, traffic theory, and inventory theory) you should consider the Field of Operations Research .

Residency Requirements

Students admitted to PhD program must be "in residence" for at least four semesters, although it is generally expected that a PhD will require between 8 and 10 semesters to complete. The chair of your Special Committee awards one residence unit after the satisfactory completion of each semester of full-time study. Fractional units may be awarded for unsatisfactory progress.

Your Advisor and Special Committee

The Director of Graduate Studies is in charge of general issues pertaining to graduate students in the field of Statistics. Upon arrival, a temporary Special Committee is also declared for you, consisting of the Director of Graduate Studies (chair) and two other faculty members in the field of Statistics. This temporary committee shall remain in place until you form your own Special Committee for the purposes of writing your doctoral dissertation. The chair of your Special Committee serves as your primary academic advisor; however, you should always feel free to contact and/or chat with any of the graduate faculty in the field of Statistics.

The formation of a Special Committee for your dissertation research should serve your objective of writing the best possible dissertation. The Graduate School requires that this committee contain at least three members that simultaneously represent a certain combination of subjects and concentrations. The chair of the committee is your principal dissertation advisor and always represents a specified concentration within the subject & field of Statistics. The Graduate School additionally requires PhD students to have at least two minor subjects represented on your special committee. For students in the field of Statistics, these remaining two members must either represent (i) a second concentration within the subject of Statistics, and one external minor subject; or, (ii) two external minor subjects. Each minor advisor must agree to serve on your special committee; as a result, the identification of these minor members should occur at least 6 months prior to your A examination.

Some examples of external minors include Computational Biology, Demography, Computer Science, Economics, Epidemiology, Mathematics, Applied Mathematics and Operations Research. The declaration of an external minor entails selecting (i) a field other than Statistics in which to minor; (ii) a subject & concentration within the specified field; and, (iii) a minor advisor representing this field/subject/concentration that will work with you in setting the minor requirements. Typically, external minors involve gaining knowledge in 3-5 graduate courses in the specified field/subject, though expectations can vary by field and even by the choice of advisor. While any choice of external minor subject is technically acceptable, the requirement that the minor representative serve on your Special Committee strongly suggests that the ideal choice(s) should share some natural connection with your choice of dissertation topic.

The fields, subjects and concentrations represented on your committee must be officially recognized by the Graduate School ; the Degrees, Subjects & Concentrations tab listed under each field of study provides this information. Information on the concentrations available for committee members chosen to represent the subject of Statistics can be found on the Graduate School webpage . 

Statistics PhD Travel Support

The Department of Statistics and Data Science has established a fund for professional travel for graduate students. The intent of the Department is to encourage travel that enhances the Statistics community at Cornell by providing funding for graduate students in statistics that will be presenting at conferences. Please review the Graduate Student Travel Award Policy website for more information. 

Completion of the PhD Degree

In addition to the specified residency requirements, students must meet all program requirements as outlined in Program Course Requirements and Timetables and Evaluations and Examinations, as well as complete a doctoral dissertation approved by your Special Committee. The target time to PhD completion is between 4 and 5 years; the actual time to completion varies by student.

Students should consult both the Guide to Graduate Study and Code of Legislation of the Graduate Faculty (available at www.gradschool.cornell.edu ) for further information on all academic and procedural matters pertinent to pursuing a graduate degree at Cornell University.

Ph.D. in Statistics

Our doctoral program in statistics gives future researchers preparation to teach and lead in academic and industry careers.

Program Description

Degree type.

approximately 5 years

The relatively new Ph.D. in Statistics strives to be an exemplar of graduate training in statistics. Students are exposed to cutting edge statistical methodology through the modern curriculum and have the opportunity to work with multiple faculty members to take a deeper dive into special topics, gain experience in working in interdisciplinary teams and learn research skills through flexible research electives. Graduates of our program are prepared to be leaders in statistics and machine learning in both academia and industry.

The Ph.D. in Statistics is expected to take approximately five years to complete, and students participate as full-time graduate students.  Some students are able to finish the program in four years, but all admitted students are guaranteed five years of financial support.  

Within our program, students learn from global leaders in statistics and data sciences and have:

20 credits of required courses in statistical theory and methods, computation, and applications

18 credits of research electives working with two or more faculty members, elective coursework (optional), and a guided reading course

Dissertation research

Coursework Timeline

Year 1: focus on core learning.

The first year consists of the core courses:

• SDS 384.2 Mathematical Statistics I
• SDS 383C Statistical Modeling I
• SDS 387 Linear Models
• SDS 384.11 Theoretical Statistics
• SDS 383D Statistical Modeling II
• SDS 386D Monte Carlo Methods

In addition to the core courses, students of the first year are expected to participate in SDS 190 Readings in Statistics. This class focuses on learning how to read scientific papers and how to grasp the main ideas, as well as on practicing presentations and getting familiar with important statistics literature.

At the end of the first year, students are expected to take a written preliminary exam. The examination has two purposes: to assess the student’s strengths and weaknesses and to determine whether the student should continue in the Ph.D. program. The exam covers the core material covered in the core courses and it consists of two parts: a 3-hour closed book in-class portion and a take-home applied statistics component. The in-class portion is scheduled at the end of the Spring Semester after final exams (usually late May). The take-home problem is distributed at the end of the in-class exam, with a due-time 24 hours later. 

Year 2: Transitioning from Student to Researcher

In the second year of the program, students take the following courses totaling 9 credit hours each semester:

• Required: SDS 190 Readings in Statistics (1 credit hour)
• Required: SDS 389/489 Research Elective* (3 or 4 credit hours) in which the student engages in independent research under the guidance of a member of the Statistics Graduate Studies Committee
• One or more elective courses selected from approved electives ; and/or
• One or more sections of SDS 289/389/489 Research Elective* (2 to 4 credit hours) in which the student engages in independent research with a member(s) of the Statistics Graduate Studies Committee OR guided readings/self-study in an area of statistics or machine learning. 
• Internship course (0 or 1 credit hour; for international students to obtain Curricular Practical Training; contact Graduate Coordinator for appropriate course options)
• GRS 097 Teaching Assistant Fundamentals or NSC 088L Introduction to Evidence-Based Teaching (0 credit hours; for TA and AI preparation)

* Research electives allow students to explore different advising possibilities by working for a semester with a particular professor. These projects can also serve as the beginning of a dissertation research path. No more than six credit hours of research electives can be taken with a single faculty member in a semester.

Year 3: Advance to Candidacy

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. At the end of the second year or during their third year, students are expected to present their plan of study for the dissertation in an Oral candidacy exam. During this exam, students should demonstrate their research proficiency to their Ph.D. committee members. Students who successfully complete the candidacy exam can apply for admission to candidacy for the Ph.D. once they have completed their required coursework and satisfied departmental requirements. The steps to advance to candidacy are:

• Discuss potential candidacy exam topics with advisor
• Propose Ph.D. committee: the proposed committee must follow the Graduate School and departmental regulations on committee membership for what will become the Ph.D. Dissertation Committee
•   Application for candidacy

Year 4+: Dissertation Completion and Defense

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. Moreover, they are expected to present part of their work in the framework of the department's Ph.D. poster session.

Students who are admitted to candidacy will be expected to complete and defend their Ph.D. thesis before their Ph.D. committee to be awarded the degree. The final examination, which is oral, is administered only after all coursework, research and dissertation requirements have been fulfilled. It is expected that students will be prepared to defend by the end of their fifth year in the doctoral program.

