phd statistics scope

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]

Quick Links

• Undergraduate Programs
• M.A. Statistics Programs
• M.A. in Mathematical Finance
• M.S. in Actuarial Science
• M.A. in Quantitative Methods in the Social Sciences
• M.S. in Data Science
• PhD Program
• BA/MA Program
• Department Directory
• Faculty Positions
• Founder’s Postdoctoral Fellowship Positions
• Staff Hiring
• Joint Postdoc with Data Science Institute
• Department News
• Department Calendar
• Research Computing

Upcoming Events

Department of Statistics and Data Science

Ph.d. program.

Fields of study include the main areas of statistical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory (stochastic processes, asymptotics, weak convergence), information theory, bioinformatics and genetics, classification, data mining and machine learning, neural nets, network science, optimization, statistical computing, and graphical models and methods.

With this background, graduates of the program have found excellent positions in universities, industry, and government. See the list of alumni for examples.

Secondary Menu

Ph.d. program.

Statistical Science at Duke is the world's leading graduate research and educational environment for Bayesian statistics, emphasizing the major themes of 21st century statistical science: foundational concepts of statistics, theory and methods of complex stochastic modeling, interdisciplinary applications of statistics, computational statistics, big data analytics, and machine learning. Life as a Ph.D. student in Statistical Science at Duke involves immersion in a broad range of research experiences and emphasizes conceptual innovation, as well as building a deep and broad foundation in theory and methods.

Coupled with our core emphases in modeling, computation and the methodologies of modern statistical science is a broad range of interdisciplinary relationships with many other disciplines (biomedical sciences, environmental sciences, genomics, computer science, engineering, finance, neuroscience, social sciences, and others). The rich opportunities for students in interdisciplinary statistical research at Duke are complemented by opportunities for engagement in research in summer projects with nonprofit agencies, industry, and academia.

• Our Mission
• Diversity, Equity, and Inclusion
• International Recognition
• Department History
• Past Recipients
• Considering a Statistical Science major at Duke?
• Careers for Statisticians
• Typical Pathways
• Applied Electives for BS
• Interdepartmental Majors
• Minor in Statistical Science
• Getting Started with Statistics
• Student Learning Outcomes
• Study Abroad
• Course Help & Tutoring
• Past Theses
• Research Teams
• Independent Study
• Transfer Credit
• Conference Funding for Research
• Statistical Science Majors Union
• Duke Actuarial Society
• Duke Sports Analytics Club
• Trinity Ambassadors
• Frequently Asked Questions
• Summer Session Courses
• How to Apply
• Financial Support
• Graduate Placements
• Living in Durham
• Preliminary Examination
• Dissertation
• English Language Requirement
• TA Guidelines
• Progress Toward Completion
• Ph.D. Committees
• Terminal MS Degree
• Student Governance
• Program Requirements
• PhD / Research
• Data Science & Analytics
• Health Data Science
• Finance & Economics
• Marketing Research & Business Analytics
• Social Science & Policy
• Admission Statistics
• Master's Thesis
• Portfolio of Work
• Capstone Project
• Statistical Science Proseminar
• Primary Faculty
• Secondary Faculty
• Visiting Faculty
• Postdoctoral Fellows
• Ph.D. Students
• M.S. Students
• Theory, Methods, and Computation
• Interdisciplinary Collaborations
• Statistical Consulting Center
• Alumni Profiles
• For Current Students
• Assisting Duke Students
• StatSci Alumni Network
• Ph.D. Student - Alumni Fund
• Our Ph.D. Alums
• Our M.S. Alums
• Our Undergrad Alums
• Our Postdoc Alums

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.

• Utility Menu

Department of Statistics

4c69b3a36a33a4c1c5b5cd3ef5360949.

• Open Positions
• PhD Program

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.

PhD Program Requirements

PhD Program Admissions Process

• PhD Admissions FAQ
• AM in Statistics
• Stat 300: Research in Statistics
• Stat 303: The Art and Practice of Teaching Statistics

Need Assistance?

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

PhD Admissions

Our PhD program welcomes students from a broad range of theoretical, applied, and interdisciplinary backgrounds, and provides rigorous preparation for a future career in statistics, probability, or data science. Our top-ranked program usually takes 5 years to complete. PhD theses are diverse and varied, reflecting the scope of faculty research interests, with many students involved in interdisciplinary research. There are also Designated Emphases in Computational and Genomic Biology; Computational Precision Health; and Computational Science and Engineering if one chooses to take a more concentrated approach.

Our department has been a leader in embracing machine learning and data science. We helped found the Division of Computing, Data Science, and Society (CDSS) , which was launched in 2019 under Associate Provost Jennifer Chayes and continues to strengthen both our interdisciplinary ties and foundational research. Our graduates go on to solve impactful problems in academia, industry, and non-profits, informing consequential decisions such as election auditing, medical treatment, police reform, and scientific reproducibility, and developing elegant mathematical tools for understanding networks, genetics, and language, among other areas.

Financial Support

Program information, the application for fall 2024 is closed., the fall 2025 phd application will open in september 2024., we do not offer spring admissions. , for fall 2024 gre is not required and will not be accepted. subject tests are optional..

• Youth Program
• Wharton Online

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

The Wharton School, University of Pennsylvania Academic Research Building 265 South 37th Street, 3rd & 4th Floors Philadelphia, PA 19104-1686

Phone: (215) 898-8222

• Contact Information
• Course Descriptions
• Course Schedule
• Doctoral Inside: Resources for Current PhD Students
• Penn Career Services
• Apply to Wharton
• Financial Aid

Our websites may use cookies to personalize and enhance your experience. By continuing without changing your cookie settings, you agree to this collection. For more information, please see our University Websites Privacy Notice .

College of Liberal Arts and Sciences

Department of Statistics

Ph.d. in statistics.

The Doctor of Philosophy (Ph.D.) in Statistics provides students with rigorous training in the theory, methodology, computation, and application of statistics.

View Admissions Requirements

Program Details

UConn statistics Ph.D. students work closely with faculty on advanced research topics over a wide range of theory and application areas. They also engage with an active community of scholars and students who engage with peers on campus and with professional networks beyond UConn.