General Information and Expectations for All Ph.D. students

• 2023-24 Student Handbook
• Annual Review At the end of every year (due May 1), students are expected to fill out the Annual Progress Review . 
• Seminar Series All students are expected to attend the SDS Seminar Series
• SDS 189R Course Description (when taken for internship)
• Internship Course Registration form
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Attending Conferences 

Students are encouraged to attend conferences to share their work. All research-related travel while in student status require prior authorization.

• Request for Travel Authorization (both domestic and international travel)
• Request for Authorization for International Travel  

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Ph.D. Program

The PhD program prepares students for research careers in theory and application of probability and statistics in academic and non-academic (e.g., industry, government) settings.  Students might elect to pursue either the general Statistics track of the program (the default), or one of the four specialized tracks that take advantage of UW’s interdisciplinary environment: Statistical Genetics (StatGen), Statistics in the Social Sciences (CSSS), Machine Learning and Big Data (MLBD), and Advanced Data Science (ADS). 

Admission Requirements

For application requirements and procedures, please see the graduate programs applications page .

Recommended Preparation

The Department of Statistics at the University of Washington is committed to providing a world-class education in statistics. As such, having some mathematical background is necessary to complete our core courses. This background includes linear algebra at the level of UW’s MATH 318 or 340, advanced calculus at the level of MATH 327 and 328, and introductory probability at the level of MATH 394 and 395. Real analysis at the level of UW’s MATH 424, 425, and 426 is also helpful, though not required. Descriptions of these courses can be found in the UW Course Catalog . We also recognize that some exceptional candidates will lack the needed mathematical background but succeed in our program. Admission for such applicants will involve a collaborative curriculum design process with the Graduate Program Coordinator to allow them to make up the necessary courses. 

While not a requirement, prior background in computing and data analysis is advantageous for admission to our program. In particular, programming experience at the level of UW’s CSE 142 is expected.  Additionally, our coursework assumes familiarity with a high-level programming language such as R or Python. 

Graduation Requirements 

This is a summary of the department-specific graduation requirements. For additional details on the department-specific requirements, please consult the  Ph.D. Student Handbook .  For previous versions of the Handbook, please contact the Graduate Student Advisor .  In addition, please see also the University-wide requirements at  Instructions, Policies & Procedures for Graduate Students  and  UW Doctoral Degrees .  

General Statistics Track

• Core courses: Advanced statistical theory (STAT 581, STAT 582 and STAT 583), statistical methodology (STAT 570 and STAT 571), statistical computing (STAT 534), and measure theory (either STAT 559 or MATH 574-575-576).  
• Elective courses: A minimum of four approved 500-level classes that form a coherent set, as approved in writing by the Graduate Program Coordinator.  A list of elective courses that have already been pre-approved or pre-denied can be found here .
• M.S. Theory Exam: The syllabus of the exam is available here .
• Research Prelim Exam. Requires enrollment in STAT 572. 
• Consulting.  Requires enrollment in STAT 599. 
• Applied Data Analysis Project.  Requires enrollment in 3 credits of STAT 597. 
• Statistics seminar participation: Students must attend the Statistics Department seminar and enroll in STAT 590 for at least 8 quarters. 
• Teaching requirement: All Ph.D. students must satisfactorily serve as a Teaching Assistant for at least one quarter. 
• General Exam. 
• Dissertation Credits.  A minimum of 27 credits of STAT 800, spread over at least three quarters. 
• Passage of the Dissertation Defense. 

Statistical Genetics (StatGen) Track

Students pursuing the Statistical Genetics (StatGen) Ph.D. track are required to take BIOST/STAT 550 and BIOST/STAT 551, GENOME 562 and GENOME 540 or GENOME 541. These courses may be counted as the four required Ph.D.-level electives. Additionally, students are expected to participate in the Statistical Genetics Seminar (BIOST581) in addition to participating in the statistics seminar (STAT 590). Finally, students in the Statistics Statistical Genetics Ph.D. pathway may take STAT 516-517 instead of STAT 570-571 for their Statistical Methodology core requirement. This is a transcriptable program option, i.e., the fact that the student completed the requirements will be noted in their transcript.

Statistics in the Social Sciences (CSSS) Track

Students in the Statistics in the Social Sciences (CSSS) Ph.D. track  are required to take four numerically graded 500-level courses, including at least two CSSS courses or STAT courses cross-listed with CSSS, and at most two discipline-specific social science courses that together form a coherent program of study. Additionally, students must complete at least three quarters of participation (one credit per quarter) in the CS&SS seminar (CSSS 590). This is not a transcriptable option, i.e., the fact that the student completed the requirements will not be noted in their transcript.

Machine Learning and Big Data Track

Students in the Machine Learning and Big Data (MLBD) Ph.D. track are required to take the following courses: one foundational machine learning course (STAT 535), one advanced machine learning course (either STAT 538 or STAT 548 / CSE 547), one breadth course (either on databases, CSE 544, or data visualization, CSE 512), and one additional elective course (STAT 538, STAT 548, CSE 515, CSE 512, CSE 544 or EE 578). At most two of these four courses may be counted as part of the four required PhD-level electives. Students pursuing this track are not required to take STAT 583 and can use STAT 571 to satisfy the Applied Data Analysis Project requirement. This is not a transcriptable option, i.e., the fact that the student completed the requirements will not be noted in their transcript. 

Advanced Data Science (ADS) Track

Students in the Advanced Data Science (ADS) Ph.D. track are required to take the same coursework as students in the Machine Learning and Big Data track. They are also not required to take STAT 583 and can use STAT 571 to satisfy the Applied Data Analysis Project requirement. The only difference in terms of requirements between the MLBD and the ADS tracks is that students in the ADS track must also register for at least 4 quarters of the weekly eScience Community Seminar (CHEM E 599). Also, unlike the MLBD track, the ADS is a transcriptable program option, i.e., the fact that the student completed the requirements will be noted in their transcript. 

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PhD Program

Wharton’s PhD program in Statistics provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include: analysis of observational studies; Bayesian inference, bioinformatics; decision theory; game theory; high dimensional inference; information theory; machine learning; model selection; nonparametric function estimation; and time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

Apply online here .

Department of Statistics and Data Science

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Phone: (215) 898-8222

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The department encourages research in both theoretical and applied statistics. Faculty members of the department have been leaders in research on a multitude of topics that include statistical inference, statistical computing and Monte-Carlo methods, analysis of missing data, causal inference, stochastic processes, multilevel models, experimental design, network models and the interface of statistics and the social, physical, and biological sciences. A unique feature of the department lies in the fact that apart from methodological research, all the faculty members are also heavily involved in applied research, developing novel methodology that can be applied to a wide array of fields like astrophysics, biology, chemistry, economics, engineering, public policy, sociology, education and many others.

Two carefully designed special courses offered to Ph.D. students form a unique feature of our program. Among these, Stat 303 equips students with the  basic skills necessary to teach statistics , as well as to be better overall statistics communicators. Stat 399 equips them with generic skills necessary for problem solving abilities.

Our Ph.D. students often receive substantial guidance from several faculty members, not just from their primary advisors, and in several settings. For example, every Ph.D. candidate who passes the qualifying exam gives a 30 minute presentation each semester (in Stat 300 ), in which the faculty ask questions and make comments. The Department recently introduced an award for Best Post-Qualifying Talk (up to two per semester), to further encourage and reward inspired research and presentations.

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PhD Program

Advanced undergraduate or masters level work in mathematics and statistics will provide a good background for the doctoral program. Quantitatively oriented students with degrees in other scientific fields are also encouraged to apply for admission. In particular, the department has expanded its research and educational activities towards computational biology, mathematical finance and information science. The doctoral program normally takes four to five years to complete.

Doctoral Program in Statistics

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This program has a rich tradition of creating groundbreaking statistical methods and conducting innovative applied statistics, bridging theory and practice and supporting knowledge discovery and decision-making through meaningful data extraction and analysis. Statistics is an indispensable pillar of modern science, including data science and artificial intelligence.

You can take advantage of the department’s flexible research options and work with your faculty of choice. You can leverage cross-department collaboration with biology, chemistry, medical sciences, economics, computer science, government, and public health to pursue your intellectual interests. You will become part of a close-knit, friendly department that offers many extra learning opportunities both inside and outside the program.

Examples of student projects include developing statistical methods to forecast infectious diseases from online search data, delineating causality from association, building a software package for evaluating redistricting plans in 50 states, leveraging machine learning algorithms for model-free inference, and employing a randomization-based inference framework to study peer effects. 

Graduates have secured faculty positions in institutions such as Stanford University; University of Pennsylvania; University of California, Berkeley; Johns Hopkins University; Carnegie Mellon University; Columbia University; and Georgia Institute of Technology. Others have begun careers at organizations such as Google, Apple, Etsy, Citadel, and the Boston Red Sox. 

Additional information on the graduate program is available from the Department of Statistics , and requirements for the degree are detailed in Policies .

Admissions Requirements

Please review admissions requirements and other information before applying. You can find degree program-specific admissions requirements below and access additional guidance on applying from the Department of Statistics .

Academic Background

Applicants should understand what the discipline of statistics entails and show evidence of involvement in applications or a strong theoretical interest.

The minimum mathematical preparation for admission is linear algebra and advanced calculus. Ideally, each student’s preparation should include at least one term each of mathematical probability and mathematical statistics. Additional study in statistics and related mathematical areas, such as analysis and measure theory, is helpful. In the initial stages of graduate study, students should give high priority to acquiring the mathematical level required to satisfy their objectives.

As statistics is so intimately connected with computation, computation is an important part of almost all courses and research projects in the department. Preferably, students should have programming experience relevant for statistical computation and simulation.

Standardized Tests

GRE General: Optional GRE Subject: Optional

Theses & Dissertations

Theses & Dissertations for Statistics

See list of Statistics faculty

APPLICATION DEADLINE

Questions about the program.

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With a graduate degree, statisticians may find jobs

With a graduate degree, statisticians may find jobs working with data in many sectors, including business, government, academia, public health, technology and other science fields. These are the best schools for statistics. Each school's score reflects its average rating on a scale from 1 (marginal) to 5 (outstanding), based on a survey of academics at peer institutions. Read the methodology »

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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

Statistics PhD

The Department of Statistics offers the Master of Arts (MA) and Doctor of Philosophy (PhD) degrees.

Master of Arts (MA)

The Statistics MA program prepares students for careers that require statistical skills. It focuses on tackling statistical challenges encountered by industry rather than preparing for a PhD. The program is for full-time students and is designed to be completed in two semesters (fall and spring).

There is no way to transfer into the PhD program from the MA program. Students must apply to the PhD program.

Doctor of Philosophy (PhD)

The Statistics PhD program is rigorous, yet welcoming to students with interdisciplinary interests and different levels of preparation. The standard PhD program in statistics provides a broad background in probability theory and applied and theoretical statistics.

There are three designated emphasis (DE) tracks available to students in the PhD program who wish to pursue interdisciplinary work formally: Computational and Data Science and Engineering , Computational and Genomic Biology and Computational Precision Health .

Contact Info

[email protected]

367 Evans Hall, University of California

Berkeley, CA 94720-3860

At a Glance

Department(s)

Admit Term(s)

Application Deadline

December 4, 2023

Degree Type(s)

Doctoral / PhD

Degree Awarded

GRE Requirements

/images/cornell/logo35pt_cornell_white.svg" alt="statistics for phd"> Cornell University --> Graduate School

Doctoral program statistics.

Use this page to explore summary statistics for research doctoral programs administered by the Graduate School. While the most common doctoral degree is the Ph.D., the D.M.A. in Music and the J.S.D. in Law are also included here. Methodology and definitions are provided at the bottom of the page.   

For additional graduate statistics, survey results, and career outcomes data, see program metrics .

Methodology and Definitions

Admissions counts.

Applied, admitted and matriculated counts are reported for new, external applications only. Current students who transfer into a different graduate program at Cornell without submitting a new application are not counted here.

Individuals may defer enrollment and/or be admitted to a program that differs from the one to which they originally applied. This can cause admitted and matriculated counts to be higher than application counts in some fields. 

Admission cycles start in the summer and continue through the following spring. For example, the 2020-21 admissions year includes data from summer 2020 through spring 2021. Because these dashboards are updated annually in the fall, the most recent year will not include data from the spring.

Average Admit Rate

Admit rate is the percentage of applicants who were admitted. Highly selective programs tend to have low admit rates. The five year average provides a good indicator of typical admit rates.

Enrollment numbers are derived from the student enrollment snapshot that is captured the sixth week of each fall term. Only students who are enrolled on the census date are counted. Students on an approved leave of absence are not included.

Average Completion Rate

Completion rate is the percentage of entering doctoral students who successfully completed the degree. Completion rates are reported by entering cohort, which is defined by the first term in which a student is enrolled in their doctoral program, regardless of any prior enrollment in a master’s program. The cohorts included here entered their programs seven to twelve years ago, and thus have had adequate time to finish a doctoral degree.

Status of Students in Each Recent Entering Cohort

This graph shows the current status of students who began the doctoral program in each of the last ten academic years. Students listed as completed have received the doctoral degree. Students are considered current in their program if they are still actively pursuing the doctoral degree or are on an approved temporary leave of absence. Students listed as discontinued have either left the university without a degree or switched to a different type of degree program (in many cases a master’s degree).

Time to Degree (TTD)

Time-to-degree degree measures the time in years from the first day of a student’s initial enrollment in their doctoral program to the day of their degree conferral. Time-to-degree measures elapsed time only, not enrolled time. It does not stop and start if a student takes a leave of absence. For Master’s/PhD students, time-to-degree starts when they begin the PhD phase of their studies. If a student was enrolled in a master’s program prior to matriculating in the doctoral program, the separate time in the master’s program is not included. Because of this, time-to-degree may appear shorter in some doctoral programs where it is common to complete a master’s prior to matriculation in the doctoral program.

The median time to degree can be thought of as the “mid-point”, where half of the students completed in a time period that is less than or equal to this value. The median is not affected by extreme values or outliers. 

• Doing a PhD in Statistics

We live in a data-rich world. The study of statistics allows us to better understand data, measure uncertainty, and calculate risk. The applications of such knowledge are widespread – from economics to medicine. A PhD in Statistics will give you a deep understanding of the mathematical framework which underpins data analysis as we know it. Read on to find out the key information about a PhD in statistics, and whether it is worth it for you.

What Does a PhD in Statistics Focus On?