Through their coursework, mentorship, and community engagement experiences, our students develop diverse skills that allow them to collaborate and innovate with researchers in applied fields. Graduates of our program go on to high profile positions in academia, industry, and government as both statisticians and data scientists.

Academic Requirements

UConn’s Ph.D. in Statistics offers students rigorous training in statistical theories and methodologies, which they can apply to a wide range of academic and professional fields. Starting in their second year, Ph.D. students establish an advisory committee, consisting of a major advisor and two associate advisors. Together they develop an individualized plan of study based on the students career goals and interests.

All Ph.D. students are required to complete:

• A sequence of required core courses and elective courses from another field of study.
• A qualifying examination and general examination.
• A dissertation.

View full degree requirements

Students entering the program with a bachelor’s degree are typically required to take 16 to 18 courses to earn a Ph.D. in Statistics.

Core Courses

The following core courses are required for all Ph.D. students:

• STAT 5585 and 5685. Mathematical Statistics.
• STAT 5505 and 5605. Applied Statistics.
• STAT 5725 and 5735. Linear Models.
• STAT 6315 and 6515. Theory of Statistics.
• STAT 6325 and 6894. Measure Theory and Probability Theory.
• STAT 5515. Design of Experiments.
• STAT 5095. Investigation of Special Topics.

Each core course carries three credits, except for the one-credit STAT 5095, for a total of 34 credits. Additional credits can be earned from the list of elective courses.

Elective Courses

In general, Ph.D. students are required to elect one or two courses from other departments. However, it is sufficient to take one graduate-level course from the Department of Mathematics. Ph.D. students are also encouraged to take courses in computer science and in application areas such as biology or economics. The elective course(s) must be approved by the student’s major advisor.

Under certain circumstances, a major advisor can exempt their student from the above requirement, if the student has had internships or a research assistantship in interdisciplinary areas.

Browse the UConn graduate course catalog.

Financial Aid

The Department expects Ph.D. students to finish their studies within four years. For students arriving without an MS degree in mathematics or statistics, the Department may provide up to five years of financial support. For those arriving with such a degree, the Department may provide up to four years of financial support.

In order to receive continuous support, Ph.D. students should take at at least nine credits per semester until taking the Ph.D. qualifying exam.

Learn more about financial aid

February 1 (early deadline) April 1 (final deadline)

Please apply by February 1 if you wish to be considered for financial aid.

Individuals with a bachelor’s degree in any major, with a background in mathematics and statistics, are encouraged to apply.

International students must consult with UConn International Student and Scholar Services for visa rules and University requirements.

Full Admissions Requirements

  Please note: The Department does not offer a joint MS/Ph.D. program. Current UConn students enrolled in a statistics master’s program who wish to pursue the Ph.D. in Statistics must reapply to the Graduate School.

For questions about the Ph.D. in Statistics, please contact:

Vladimir Pozdnyakov

Professor and Director of Graduate Admission [email protected]

PhD in Statistics

The PhD degree in statistics is designed for students who wish to pursue a career in statistics research in academia, government, or industry. The curriculum is designed to provide a strong in-depth and broad training in statistical theory, methodology, computation, and applications. Students begin their research experience in the first year and participate in on- or off-campus internships in the second year. These provide a well-rounded, solid education for graduates to assume and advance their roles as university professors, senior statisticians, or data scientists.

Dissertations

While PhD students are engaged in research from the first year, they formally begin their dissertation work after completing their doctoral preliminary exams. Dissertations may be oriented toward applied statistics, computational methods, theoretical statistics, or probability. It typically takes one to two years to complete and defend the dissertation work. The dissertation is expected to be of publishable quality in reputable academic journals. Almost all PhD students complete the degree in five years.

College Resources for Graduate Students

Visit CLA’s website for graduate students to learn about collegiate funding opportunities, student support, career services, and more.

Student Services      Career Services     Funding & Support

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
• Intel Corporation
• Berry Consultants

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  

• Graduate Studies

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. 

Doctoral Program - Coursework

PhD students register for 10 units in each of the autumn, winter and spring quarters. Most courses offered by the department for PhD students are three units, including the core courses of the first year program. In addition to regular lecture courses on advanced topics, reading courses in the literature of probability and the literature of statistics are available each quarter. Students working on their dissertation may register for up to 10 units of directed research in each quarter. Students should also register for selected courses outside the statistics department in order to fulfill the  breadth requirement .

Prerequisites

Equivalents of Math 113, Math 115; Stats 116, Stats 200; CS 106A. (Descriptions of these courses may be viewed on Stanford's  ExploreCourses  course listings pages.

Previous experience has shown that before starting the core courses students need to have mastered the material in the prerequisite courses (or their equivalents at other universities), as demonstrated by very strong and relatively recent grades. Where this background is missing or not recent, admission to the PhD program will involve working with the Graduate Director to design an individual program to make up the necessary courses.

Core Courses

Statistics 300A, 300B and 300C systematically survey the ideas of estimation and of hypothesis testing for parametric and nonparametric models involving small and large samples.

Statistics 305A is concerned with linear regression and the analysis of variance. Statistics 305B and 305C survey a large number of modeling techniques, related to but going significantly beyond the linear models of 305A.

Statistics 310A, 310B and 310C are measure-theoretic courses in probability theory, beginning with basic concepts of the law of large numbers, and martingale theory.

Although the content of the first year core courses is specified by the department, the order in which topics are studied and details of the presentation are left to the instructor and will vary from year to year. Unusually well prepared students may place out of Statistics 305A. Students who do not have a sufficient mathematics background can, with approval from the Graduate Director, take the 310 series after the first year. All core courses must be taken for a letter grade.

Literature/Work In Progress Course

Stats 319 is a literature course in statistics and probability that is offered each quarter. The course is generally taken by students in the second and third years, and may be taken repeatedly. It serves two connected purposes:

• to expose students to a variety of topics of current research interest, for example, to help identify dissertation topics. Students are expected to read a number of articles and to write a short paper related to the reading that is presented to the class. The paper can be a synthesis of the reading material, or it may mark the beginning of research in the area. Reading assignments are made in consultation with any faculty member, especially the course instructor.
• to fulfill the Work in Progress requirement. Each post-quals and pre-orals student gives a 50 minute talk once a year. This requirement gives the student practice in giving and receiving feedback on talk technique, and keeps the department informed on the student's work. The talk can be on dissertation work in progress, on an ancillary project (consulting, RA work), or on a selection of papers that the student has recently read. The instructor of the literature course, along with the student's course peers, provide feedback on the talk, and can also provide guidance in topic choice where needed.