A Statistics PhD programme can focus on:

• Statistical theory and statistical methods
• Bayesian statistics
• Covariance modelling
• High dimensional data
• Probability theory
• Causal inference
• Extreme value theory
• Non-parametric regression
• Symbolic computation
• Applied statistics

The list above is only a small sample of the many different areas within probability and statistics. Many PhD research projects place a particular emphasis on statistics within environmental, biomedical, and social science. Aside from this there is also overlap with other field such as computer science, applied mathematics, and linear algebra.

Browse PhDs in Statistics

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, entry requirements for a phd in statistics.

Most Statistics PhD programmes require applicants to have, or expect to obtain, a bachelor’s degree (or international equivalent) in Mathematics or Statistics. However, many Statistics PhD research projects also accept applications from graduate students with a bachelor’s degree in other subjects if they involve a significant mathematical component (such as Data Science , Physics, or Computer Science). Many universities expect first class honours due to the high competition for places, though for some institutions second class honours (2:1) is adequate.

It is also common for universities to accept second class honours (2:1), if the graduate has a master’s degree or relevant work experience.

Universities typically expect international students to provide evidence of their English Language ability. This is usually benchmarked by a IELTS score of 6.5 (with a minimum score of 6 in each component), a TOEFL (iBT) score 92, a CAE and CPE score of 176 or another equivalent. The exact score requirements may differ across different universities.

Duration and Programme Types

The typical doctoral programme in Statistics takes 3-4 years full-time, or 6 years part-time.

A PhD research project in Statistics can focus on a particular application of statistics. For example, you may undertake a PhD in statistical genomics or biostatistics, which would involve interdisciplinary work and additional training modules to understand how statistics can improve biological and genetic study.

In addition to the statistics course modules, you will likely undertake ‘ transferable skills ‘ training in communication, management, and commerce – all of which are skills a good postgraduate research student needs.

As with most PhDs, you will have to complete a dissertation at the end of your postgraduate research project, and undertake an oral examination known as the viva , where you are required to defend your dissertation to a supervisory committee/dissertation committee usually made up of two examiners.

Costs and Funding

Annual tuition fees for PhDs in Statistics are typically around £4,000 to £5,000 for UK/EU students. Tuition fees for international students are usually much higher, typically around £20,000 – £25,000 per academic year. Tuition fees for part time programmes are typically scaled down according to the programme length.

Some Statistics PhD programmes also have additional costs to cover laboratory resources, administration and computational costs.

Together with EPSRC and other national funding sources, many Universities offer postgraduate studentships which cover the tuition fees for Statistical PhD programmes. EPSRC DTA research studentships are available in all areas for UK and EU students. Students who are normally resident in the EU but not in the UK are eligible for EPSRC PhD studentships, but the awards in such cases currently cover only the course fees, not maintenance stipends .

Available Career Paths in Statistics

One of the key advantages of Statistics is that it is a fundamental concept which underpins most industries. Consequently, there are an abundance of career paths available for Statistics PhD doctorates such as agriculture, forensics, machine learning, informatics, geosciences, law and biomathematics.

Examples of common destinations for a Statistics PhD student include:

• Actuarial Science – Actuaries are responsible for analysing data to help non-specialists make informed decisions about risks. A good understanding of probability and investment is crucial in this field. Salaries for Statistics PhD students in this field vary, but with around 10 years’ experience typically are around £60,000.
• Environmental statistician – In this role, Statistics doctorates use their knowledge to contribute to environmental study. This can include monitoring climate patterns, carrying out flood risk assessments, or transforming large amounts of temperature data into information for the public.
• Data Analyst – Some people use their PhD in stats to become data analysts, responsible for data management, developing automated processes, tracking KPIs, and more. Data analysts can be found in various industries form logistics & transport to marketing. Again, with experience Statistics doctorates in this path can expect a lucrative salary.
• Medical statistician – PhD graduates in the medical field aid health research in a number of ways, for example analysing data from clinical studies to identify patterns. The NHS, private health companies and the pharmaceutical industry are common employers for those with a PhD degree in statistics or applied statistics.
• University lecturer – Often PhD students opt to stay in academia. This can be as a university lecturer where you will teach students about statistical theory.

Browse PhDs Now

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Statistics, PHD

On this page:, at a glance: program details.

• Location: Tempe campus
• Second Language Requirement: No

Program Description

Degree Awarded: PHD Statistics

As a science, statistics focuses on data collection and data analysis by using theoretical, applied and computational tools. The PhD program in statistics reflects this breadth in tools and considerations while allowing students sufficient flexibility to tailor their program of study to reflect individual interests and goals. Research can be of a disciplinary or transdisciplinary nature.

Degree Requirements

84 credit hours, a written comprehensive exam, a prospectus and a dissertation

Required Core (3 credit hours) STP 526 Theory of Statistical Linear Models (3)

Other Requirements (15 credit hours) IEE 572 Design Engineering Experiments (3) or STP 531 Applied Analysis of Variance (3) IEE 578 Regression Analysis (3) or STP 530 Applied Regression Analysis (3) STP 501 Theory of Statistics I: Distribution Theory 3 (3) STP 502 Theory of Statistics II: Inference (3) STP 527 Statistical Large Sample Theory (3)

Electives (42 credit hours)

Research (12 credit hours) STP 792 Research (12)

Culminating Experience (12 credit hours) STP 799 Dissertation (12)

Additional Curriculum Information Electives are chosen from statistics or related area courses approved by the student's supervisory committee.

Other requirements courses may be substituted with department approval.

Students must pass:

• one qualifying examination and coursework in analysis
• a written comprehensive examination
• a dissertation prospectus defense

Students should see the department website for examination information.

Each student must write a dissertation and defend it orally in front of five dissertation committee members.

Admission Requirements

Applicants must fulfill the requirements of both the Graduate College and The College of Liberal Arts and Sciences.

Applicants are eligible to apply to the program if they have earned a bachelor's or master's degree in mathematics, statistics or a closely related area from a regionally accredited institution.

Applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in the last 60 hours of their first bachelor's degree program or a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in an applicable master's degree program.

All applicants must submit:

• graduate admission application and application fee
• official transcripts
• statement of education and career goals
• three letters of recommendation
• proof of English proficiency

Additional Application Information An applicant whose native language is not English must provide proof of English proficiency regardless of their current residency.

Completion of the following courses (equivalents at ASU are given in parentheses) is required. Applicants who lack any of these prerequisite courses must complete them before being considered for admission.

• calculus (MAT 270, 271 and 272)
• advanced calculus (MAT 371)
• linear algebra (MAT 342)
• computer programming (CSE 100)
• introductory applied statistics (STP 420)

Next Steps to attend ASU

Learn about our programs, apply to a program, visit our campus, application deadlines, learning outcomes.

• Able to complete original research in statistics.
• Proficient in applying advanced statistical methods in coursework and research.
• Address an original research question in statistics.

Career Opportunities

Statistical analysis and data mining have been identified as two of the most desirable skills in today's job market. Data, and the analysis of data, is big business, and the Department of Labor projects that overall employment of mathematicians and statisticians will grow 33% between 2020 and 2030, much faster than the average for all occupations.

For graduates of the doctoral program in statistics, that means a broad variety of career opportunities in fields as diverse as business, finance, engineering, technology, education, marketing, government and other areas of the economy.

These are just a few of the top career opportunities available for a graduate with a doctoral degree in statistics:

• business consultant or analyst
• data science professor, instructor or researcher
• data scientist
• faculty-track academic
• financial analyst
• market research analyst
• software engineer
• statistician

Program Contact Information

If you have questions related to admission, please click here to request information and an admission specialist will reach out to you directly. For questions regarding faculty or courses, please use the contact information below.