All students who have passed the qualifying exams but have not yet passed the Dissertation Proposal Meeting must take Stats 319 Literature of Statistics at least once per year.

Advanced Courses (Depth Requirement)

Students are required to complete a depth requirement consisting of a minimum of three courses (nine units) of advanced topics courses offered by the department. Courses for the depth and breadth (see below) requirements must equal a combined minimum of 24 units. Recommended advanced topics courses include the following:

• In troduction to Time Series Analysis (Stats 307)
• In formation Theory and Statistics (Stats 311)
• Advanced Statistical Methods (Stats 314A)
• Modern Applied Statistics: Learning (Stats 315A)
• Modern Applied Statistics: Learning II (Stats 315B)
• Stochastic Processes (Stats 317)
• Modern Markov Chains (Stats 318)
• Machine Learning Methods for Neural Data Analysis (Stats 320)
• Function Estimation in White Noise (Stats 322)
• Multivariate Analysis (Stats 325)
• Topics in Probability Theory (Stats 350)
• Topics in Mathematical Physics (Stats 359)
• Causal Inference (Stats 361)
• Monte Carlo (Stats 362)
• Design of Experiments (Stats 363)
• Statistical Models in Genetics (Stats 367)
• Bayesian Statistics (Stats 370)
• Convex Optimization I (EE 364A)
• Convex Optimization II (EE 364B)

In any given year only some of these courses will be offered.

These courses are normally taken after the first year and may help students to find dissertation topics.

Consulting Workshop

Students taking the consulting workshop, Stats 390, provide a free consulting service to the Stanford community. Researchers from all areas of the University drop in to discuss their problems. This course allows students to assimilate the material from their first year courses, especially Stats 305A/B/C.

The consulting is executed by teams of students, in which inexperienced students are matched with those more experienced. The course is offered each quarter and may be taken repeatedly. Students are encouraged to participate in the formulation of the consulting problems and in any data analysis which may be involved.

• Diversity and Inclusion
• Faculty Positions
• Location and Maps
• Our Department
• Graduate Programs
• How to Apply
• Online Programs
• Student Resources
• Grad Student Assoc.
• Graduate Student Highlights
• Frequently Asked Questions
• Online Courses
• Prospective Students
• Undergraduate Programs
• Integrated Degree
• Research Opportunities
• Statistics Club
• Syllabus Archive
• Career Development
• Administrative Faculty
• Graduate Students
• M.A.S. Students
• Affiliated Faculty
• Lindsay Assistant Professors
• Online Instructors
• Post-docs / Visitors
• Astrostatistics
• Bayesian Statistics
• Biostatistics & Bioinformatics
• Business Analytics
• Computational Statistics
• Functional Data Analysis
• High Dimensional Data
• Imaging Science
• Social Science
• Spatial and Spatiotemporal Data

• Statistical Network Science
• Statistical and Machine Learning
• Statistics Education
• Giving to Statistics

Dept. of Department of Statistics

Doctorate in statistics, advanced exploration and investigation of the theory and application of statistics and data analysis..

Ph.D. candidates in the Department of Statistics can tailor the program to suit their career goals. Depending on the choice of courses, the program can emphasize theoretical, applied, or computational aspects of statistics and data analysis. Those that choose to emphasize statistical theory would be well prepared to pursue an academic career in research and teaching. An emphasis on applied or computational statistics would be appropriate for a career in government such as at the census bureau, developing methodologies for government surveys, or in industry, assisting with high-level data analytics, research, and in the design and analysis of surveys and experiments.

Ph.D./M.S. Graduate Handbook

The Ph.D./M.S. Graduate Handbook contains policies, suggestions, and links to forms needed for all M.S. and Ph.D. students in Statistics. Topics include requirements for the degree, description of milestones like the comprehensive exam and thesis defense, and required forms for all milestones to a Ph.D. or M.S. in Statistics.

Financial Assistance

All Statistics Ph.D. students in good standing receive year-round financial support in the form of assistantships and fellowships. This support includes a stipend to cover living costs as well as full tuition and academic fees. On admission, Ph.D. students are guaranteed five years of support. Outstanding applicants to the Statistics Ph.D. Program may be nominated by the department for particular fellowships or awards offered by the University or by the Eberly College of Science (e.g., University Graduate Fellowships or Science Achievement Graduate Fellowships). Strong candidates are encouraged to submit a formal application by January 7th to be considered in the first rounds of competition for these awards. 

Applicants and current students also may wish to seek alternative sources of funding, such as a National Science Foundation Graduate Research Fellowship. The Department of Statistics is happy to consider applications from students who are receiving outside funding.

What are the requirements?

For detailed and up-to-date course requirements, please see the degree requirements under the Graduate Bulletin .

Candidacy Examination

At the end of the first year, students will take a qualifying examination to be admitted to Ph.D. candidacy, which is offered at both the beginning and end of the summer. The exam consists of three parts based on theoretical statistics, applied statistics, and probability and Monte Carlo methods. Incoming students with exceptionally strong backgrounds may petition to take any section prior to starting the program, which will excuse them from the respective course work. Students are given two chances at passing each section  

Graduate School Oral Comprehensive Examination

During the second and third year, students are expected to form a Ph.D. committee and schedule the Graduate School oral comprehensive examination. This exam consists of a written component, whose content will be determined and administered by the student's Ph.D. graduate committee, and an oral component, which includes the presentation of a thesis research proposal.

Dissertation

The student must submit and defend a doctoral thesis during the semester they intend to graduate. The oral defense will include a public presentation of the thesis, followed by a private questioning by the committee. Information on the timeline for the graduation semester can be found under the  Grad School's thesis calendar .

Foreign Language

There is no foreign language requirement for a Ph.D. in Statistics.

Dual Degrees

Three dual degrees are currently offered, one with the  Department of Meteorology and Atmospheric Science , one with  Operations Research (OR) , and one with  Social Data Analytics (SoDA) . Interested students may apply to these dual degrees after being accepted to the Ph.D. program in Statistics, though they may make their interest in these programs known while applying to the Statistics program.  For up-to-date degree requirements and details, please see the Graduate Bulletin .

How do I Apply?

Apply to a Graduate Program at Penn State. Additional information can be found here on the Statistics Graduate Program admission requirements, application deadlines, and the Penn State Graduate school requirements and application.

DiscoverDataScience.org

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

Created by aasif.faizal

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

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

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

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

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

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

So why pursue a data science PhD?

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

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

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

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

Topics include:

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

Preparing for a Doctorate Program

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

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

Data Science PhD Programs, Historically

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

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

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

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

Can You Get a PhD in Data Science Online?

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

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

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

Overview of PhD Coursework

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

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

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

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

What’s the core curriculum like?

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

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

Dissertation

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

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

Join professional associations

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

Leverage your social network

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

Kaggle competitions

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

Internships

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

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

Get certified

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

Conferences

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

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

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

What most schools will require when applying:

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

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

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

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

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

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

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

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

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

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

Complete List of Data Science PhD Programs

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

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

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

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

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

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

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

View Course Offerings

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

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

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

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

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

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

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

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

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

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

Coding well in C++ is preferred.

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

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

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

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

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

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

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

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

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

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

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

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

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

View Course Offerings – Clemson

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

View Course Offering

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

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

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

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

Applicants must have a master’s already.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Students in this program work in research groups across campus.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Worcester Polytechnic Institute – Worcester, Massachusetts PhD in Data Science

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

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

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

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

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

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

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

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

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

• Related Programs

• Core Members
• Affiliate Members
• Interdisciplinary Doctoral Program in Statistics
• Minor in Statistics and Data Science
• MicroMasters program in Statistics and Data Science
• Data Science and Machine Learning: Making Data-Driven Decisions
• Norbert Wiener Fellowship
• Stochastics and Statistics Seminar
• IDSS Distinguished Seminars
• IDSS Special Seminar
• SDSC Special Events
• Online events
• IDS.190 Topics in Bayesian Modeling and Computation
• Past Events
• LIDS & Stats Tea
• Interdisciplinary PhD in Economics and Statistics

Students must complete their primary program’s degree requirements along with the IDPS requirements. Statistics requirements  must not unreasonably impact performance or progress in a student’s primary degree program.

The Economics program allows students to replace required courses in Probability and Statistics with more advanced courses by petition.

Special note about integrating IDPS requirements and Economics requirements:

The Doctoral Program in Economics requires students to complete two majors and two minors. IDPS requires one of these major fields to be Econometrics. The IDPS requirement for Computation & Statistics may be used to satisfy one of the minor field requirements in the Doctoral Program in Economics as long as the student’s other minor field is in Economics, and is not a research or ad-hoc minor.

PhD Earned on Completion: Economics and Statistics

IDPS/Economics Chair :  Victor Chernozhukov

*Advanced Research and Communication – 14.192 – no longer requires a focus on Data Analysis. Students pursuing the IDPS will need to keep this focus on Data Analysis to successfully meet IDPS requirements.  The IDPS/Economics Chair will verify that admitted students submit a paper that satisfies the IDPS requirements.

MIT Statistics + Data Science Center Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge, MA 02139-4307 617-253-1764

• Accessibility
• Interdisciplinary PhD in Aero/Astro and Statistics
• Interdisciplinary PhD in Brain and Cognitive Sciences and Statistics
• Interdisciplinary PhD in Mathematics and Statistics
• Interdisciplinary PhD in Mechanical Engineering and Statistics
• Interdisciplinary PhD in Physics and Statistics
• Interdisciplinary PhD in Political Science and Statistics
• Interdisciplinary PhD in Social & Engineering Systems and Statistics
• LIDS & Stats Tea
• Spring 2023
• Spring 2022
• Spring 2021
• Fall – Spring 2020
• Fall 2019 – IDS.190 – Topics in Bayesian Modeling and Computation
• Fall 2019 – Spring 2019
• Fall 2018 and earlier

• Top Colleges
• Top Courses
• Entrance Exams
• Admission 2024
• Study Abroad
• Study in Canada
• Study in UK
• Study in USA
• Study in Australia
• Study in Germany
• IELTS Material
• Scholarships
• Sarkari Exam
• Visual Stories
• College Compare
• Write a review
• Login/ Register
• Login / Register

PhD in Statistics Jobs, Scope, Salary in India

PhD in Statistics graduates earn a relatively better package than few other specialization graduates because this course is a bit tougher so it holds quite more value. Graduates with this course don't lack the opportunity to get a rewarding and successful career. The education that the students are exposed to makes them very employability-oriented, so getting jobs after a PhD Statistics becomes easier. 

Career Prospects and Job Scope for a PhD in Statistics Graduate

Post graduating from a respected college with a PhD in Statistics degree, a PhD in Statistics job opportunities become very wide and far stretched for the students. The average salary of a fresh graduate of this course is INR 3 - 8 LPA (Source: Payscale).

PhD in Statistics jobs for students after completion of PhD in Statistics:

• Econometrician
• Article Writer
• Assistant Professor
• Biostatistician
• Data Analyst
• Research Analyst
• Data Interpreter
• Research Scholar
• Statistician
• Content Developer
• Labor Counselor
• Curriculum Trainer
• Trainee Associate
• Sociologist
• Trainee - Teaching Associates
• Asst. Professor & Lecturer 

Job designation for PhD in Statistics graduates with experience:

• Lead Data Scientist
• Senior Data Scientist Payments
• Senior Data Scientist (Asset & Wealth Management)
• Senior Data Scientist
• Vice President - Data Scientist Lead
• Asset Management

Areas of Recruitment for PhD in Statistics

Factors such as job designation, job location, and sector type can influence salary after PhD in Statistics in India. After finishing a PhD in Statistics, one can do many PhD Statistics jobs where the expertise and knowledge of the students are valued.