• [email protected]
• 480/965-3951

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• Statistics Major Advising and Registration FAQs
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• Clubs and Organizations

Ph.D. Programs

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We are an excellent choice for bright minds seeking an outstanding statistics doctoral education.

Ph.D. in Statistics Our core Ph.D. program focuses on providing students with comprehensive and rigorous training in cutting-edge statistical theory, methodology and applications, state-of-the-art computing skills and ability for conducting high-quality interdisciplinary research. Our Ph.D. graduates typically take positions in academia, government, financial services, and pharmaceutical and IT industries.

Ph.D. Co-major A student pursuing the Ph.D. co-major program must satisfy the requirements of both departments. In the past, co-major degrees at the Ph.D. level were awarded jointly with biomathematics, crop science, economics and operations research. The dissertation must embody the results of original research of a standard that would warrant publication in a research journal from one or both of the two fields. A statistics Ph.D. co-major committee must have at least two statistics faculty (faculty with at least a 25 percent appointment in statistics), including the chair or one of the co-chairs.

Students currently enrolled in a graduate program at NC State may request a statistics minor, unless the program is an option B program. Please consult the university’s Graduate Administrative Handbook for rules about obtaining a minor.

Learn more about minors.

Program Prerequisites

Students are expected to have a good foundation in the material covered in the core courses (ST 701, 702, 703, 704 and 705), even if their master’s degree was received at another institution. Some students with previous master’s degrees find it useful to take these courses at NC State. However, this tends to lengthen the time to degree. Students are also expected to have had a course comparable to MA 425 (Mathematical Analysis I).

Required Coursework

Students that join our doctoral program with a Master of Statistics from another university are required to have a minimum of 54 credit hours in their doctoral Plan of Work (POW). Students who receive their master’s degree from NC State must have a minimum of 72 credit hours on the master’s and Ph.D. POWs combined. The POW may include research credit hours (ST 895); however, students are required to take 24 hours of coursework consisting of core courses, a consulting course, and electives as detailed below.

• ST 779: Advanced Probability
• ST 793: Advanced Statistical Inference
• ST 758: Computation for Statistical Research
• ST 841: Statistical consulting (unless student has taken ST 542 in our department)
• Ethics sequence: Ethics in Statistics (currently offered as ST810-004) and PHI 816 Research Ethics. These partial-semester courses are designed to be taken together.

Nine hours of statistics Ph.D. electives are required from the following list:

• ST 732: Longitudinal Data Analysis
• ST 733: Spatial Statistics
• ST 740: Bayesian Inference and Analysis
• ST 746: Stochastic Processes
• ST 790: Advanced Special Topics

Three hours of supporting electives are also required — a 500 or 700 level course in either statistics or another department with material relevant to the student’s plan of work. Examples include ST 520, ST 733, ST 744 and ST 745.

Other Information

Learn about Ph.D. graduate advisory committee requirements.

Learn about Ph.D. examination requirements.

See the Graduate School’s site for a list of required documents that must be received before graduation.

All questions regarding the graduate programs should be emailed to Graduate Services.

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• Dissertation Areas and Joint PhD Programs
• PhD Career Outcomes
• PhD Proposals and Defenses
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• 100 Years of Pioneering Research
• Rising Scholars Conference
• Yiran Fan Memorial Conference
• Frequently Asked Questions
• PhD in Accounting
• PhD in Behavioral Science

PhD in Econometrics and Statistics

• PhD in Economics
• PhD in Finance
• PhD in Management Science and Operations Management
• PhD in Marketing
• Joint Program in Financial Economics
• Joint Program in Psychology and Business
• Joint PhD/JD Program

The Econometrics and Statistics Program provides foundational training in the science of learning from data towards solving business problems. Our students engage in extensive collaborative research on cutting-edge theory in Econometrics, Statistics and Machine Learning as well in applied research from a variety of fields within Booth (such as finance, marketing or economics).

Our program builds on a long tradition of research creativity and excellence at Booth.

Our PhD students become active members of the broad, interdisciplinary and intellectually stimulating Booth community. The program is ideal for students who wish to pursue an (academic) research career in data-rich disciplines, and who are motivated by applications (including but not limited to economics and business). As our PhD student, you will have a freedom to customize your program by combining courses at Booth (including business-specific areas such as marketing, finance or economics) with offerings at our partnering departments at the University of Chicago (Department of Statistics and Kenneth C. Griffin Department of Economics). You will acquire a solid foundation in both theory and practice (including learning theory, Bayesian statistics, causal inference or empirical asset pricing).

Our Distinguished Econometrics and Statistics Faculty

Chicago Booth’s Econometrics and Statistics faculty are committed to building strong collaborative relationships with doctoral students. We serve as research advisors and career mentors. Major areas of departmental research include: learning theory; causal inference; machine learning; Bayesian inference; decision theory; graphical models; high dimensional inference; and financial econometrics.

Bryon Aragam

Assistant Professor of Econometrics and Statistics and Robert H. Topel Faculty Scholar

Nabarun Deb

Assistant Professor of Econometrics and Statistics

Christian B. Hansen

Wallace W. Booth Professor of Econometrics and Statistics

Tetsuya Kaji

Associate Professor of Econometrics and Statistics and Richard Rosett Faculty Fellow

Mladen Kolar

Associate Professor of Econometrics and Statistics

Tengyuan Liang

Professor of Econometrics and Statistics and William Ladany Faculty Fellow

Nicholas Polson

Robert Law, Jr. Professor of Econometrics and Statistics

Veronika Rockova

Professor of Econometrics and Statistics, and James S. Kemper Foundation Faculty Scholar

Jeffrey R. Russell

Alper Family Professor of Econometrics and Statistics

Ekaterina (Katja) Smetanina

Assistant Professor of Econometrics and Statistics and Asness Junior Faculty Fellow

Panagiotis Toulis (Panos)

Associate Professor of Econometrics and Statistics, and John E. Jeuck Faculty Fellow

Dacheng Xiu

Professor of Econometrics and Statistics

A Network of Support

Booth’s Econometrics and Statistics group has been partnering with several (data science and interdisciplinary) research centers and institutes that facilitate the translation of research into practice. Through these venues, our PhD students can foster a strong research community and find additional resources including elective courses, funding for collaborative student work, and seminars with world-renowned scholars.

Data Science Institute at the University of Chicago The Data Science Institute executes the University of Chicago’s bold, innovative vision of Data Science as a new discipline by advancing interdisciplinary research, partnerships with industry, government, and social impact organizations. Center for Applied Artificial Intelligence The Center for Applied AI incubates transformative projects that fundamentally shape how humans use AI to interact with each other and the world. The Center’s innovative research uses machine learning and behavioral science to investigate how AI can best be used to support human decision-making and improve society. Toyota Technological Institute at Chicago Toyota Technological Institute at Chicago (TTIC) is a philanthropically endowed academic computer science institute, dedicated to basic research and graduate education in computer science. Its mission is to achieve international impact through world-class research and education in fundamental computer science and information technology. The Institute is distinctive to the American educational scene in its unique combination of graduate education and endowed research.