PhD in Statistics has scope for working in diverse fields, from the automobile industry to NGOs. The areas of recruitment are:

• Statistical Research
• Indian Statistical Services
• Social Research
• Indian Economic Services
• Govt. Jobs Consulting Firms
• Data Survey Agencies
• Public Sector Undertakings (PSUs)
• Statistical and Economic Bureaus

Salary Packages for PhD in Statistics

The PhD in Statistics salary in India differs based on many factors such as experience, area of specialization, location etc. The average salary of a PhD in Statistics graduate is around INR 3-8 LPA. (Source: Payscale)

The other factors that affect the salary include the type of sector one chooses to work in and the job designation. Graduates can increase their salary by doing internships and work placements as it would add to their experiences.

The table below shows the PhD in Statistics salary range in India;

The table below contains the list of PhD in Statistics job profiles and their average salaries per annum:

Source: Payscale

PhD in Statistics Salary Abroad

PhD in Statistics candidates often get jobs in international countries as the education and information are so specialized that the demand for those graduates is higher than the supply. The course is designed to make every student employable in a foreign country, irrespective of their specialization.

The average salary of a PhD in Statistics graduate abroad is AED 140,000(Source: Glassdoor). The table below contains the list of job profiles abroad and the appropriate per annum salaries:

Career Scope of PhD in Statistics

A PhD in Statistics or Doctor of Philosophy is a doctoral research degree and is normally the highest academic qualification one can achieve. It involves engaging in in-depth research along with a thorough understanding of research issues and the ability to solve key problems with exceptional analytical and observational skills. The aspirant should be comfortable with long working hours, analyzing and solving complex problems with calmness.

Courses after PhD in Statistics

There are actually a number of degrees which require a Doctor of Philosophy degree or are considered higher in qualification than a PhD Statistics. But this all depends on the country’s system you are looking at. Here are some courses that can be done after PhD in Statistics:

• MPhil Mathematics
• MPhil Statistics

Career options after PhD in Statistics

India's economy and dynamics are rapidly changing daily, and this has made the PhD aspirants join the specializations in academia and many more.

The aspirants are provided many options after they complete their graduation in PhD in Statistics according to their interests, some of the list of basic options one can go after PhD program:

• Statisticians: They collect numeric-related information and show it, helping organizations comprehend quantitative information and to spot patterns and make forecasts. They have to create methods to beat issues in information-gathering and examination.
• Economists: These professionals investigate and examine information that influences the financial and money-related occupations of the government. They forecast and clarify economic patterns in view of such data. Economists also inspect and break down information utilizing an assortment of programs, including spreadsheets, factual examination, and database administration.
• Enumerators: An Enumerator is essentially in charge of recording the number of individuals in a family, their ages, genders, and other such data. They collect and record such factual information.

Government Jobs for PhD in Statistics Graduates

If the aspirants want to go for a sector other than private faculties, then there will be the government sector at its best. There are many other opportunities and multiple positions available with the state and central government. There are also options available for conducting research as a civilian government employee.

There are many PhD in Statistics government jobs that students post their graduation can pursue. The average salary for these jobs is INR 3-8 LPA. (Source:Payscale) The job designations include:

Private Jobs for PhD in Statistics Graduates

Students who don’t wish to pursue a government job can build a career after PhD in Statistics in the private sector. The job opportunities for PhD in Statistics students in the private sector are endless. The average salary of PhD Statistics graduates is INR 2 - 10 LPA. (Source: Payscale) The job designations include:

Job Opportunities Abroad for PhD in Statistics Graduates

Top companies for phd in statistics graduates.

Below is a list of the top international companies that hire PhD in Statistics Graduates:

• Blue Ocean Marketing
• BNP Paribas India 
• Deloitte Consulting
• TCS Innovations Labs
• Nielsen Company

Best Countries for PhD in Statistics Graduates

The scope Of PhD in Statistics in India and abroad is very wide and broad. Below is the list of top countries offering job opportunities to PhD in Statistics Graduates: 

• United Kingdom

Various Career Designations Abroad for PhD in Statistics Graduates

Here is the list of exciting job roles that attract PhD in Statistics graduates to work abroad:

Famous PhD in Statistics Graduates

PhD in Statistics graduates always find success in the field they decide to pursue as they get to learn about a subject in-depth and, eventually, at the end of their degree, have to research something that hasn't been discussed in the world of research. Here is a list of some such PhD in Statistics graduates who have worked hard to create an important impact in the society we live in today:

• Robert Vaughn
• Peter Weller
• Dan Grimaldi
• Shaquille O'Neal
• Mikhail Youzhny
• Vitali and Wladimir Klitschko
• Mike Marshall

Get Free Scholarship worth 25000 INR

Cookies on GOV.UK

We use some essential cookies to make this website work.

We’d like to set additional cookies to understand how you use GOV.UK, remember your settings and improve government services.

We also use cookies set by other sites to help us deliver content from their services.

You have accepted additional cookies. You can change your cookie settings at any time.

You have rejected additional cookies. You can change your cookie settings at any time.

• Entering and staying in the UK
• Visas and entry clearance

Analysis of migrants use of the Graduate route

• Home Office

Published 14 May 2024

© Crown copyright 2024

This publication is licensed under the terms of the Open Government Licence v3.0 except where otherwise stated. To view this licence, visit nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] .

Where we have identified any third party copyright information you will need to obtain permission from the copyright holders concerned.

This publication is available at https://www.gov.uk/government/statistics/analysis-of-migrants-use-of-the-graduate-route/analysis-of-migrants-use-of-the-graduate-route

1. Introduction

This report looks at the journeys of foreign nationals who came to study in the UK, with a particular focus on those entering and leaving the Graduate route. It looks at who is entering the Graduate route, what they do after their Graduate leave expires, and if they extended into other leave. Additionally, it includes early insights on their earnings and employment by linking Home Office visa records to HMRC earnings data.

This report uses data that underpins the Home Office migrant journey statistical publications. The next full update of the Migrant journey covering 2023 is due to be published on 23 May 2024.

The report seeks to complement the Migration Advisory Committee’s (MAC’s) rapid review of the Graduate route published on 14 May 2024.

The report defines a migrant journey as a series of grants of leave to an individual where each grant is no more than 12 months after the expiry of the previous grant. As such, the number of new journeys in a given year within the report will not match the total number of overseas visa grants in the same period as published in the Immigration system statistics.

Overview of the Graduate route

The Graduate route was introduced in July 2021 and allows foreign students who have successfully completed a UK bachelor’s degree, postgraduate degree or other eligible course to stay in the UK for at least 2 years. Graduate visa holders can work or look for work during this period and may switch to another immigration route at any point.

It is too early to say whether the behaviours of the early adopters of the scheme will be indicative of the behaviours of later cohorts. This also applies when looking at Graduate visa holders’ earnings.

2. The Graduate Journey

What are study visa holders doing after their studies?

The proportion of students granted further leave to remain in the UK following their studies more than tripled between 2019 and 2023, from 18% to 56%.

Over half (56%) the number of students who came to the end of their studies in 2023 had further leave to remain in the UK, mostly on the Graduate route (32%) and other work routes (18%).

Figure 1: Students who came to the end of their studies by subsequent leave category

Source: Migrant journey underlying datasets, Home Office

• ‘Other work’ includes all work routes excluding the Graduate route. For example ‘Skilled Worker’ and ‘Skilled Worker - Health and Care’ visas.
• ‘Other leave’ includes all non-work routes.
• The numbers at the top of the bars are the total number of people leaving the student route in each year. This includes people whose leave came to end, and people who switched onto another route before their leave expired.

Figure 1 shows the proportion of students who remained in the UK after their studies by switching to another type of leave was between 15% and 20% in 2019 and 2020. This proportion has since increased reaching 52% in 2022 and 56% in 2023. In addition, the number of people leaving the student route increased in each year since 2019 (from 160,104 in 2019 to 350,365 in 2023), so both the number and proportion of students remaining in the UK beyond their studies has increased. While more students appear to be extending their stay by making use of the Graduate visa and other routes, we do not yet know if they will remain in the UK permanently, or whether they are just staying longer.

Before 2021, between 80% and 85% of students held no valid leave to remain in the UK following the expiry of their student leave. This proportion has fallen since 2020, reaching 44% in 2023. This period saw a number of different factors which may have affected the proportion of foreign students remaining in the UK, including the COVID-19 pandemic, the introduction of the Graduate route, and the ending of freedom of movement for EU nationals. It is difficult to unpick the extent to which these factors are impacting recent trends, however 2022 was the first year on record where more than half of student leavers were granted further leave.

Who is on the Graduate route?

The latest Immigration system statistics extensions data shows that 213,250 main applicants and 45,836 dependants were granted Graduate visas between its launch in July 2021 and the end of 2023.

Figure 2: Graduate route visas granted by year and applicant type

Source: Immigration system statistics; Extensions detailed datasets, table Exe_D01

The top 5 nationalities account for almost three-quarters (74%) of Graduate visas issued to main applicants with Indian nationals accounting for 42%.

Table 1: Top 5 nationalities granted Graduate visas (main applicants)

Table 1 shows that Indian (42%), Nigerian (11%) and Chinese (10%) nationals accounted for two-thirds of those entering the Graduate route in 2023. Indian students were proportionally more likely to switch to the Graduate route, accounting for 42% of Graduate visa grants but only 23% of Student visa expiries between 2021 and 2023; while Chinese students were proportionally less likely, accounting for only 10% of Graduate visa grants but 30% of Student visa expiries.

Those entering the Graduate route tend to be in their mid-to-late 20s, with more than half (58%) being between the ages of 24 and 29. Slightly more (52%) of the 2023 graduate entrants were male than female (48%).

113,105 students switched to the Graduate route in 2023; 69% of these students had been studying for one year or less.

Figure 3: Graduate route entrants by length of study leave between 2021 and 2023    

Source: Migrant Journey underlying datasets, Home Office

• Length of study leave is the number of complete years of valid study leave held before switching to the Graduate route.
• The numbers at the top of the bars are the total number of Graduate route entrants in each year.

Figure 3 shows that more than two-thirds (69%) of people taking up the Graduate route in 2023 had been studying for one year or less, compared with 56% in 2022. ​This is slightly higher than for those extending onto non-Graduate routes, where 60% had studied for one year or less​. This broadly aligns with the length of time students are studying in the UK more generally, with 66% of all students who came to the end of their studies in 2023 having studied for one year or less, up from 58% in 2022.

The increase in people switching within one year follows an increase in the number of one-year study visas being issued, which has more than doubled from 137,885 in 2019 to 298,383 in 2023.

For students who started their studies between 2011 and 2018, two-thirds (66%) held no leave to remain in the UK after 3 years. This fell to 61% for 2019, and 44% for the 2020 cohort who are the latest 3-year cohort. The numbers who hold no leave to remain are indicative of the proportion who should have left the UK at the point of analysis.

What do people do once their Graduate route leave ends?

25,469 people’s Graduate visas had expired by the end of 2023, with 63% switching to other routes.

The chart below presents what foreign students who had switched to the Graduate route did before their Graduate leave expired, indicated by their latest visa. However, it is too early to say whether the behaviours of early adopters of the scheme will be indicative of the behaviours of later cohorts. ​

Figure 4: Graduate visa expiries by subsequent category

Figure 4 shows that 63% of the 25,469 people whose Graduate visas had expired by the end of 2023 had switched to another route. Just under half (46%) had switched to a work route (33% extending into Skilled Worker, 9% into Skilled Worker - Health and Care, and 4% into other work routes). Smaller proportions had returned to study (7%) or switched to family (6%) or other routes (5%).

An additional 17,080 people had extended out of the Graduate route despite still holding valid leave at the end of 2023 with the majority (12,549) switching to work routes (with 8,485 into Skilled Worker, and 3,245 into Skilled Worker - Health and Care).

3. Graduate visa holder earnings

This section presents early findings on the earnings and employment of Graduate visa holders by linking Home Office visa records to HMRC’s Pay As You Earn ( PAYE ) Real Time Information ( RTI ) data.

Data is available from July 2021 (when the Graduate route was introduced) to March 2023. This means insights can be provided over one entire financial year, from April 2022 to March 2023. Findings over the whole period are provided where relevant and stated as such.