The Becker Friedman Institute for Economics With a mission of turning evidence-based research into real-world impact, the Becker Friedman Institute brings together the University of Chicago’s economic community. Ideas are translated into accessible formats and shared with world leaders tasked with solving pressing economic problems. Committee on Quantitative Methods in Social, Behavioral and Health Sciences This is an interdisciplinary community of faculty and students interested in methodological research in relation to applications in social, behavioral, and health sciences. The goals are to create an intellectual niche, exchange research ideas, facilitate research collaborations, share teaching resources, enhance the training of students, and generate a collective impact on the University of Chicago campus and beyond. The Institute for Data, Econometrics, Algorithms, and Learning The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL) is a multi-discipline (computer science, statistics, economics, electrical engineering, and operations research) and multi-institution (Northwestern University, Toyota Technological Institute at Chicago, and University of Chicago) collaborative institute that focuses on key aspects of the theoretical foundations of data science. The institute will support the study of foundational problems related to machine learning, high-dimensional data analysis and optimization in both strategic and non-strategic environments.

The Fama-Miller Center for Research in Finance Tasked with pushing the boundaries of research in finance, the Fama-Miller Center provides institutional structure and support for researchers in the field. James M. Kilts Center for Marketing The Kilts Center facilitates faculty and student research, supports innovations in the marketing curriculum, funds scholarships for MBA and PhD students, and creates engaging programs aimed at enhancing the careers of students and alumni.

Scholarly Publications

Our PhD students' research has been published in top journals including Econometrica, Journal of Royal Statistical Society, Journal of Econometrics, Neural Information Processing Systems and Journal of Machine Learning Research. Below is a recent list of publications and working papers authored by our PhD students. Modeling Tail Index with Autoregressive Conditional Pareto Model Zhouyu Shen, Yu Chen and Ruxin Shi, Journal of Business and Economic Statistics, (40) 2022 Online Learning to Transport via the Minimal Selection Principle Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang, Chris Ryan, Proceedings of 35th Conference on Learning Theory (COLT), (178) 2022 FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting Boxin Zhao, Samuel Wang and Mladen Kolar, Journal of Machine Learning Research, (23) 2022 Approximate Bayesian Computation via Classification Yuexi Wang, Tetsuya Kaji and Veronika Rockova, Journal of Machine Learning Research (In press), 2022 Reversible Gromov-Monge Sampler for Simulation-Based Inference YoonHaeng Hur, Wenxuan Guo and Tengyuan Liang, Journal of the American Statistical Association (R&R). 2021. Data Augmentation for Bayesian Deep Learning Yuexi Wang, Nicholas Polson and Vadim Sokolov, Bayesian Analysis (In press), 2022 Pessimism Meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning Boxiang Lyu, Zhaoran Wang, Mladen Kolar and Zhuoran Yang, In Proceedings of the 39th International Conference on Machine Learning (ICML), (162) 2022 Optimal Estimation of Gaussian DAG Models Ming Gao, Wai Ming Tai and Bryon Aragam, International Conference on Artificial Intelligence and Statistics (AISTATS), (151) 2022 Multivariate Change Point Detection for Heterogeneous Series Yuxuan Guo, Ming Gao, and Xiaoling Lu, Neurocomputing, (510) 2022 Disentangling Autocorrelated Intraday Returns Rui Da and Dacheng Xiu, Journal of Econometrics (R&R), 2021 When Moving-Average Models Meet High-Frequency Data: Uniform Inference on Volatility Rui Da and Dacheng Xiu, Econometrica, (89) 2021 Efficient Bayesian Network Structure Learning via Local Markov Boundary Search Ming Gao and Bryon Aragam, Advances in Neural Information Processing Systems (NeurIPS), (34) 2021 Structure Learning in Polynomial Time: Greedy Algorithms, Bregman Information, and Exponential Families Goutham Rajendran, Bohdan Kivva, Ming Gao and Bryon Aragam, Advances in Neural Information Processing Systems (NeurIPS), (34) 2021 Variable Selection with ABC Bayesian Forests Yi Liu, Yuexi Wang and Veronika Rockova, Journal of the Royal Statistical Association: Series B, (83) 2021  A Polynomial-time Algorithm for Learning Non-parametric Causal Graphs Ming Gao, Yi Ding, and Bryon Aragam, Advances in Neural Information Processing System (NeurIPS), (33) 2020 Uncertainty Quantification for Sparse Deep Learning Yuexi Wang and Veronika Rockova, International Conference on Artificial Intelligence and Statistics (AISTATS), (2018) 2020 Direct Estimation of Differential Functional Graphical Models Boxin Zhao, Samuel Wang and Mladen Kolar, Advances in neural information processing systems (NeurIPS), (32) 2019

The Effects of Economic Uncertainty on Financial Volatility: A Comprehensive Investigation Chen Tong, Zhuo Huang, Tianyi Wang, and Cong Zhang, Journal of Empirical Finance (R&R), 2022

Spotlight on Research

Econometrics and statistics research from our PhD students and faculty is often featured in the pages of Chicago Booth Review.

Is There a Ceiling for Gains in Machine-Learned Arbitrage?

In a recent paper by Chicago Booth’s Stefan Nagel and Dacheng Xiu and Booth PhD student Rui Da, findings suggest that there are limits to statistical arbitrage investment.

How (In)accurate Is Machine Learning?

Three Chicago Booth researchers quantify the likelihood of machine learning leading business executives astray.

Would You Trust a Machine to Pick a Vaccine?

"If we understand why a black-box method works, we can trust it more with our decisions, explains [Booth's] Ročková, one of the researchers trying to narrow the gap between what’s done in practice and what’s known in theory. "

Inside the Student Experience

Damian Kozbur, PhD ’14, says PhD students at Booth have the flexibility to work on risky problems that no one else has examined.

Video Transcript

Damian Kozbur, ’14: 00:01 I went to graduate school in order to develop econometrics tools in conjunction with machine-learning tools in conjunction with economic theory in order to do inference for economic parameters. When you work in high dimensional estimation and you're dealing with problems where the number of variables you're looking at can potentially be in the millions, there's no way to visualize what's going on. Demands now really require that you can handle huge datasets. There's something really satisfying about studying a problem and studying it well. I would say Booth is an excellent place to do it. You have the flexibility to work on really risky problems where you're trying to navigate this landscape that nobody's ever really looked at before. You have an opportunity to dig deeper. You have an opportunity to be rigorous. The faculty is there to help you. They're trying to figure out the same kinds of problems. Things that you figure out cannot always be visualized and it cannot always be easily understood. That doesn't necessarily mean that it's not practical or not useful.

Damian Kozbur, ’14: 01:08 There's an incredible explosion in terms of the amount of data we have on everything. There is an incredible explosion in terms of our understanding of high dimensional econometrics. If you're doing innovative work right now, it will have an impact.

Current Econometrics and Statistics Students

PhD students in econometrics and statistics apply statistical methods to a wide range of business problems, from the effectiveness of machine-learning tools to video-game preferences. Our graduates go on to work in high-profile institutions, generally in academia, finance, or data science.

Current Students

Y ifei Chen Rui Da

Chaoxing Dai

Wenxuan Guo

Shuo-Chieh Huang

Shunzhuang Huang So Won Jeong

Boxiang (Shawn) Lyu Edoardo Marcelli

Zhouyu Shen

Shengjun (Percy) Zhai

Current Students in Sociology and Business

Jacy Anthis

Program Expectations and Requirements

The Stevens Program at Booth is a full-time program. Students generally complete the majority of coursework and examination requirements within the first two years of studies and begin work on their dissertation during the third year. For details, see General Examination Requirements by Area in the Stevens Program Guidebook below.

Download the 2023-2024 Guidebook!

PhD in Statistics

The PhD in Statistics prepares students for a career pursuing research in either academia or industry.  The program provides rigorous classroom training in the theory, methodology, and application of statistics, and provides the opportunity to work with faculty on advanced research topics over a wide range of theory and application areas.

Degree Requirements

The requirements for obtaining an PhD in Statistics can be found on the associated page of the BU Bulletin .

• Courses : All Ph.D. students in the statistics track must take the following two-semester sequences: MA779 and MA780 (Probability Theory I and II), MA781 (Estimation Theory) and MA782 (Hypothesis Testing), and MA750 and MA751 (Advanced Statistical Methods I and II). In addition to the required courses listed on the BU Bulletin page, the remaining coursework can be chosen from the graduate courses we offer here . Students can also request to use courses from other departments to satisfy some of these requirements. Please contact your advisor for more information about which courses can be used in this way. All courses must be passed with a grade of B- or higher.
• PhD Exam in Probability: This exam covers the material covered in MA779 and MA780 (Probability Theory I and II).
• PhD Exam in Mathematical Statistics: This exam covers material covered in MA781 (Estimation Theory) and MA782 (Hypothesis Testing).
• PhD Exam in Applied Statistics: This exam covers the same material as the M.A. Applied comprehensive exam (see the MA degree requirements ) and is offered at the same time, except that in order to pass it at the PhD level a student must correctly solve all four problems.
• Dissertation and Final Oral Examination: This follows the GRS General Requirements for the Doctor of Philosophy Degree .

Admissions information can be found on the BU Arts and Sciences PhD Admissions website .

Financial Aid

Our department funds our PhD students through a combination of University fellowships, teaching fellowships, and faculty research grants. More information will be provided to admitted students.

More Information

Please reach out to us directly at [email protected] if you have further questions.

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PhD in Statistics

Study with leading statisticians at a world-class university

  Applications for entry 2024/25 are open  

Funding deadlines: 15 January 2024 (Applications received by this date will be considered for available studentships; we may also be able to consider applications received by the end of March for funding, but this is not guaranteed.) Final application deadline: 23 May 2024  

How to Apply

A PhD offers the chance to undertake a substantial piece of supervised work that is worthy of publication and which makes an original contribution to knowledge in a particular field. Our PhD programme is designed to produce professional social scientists, well versed in a range of advanced statistical techniques and methods, in addition to having an in-depth knowledge of your topic of interest. 

The Department of Statistics is one of the world's leading centres of quantitative methods in the social sciences and has long been home to some of the world's most famous and innovative statisticians. Today, the department has an international reputation for the development of statistical methodology that has grown from our long history of active contributions to research and teaching in statistics. 

Our core research areas are:

• Data science
• Probability in finance and insurance
• Social statistics
• Time series and statistical learning

If you have any questions about our MPhil/PhD Statistics programme, please  email the Research Manager .  

Research environment

The Department of Statistics at LSE is one of the oldest and most distinguished in the UK. It has a rich research portfolio covering core areas of statistical inference and real applications, particularly in the economic, financial and actuarial, social and industrial arenas. The close collaboration with other LSE departments, our London location and strong international partnerships are reflected in the research life of the Department of Statistics through the members of staff, PhD students, postdoctoral research fellows and the thriving visitor and seminar programmes.

Research in the department is concentrated in the following areas and PhD proposals should normally be linked to one of these areas:

Data Science

Research in the data science area is concerned with the development of new machine learning and statistical methods, and their applications. The areas of applications include the design of novel methods for understanding user behaviour, analysis of social data, modelling and inference for information cascades and epidemic processes that arise in social networks and biomedical applications, as well as algorithms for development of next-generation artificial intelligence systems.

Possible areas of research include:

• Bayesian inference and predictions
• Functional data analysis
• High-dimensional statistics
• Machine and statistical learning for relational data
• Network data models, inference and predictions
• Optimisation and machine learning
• Reinforcement learning
• Statistical learning methods in precision medicine
• Statistical models and inference for ranking data
• Stochastic models of epidemic processes
• Stochastic optimisation methods
• Stochastic processes in econometrics and finance

For more information about potential supervisors and their areas of interest, visit the Data Science research group .

Probability in Finance and Insurance

PhD research in probability in finance and insurance encompasses many aspects of the discipline. Methodological and theoretical research is mainly guided by applications with the aid of both academic and industrial experts, covering topics of modern stochastic finance with an emphasis on insurance and financial mathematics.  Applications include pricing and hedging exotic products, counterparty risk, portfolio optimisation, risk management and insurance, risk transfer and securitisation, etc. 

Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary. 

Suggested research areas of PhD research projects include:

• Energy markets
• Excursions of Lévy processes and applications in finance and insurance
• Financial market with frictions
• Information asymmetry
• Interface between insurance and finance
• Lévy processes
• Optimal stopping
• Point processes in insurance and credit risk
• Quantile options and options based on occupation times
• Stochastic analysis and its applications in financial mathematics
• Stochastic control and analysis of partial differential equations in mathematical finance

This list is indicative only and by no means exhaustive. For more details about supervisors and their areas of research interests, please see the  Probability in Finance and Insurance research group . You will find links to the web pages of individual members of staff here. If you are interested in applying to undertake PhD research in probability in finance and insurance, you are welcome to contact one of these members of staff regarding a suitable topic for your research proposal. 

Social Statistics

PhD programmes of study in social statistics typically include both methodological development and the application of statistical methods to a social science field or to address new developments in social data, such as in sample surveys or social networks. Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary. 

• Analysis of complex survey data
• Disclosure risk assessment and statistical disclosure control
• Estimation from survey data (and related data), taking account of nonresponse and using auxiliary information
• Latent transition and latent class models for modelling diagnostic tests
• Latent variable models and structural equation models for categorical data
• Longitudinal data analysis, especially event history (survival) analysis and dynamic panel models
• Modelling response strategies and detection of outliers in educational and behavioural sciences
• Multilevel simultaneous equations modelling of correlated social processes

For more details about potential supervisors and their areas of interest, visit the  Social Statistics research group . If you are interested in applying to undertake PhD research in social statistics, you are welcome to contact one of these members of staff regarding a suitable topic for your research proposal.

Time Series and Statistical Learning

PhD research in time series and statistical learning encompasses many aspects of these disciplines. We are keenly involved in both theoretical developments and practical applications. Current areas of interest include time series (including high-dimensional and non-stationary time series), data science and machine learning, networks (including dynamical networks), high-dimensional inference and dimension reduction, statistical methods for ranking data, spatio-temporal processes, functional data analysis, shape-constrained estimation, multiscale modelling and estimation and change-point detection.

Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary.

Suggested PhD research areas include:

• Automating statistical advice
• Change detection for complex data
• Dimension reduction and factor modelling
• Estimation of stochastic volatility models
• Financial econometrics
• Functional data analysis including functional time series
• High-dimensional time series analysis
• High-dimensional variable selection
• Infectious disease modelling
• Inference for sequential data including change detection in multiple data streams
• Network time series analysis
• Nonparametric and semiparametric regression
• Non-stationary time series analysis
• Reinforcement learning for time-dependent data
• Robust statistical analysis for high-dimensional data
• Shape-constrained methods
• Spatial econometrics modelling
• Spatio-temporal modelling
• Statistical analysis of high-dimensional multi-type recurrent events

For more information, please see the  Time Series and Statistical Learning research group . 