All figures and tables in this section relate to the PAYE reported gross earnings of main applicant Graduate visa holders who were aged between 18 and 65, whose visa was granted before the start of the period being looked at (and who had not switched onto any other visa type during this period).

Of the approximately 131,000 unique Graduate visa records extracted from Home Office case working systems that were granted between July 2021 and March 2023, 101,000 (77%) yielded a robust match to HMRC’s Migrant Worker Scan ( MWS ) database, allowing for the linking of these records to HMRC PAYE RTI data. A further 9,000 Graduate visas (7%) were partially matched but have been excluded from all analysis as their match was not deemed robust. Most of those who were not successfully matched to MWS are assumed to have never worked in the UK given that the reason they did not match is likely due to them not applying for a National Insurance number. However, there may be a small number of records that did not match due to differences in the information held by the Home Office and HMRC. These unmatched Graduate visa records are counted as unemployed within all figures reporting on proportions of Graduate visa holders in employment but are excluded from all analyses on earnings. See ‘ About the data ’ section for further details on methodology.

How many Graduate visa holders were in employment?

Of all Graduate visa holders in scope to earn across the whole financial year ending 2023, 73% of Graduate visa holders were in employment at some point during financial year ending 2023; however, of this 73%, the majority (63%) were not in employment for the full year.

Figure 5: Number of months Graduate visa holders worked during financial year ending 2023

Figure 5 shows the number of months in which Graduate visa holders worked during this period varied. This generally leaned towards longer spans, with 61% in employment for at least half of the financial year, and a further 27% consistently working throughout this period. Only 12% worked for less than 6 (but more than 0 months) out of the full 12 months, while 27% did not work at all. While figure 5 includes all Graduate visa holders in scope to earn across financial year ending 2023, this includes those who may have only recently graduated before this period and therefore had less time to seek employment.

How long did it take for Graduate visa holders to start earning?

Of all Graduate visa holders in scope to earn across the whole financial year ending 2023 who were in employment at some point in the financial year, 62% of Graduate visa holders were earning in the first month following their visa being granted.

One in ten (10%) started earning in the second month following their visa being granted and this proportion continues to decrease over subsequent months.

Figure 6: Month in which Graduate visa holders started earning following their visa being granted

• A small number of Graduate visa holders first earned in months that were more than 12 months following the grant of their visa. These proportions each rounded to zero and have therefore not been shown in figure 6.

How did Graduate visa holder employment differ by demographic characteristics?

Of the top 5 nationalities granted Graduate visas, Nigerian nationals were most likely to have worked at least one month (86%).

Chinese nationals were least likely of the top 5 nationalities to have worked at least one month (60%) and for the full year (18%).

Graduate visa holders aged between 25 to 34 and 35 to 49 were more likely to work at least one month compared to those aged 18 to 24 (both 76% compared to 68%). There was no notable difference in the proportion of Graduate visa holders in employment across gender.

Table 2: Proportion of Graduate visa holders in employment during financial year ending 2023 by nationality

• Proportions relate to the proportion of records where nationality was available in the data (excluding ‘all nationalities’ which relates to all available records).

How much did Graduate visa holders earn in each month?

Since the launch of the Graduate route in July 2021, the median monthly pay gradually rose from £1,227 to £1,937 in March 2023.

Figure 7: Median pay in each month from August 2021 to March 2023, where Graduate visa holders earned in the month

• August 2021 represents the first full month of earnings for those granted the Graduate visa in July 2021. This time series looks at each month in isolation and does not track the earnings of the same cohort over time.

While not directly comparable to UK labour market statistics (see ‘ About the data ’ section for further information), this reflects the wider general trend of monthly median earning for the general UK population aged between 18 and 65 across the same period ( Earnings and employment from Pay As You Earn Real Time Information ). Graduate visa holders tended to earn slightly less compared to the general UK population (approximately £300 less as of March 23). However, caution is required when comparing due to variations in cohort composition relative to the general UK population (including factors such as age, region of employment and other characteristics).

How much did Graduate visa holders earn in financial year ending 2023?

The median annual earning for the 73% of Graduate visa holders who were in employment for at least one month in financial year ending 2023 was £17,815. Whereas, for the 27% who were in employment across the entire year, this was £26,460.

Figure 8: Annual Graduate visa holder employment earnings for financial year ending 2023 by earning band

Figure 8 shows that 41% of Graduate visa holders who earned in at least one month in financial year ending 2023 earned less than £15,000. 9% of those who earned for the full year earned less than £15,000. For those in employment across the entire year, just under half (46%) earned between £20,000 and £29,999.

How did annual Graduate visa holder earnings differ by demographic characteristics?

Of the top 5 nationalities granted Graduate visas, USA nationals who worked at least one month had a noticeably higher median annual earning during financial year ending 2023 (£21,135).

In comparison, those from Pakistan had a lower median annual earning (£14,402), as did those from China (£15,762). The differences between nationalities amongst those who were employed over the full year are less stark, although the pattern is broadly similar; median earnings for USA nationals sat above the overall level at £28,000, with earnings for Pakistan nationals sitting below the overall level at £24,955.

Graduate visa holders aged between 35 to 49 who worked at least one month had a higher median annual earning (£19,328) compared to those aged between 18 to 24 and 25 to 34 (£17,701 and £17,746 respectively). Men earned a median of £17,792 (£26,879 where employed over the full year) and women earned £17,856 (£25,988 where employed over the full year).

Table 3: Median annual earnings during financial year ending 2023 by nationality

• Calculations relate to the records where nationality was available in the data set (excluding ‘all nationalities’ which relates to all available records).

What sectors do Graduate visa holders tend to work in?

Graduate visa holders were most likely to be employed within the administrative and support services sector (25%) followed by health and social work and professional, scientific and technical activities (16% and 14% respectively).

Of all Graduate visa holders in scope to earn across the whole financial year ending 2023 who were in employment at some point in the financial year (As categorised using UK Standard Industrial Classification ( SIC ) codes as defined by the Office for National Statistics ( ONS )).