PhD Programme Structure

PhD Your application

PhD Funding Opportunities

FAQs Frequently Asked Questions

PhD Handbook Our guide for current students

Past PhD students A listing of our PhD graduates

Visiting Research Students How to apply

PhD Academy A dedicated space for PhD students

Graduate Career Destinations What our PhD graduates have done next

Statistics Spotlight: Steve Howard Ph.D. ‘10

Steve Howard’ s early interest lay in programming and was nowhere near statistics. As a high school student, he attended coding camps and competitions. Howard’s internships and other opportunities focused on programming and computer science throughout his education. Notably, Howard never found much interest in statistics growing up despite living under the same roof as a statistician. 

“My mom was actually a biostatistician working in healthcare, doing what I now appreciate as pretty interesting work on medical studies, but it's funny because I was never really interested in what she was doing growing up,” Howard said.

“Basically like any startup or any tech company, we were running A/B tests, and no one at the company knew anything about statistics or experimentation. So I said, ‘I'll learn about it,’ and I ordered some textbook on statistics from Amazon and started reading. I started asking my mom questions and realized, ‘oh, this is kind of useful.’”

Howard would go on to teach himself more about statistics and machine learning, and it was clear his interests were piqued.

“I found it fascinating that in this field, even the most basic questions, like how to form a confidence interval for a conversion rate, did not seem to have straightforward answers.”

Despite not having a background in statistics, Howard was accepted into the Ph.D. program and enrolled at Berkeley in 2015. In the year between acceptance and enrollment he studied statistics to prepare himself, but once Howard started, he quickly realized that his thirst for knowledge was strong.

“Working in industry, you don’t get to spend that time learning and investing in yourself. It almost feels like an indulgent luxury just to spend all day every day reading books and thinking about stuff I want to think about and teaching myself or learning.”

Howard quickly found a collaborative department with a collegial cohort of graduate students and a rigorous academic environment with faculty willing to share their expertise. Advised by Adjunct Professor Jon McAuliffe  (Ph.D. 2005) and former faculty member Jasjeet Sekhon , his thesis was titled “Sequential and Adaptive Inference Based on Martingale Concentration.” When reflecting on his time at Berkeley, Howard lauded how his degree gave him a solid foundation from which to solve real-life problems.

“That is what is great about going through the challenges of earning a Ph.D. at a place like Berkeley Statistics. I was able to get comfortable thinking from first principles rather than just applying a prepackaged kind of method to solving problems.”

Following graduation, Howard joined McAuliffe at the Voleon Group , one of the leading quantitative investment management firms, where he is a member of the research staff. Howard lives with his wife and two daughters in Orinda and serves on the Statistics Alumni Advisory Board .  

-Alex Coughlin

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Statistics PhD Program

Fall 2024 courses, important dates.

        First day of classes: Monday, August 26, 2024

        No classes (Labor Day): Monday, September 2, 2024

        No BST classes (Thanksgiving): Wednesday, November 27 - Friday, November 29, 2024

        Last day of classes : Monday, December 9, 2024

        Final exam period : Friday, December 13 - Wednesday, December 18, 2024

**Please check back later for course meeting times and locations.**

Course Offerings

For students matriculated in the Department of Biostatistics and Computational Biology      BST 590 - Supervised Teaching      BST 591 - Reading Course at the PhD Level*      BST 592 - Supervised Statistical Consulting         BST 595 - Research at the PhD Level       BST 999 - Doctoral Dissertation * maximum of 6 credits per BST 591 course

Please direct questions to: [email protected]

Last updated: April 4, 2024

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Double the degrees

Article by Nya Wynn Photo by Evan Krape April 02, 2024

UD’s statistics 4+1 program gives Audrey Bufano valuable internship experience while earning both undergraduate and graduate degrees

The University of Delaware’s 4+1 programs provide students with a fast-tracked path to both an undergraduate and master’s degree. Although relatively new, the B.S./M.S. in Statistics 4+1 program is launching Blue Hens into a wide range of careers. 

With the high demand for statisticians in seemingly every major industry, two degrees in five years seemed like a rigorous but financially smart opportunity for students like Audrey Bufano. Now in her graduate student year, the Wilmington, Delaware, native earned her bachelor of science in statistics degree in spring 2023 and jumped right into her graduate coursework.

In the master’s year, Blue Hens choose between non-thesis options, an internship or a thesis to complete their second degree. Bufano took the internship path, landing a position with FMC, a global agricultural sciences company only a few miles from UD’s Newark campus.

“The internship program that UD did with the 4+1 program is like a substitution for a thesis,” Bufano said. “It’s a great opportunity for getting hands-on and real-world experience.”

Bufano honed her coding knowledge, using computer programming to help companies make informed business decisions. 

“Audrey’s current work focuses on preparing summary reports for studies done by weed scientists, plant pathologists and nematologists to understand how product performance is impacted by different environmental factors,” said Melissa Ziegler, Bufano’s supervisor at FMC. 

Bufano is working with an herbicide discovery group on which compounds they should use for the best possible crop yield. This type of work is critical to developing and improving crop protection products. 

Not only is she strengthening her coding skills through this internship, she also appreciates learning how a corporate environment functions.

“That has been really beneficial — you don’t necessarily get that in school,” Bufano said.

Throughout their undergraduate statistics coursework, students take several computer science, economics and critical thinking courses on top of their core statistics courses.

“All of that experience with data from a theoretical standpoint and also on the applied side that [Audrey] is getting in this program positioned her really well for an internship there at FMC,” said Bryan Crissinger , senior instructor in the Department of Applied Economics and Statistics . “Any sort of skills that would require working with data — research and product development lands squarely in Audrey’s wheelhouse.” 

Working with data in any context — from working with Excel formulas to learning complex coding and programming — prepares students for the 4+1 program and lifelong careers in statistics. 

“To excel in statistics, students need to have an interest in manipulating data and be comfortable working within the intersection of math and data science,” Crissinger said.  

In addition to being comfortable with manipulating data and statistical thinking, Ziegler also feels that people within the field need to have a more holistic view of the problems they’re tasked with. 

“What separates good from great is the ability to understand the business or scientific context of a problem, identify the critical questions that should be answered and then communicate the results to a non-technical audience,” Ziegler said.  

During her time at UD, Bufano enhanced her statistics degree with a minor in computer science. She found the applied database management courses that dive into the SAS coding language particularly useful. 

“That’s 90% of what I do on a daily basis at FMC,” Bufano said. “Having a computer science minor is valuable because, wherever I go, it will involve computer programming. I don’t yet know what language I’ll use or in what context, but it will involve processing and analyzing data all day.” 

On top of her computer science experience, Bufano learned a great deal about the business. 

“I took business development courses that helped with soft skills,” Bufano said. “I networked and developed personal relationships with professors. Once a UD faculty member knows you and what you’re interested in, then they connect you with others who may be looking to hire in that field.”  

In the future, Bufano hopes to branch out and explore how she can use her skills in statistical analysis across career fields such as scientific research or sports. 

“I know that UD statistics graduates find it pretty easy to come out and get jobs just because of the skill set they have,” Crissinger said. “It’s a skill set that is in-demand these days.”

As Bufano approaches the final semester of her UD academic career, she reflected on her educational and personal journey.

“A lot of great experiences built me into the person I am today versus the one I was four and a half years ago when I started my college career; UD has provided a lot for me,” Bufano said. “I am a big efficiency person, which the 4+1 is great for, and I just appreciate this opportunity very much.”

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