Figure 9: Proportion of Graduate visa holders in employment during financial year ending 2023, by sector

• Sectors are based on the UK SIC codes, as defined by the ONS . These codes have been determined from both the Inter-Departmental Business Register ( IDBR ) and data from Companies House for each PAYE enterprise. Sector information is included where available in the data. ‘Other sectors’ contains remaining sectors with below 175 Graduate visa holders earning at some point in financial year ending 2023. Graduate visa holders may have worked in multiple sectors either concurrently across the financial year or even simultaneously.

Graduate visa holders were least likely to be employed in real estate (1%) and transportation and storage sectors (1%). A smaller proportion of Graduate visa holders worked in the administrative and support services sector for the full financial year (19%) compared to the proportion who were in employment in this sector for at least one month (25%). Compared to the other sectors, this relative proportion was notably larger, suggesting Graduate visa holders are more likely to have worked in this sector for a short period compared to other sectors.

Of the 5 largest sectors of Graduate visa holder employment, Nigerian nationals were the most likely to be working in the health and social work sector. 41% of Nigerian nationals were in employment in this sector for at least one month compared to 14% of Indian nationals, 11% of Pakistani nationals, 10% of USA nationals and 3% of Chinese nationals. Pakistani and Indian nationals were most likely to be employed in the administrative and support service activities sector (38% and 33% respectively). Chinese and USA nationals were most likely to be employed in professional, scientific and technical activities (21% of both).

How much do Graduate visa holders working in different sectors earn?

The sector with the highest annual earning for financial year ending 2023 was finance and insurance. The median earning was £34,846 for those who earned for the entire year and £27,879 for who earned in at least one month.

These annual earnings are substantially above the median annual earning for the full cohort (£26,460).

Figure 10: Median annual Graduate visa holder employment earnings for financial year ending 2023, by sector

• Sectors are based on the UK SIC codes, as defined by the ONS . These codes have been determined from both the IDBR and data from Companies House for each PAYE enterprise. Sector information is included where available in the data. ‘Other sectors’ contains remaining sectors with below 175 Graduate visa holders earning at some point in financial year ending 2023. Graduate visa holders may have worked in multiple sectors either concurrently across the financial year or even simultaneously.

Figure 10 shows that the lowest median annual earning for those Graduate visa holders employed in in at least one month of the financial year ending 2023 was for the accommodation and food service activities sector (£12,805). The administrative and support services sector had the second lowest median annual earning of £14,438 for those who earned in at least one month. The health and social work sector (the second most common sector for Graduate visa holder employment as shown in figure 9) ranked comparatively higher (£16,559). However, those in the administrative and support services sector who worked for the full financial year still earned more than those in the health and social work sector who did the same (£25,550 compared to £24,242).

The gap between the median annual earnings of those working for the full year, compared to those who worked in at least one month, was notably wider for the accommodation and food service activities (£12,805 vs £21,852) and the administrative and support services sectors (£14,438 vs £25,550) compared to the health and social work sector (£16,559 vs £24,242). This suggests that those in these 2 sectors were more likely to be in part-year or seasonal employment compared to those in the health and social work sector.

4. About the data

Migrant journey data

The ‘Migrant journey: user guide’ provides further details on this topic including definitions used, how figures are compiled, data quality and issues arising from figures based on data sourced from an administrative database.

The analysis in this report is based on an earlier data extract than the one which will be used in the upcoming Migrant journey 2023 report. As extracts are taken from a live data-matching system, there may be differences between numbers included in this report and in the upcoming Migrant journey report.

Unless stated otherwise, figures refer to the number of people (main applicants only) whose sponsored study visas and Graduate route extension visas have been successfully matched. Therefore, these figures may not match visa totals published in the Immigration system statistics .

Earnings data

Earnings data used for this report comes from a range of Home Office case working systems. Data is extracted from the Initial Status Analysis ( ISA ) system comprising data from the Case Information Database (CID), the Central Reference System (CRS) and Atlas. This includes data on grant of entry clearance (visas issues) and extensions of stay within the UK.

This data is then matched against HMRC Real Time Information ( RTI ) for Pay As You Earn ( PAYE ) data using the available common identifiers.

Where a visa record had a National Insurance number ( NINo ), this was verified against the Migrant Worker Scan ( MWS ) – a list of individuals who successfully applied for a NINo through the Department for Work and Pensions (DWP) post-16 registration process, or have had a NINo allocated to them as part of their visa granted by the Home Office – before linking to RTI . A record was deemed robust if it matched on at least 5 out of 6 of the following identifiers:

• date of birth
• nationality

Where the visa record had no NINo , fuzzy matching to MWS to assign a NINo was done using the same variables. Two primary matching methods are employed:

• precision matching
• Levenshtein edit distance ( LED )

Precision matching assesses specific variables from visa data against HMRC tax records. LED is used to match visa applicants with minor input errors in key fields, employing substitution, deletion, and replacement to compare strings. Matches where a NINo was not available were deemed robust if the record matched an MWS record on date of birth, surname, forename and at least 2 out of 3 of the following:

The visa records extracted for the purpose of matching to HMRC records in time to produce analyses for this report were extracted in a bespoke way for this data set. This means the number of records extracted may not necessarily reflect total figures presented in other published statistics for the immigration system.

These data and all figures produced from it are classified as ‘Official Statistics in Development’. This means statistics remain subject to further development and will have a wider degree of uncertainty. Please see ‘Official Statistics in Development’ . New methods are being tested to improve quality and provide better coverage across wider visa routes. Limitations of the data is explained in further detail below.

Further information about the data set used for this analysis

The data set contains all unique Graduate visa records that were successfully extracted from Home Office’ case working information databases and securely shared with HMRC.

The data set only contains main applicant Graduate visas granted to those aged 18 and over. It does not contain information on their dependants.

The data set only includes PAYE employment earnings in the UK and does not contain self-employed earnings or earnings from any other sources. This has meant that a small number of Graduate visa holders counted as unemployed might in fact have been self employed, however, the number of self employed Graduate visa holders is very small.

All figures and tables in this section relate to Graduate visa holders whose visa was granted before the start of the period being looked at and who had not switched onto any other visa type during the period.

We currently are unable to identify hours worked or whether employment is part-time or full-time.

By ‘in employment’ for the full financial year, at least some part of it or in a single month, this refers to the monthly employment level gross pay for this period being greater than £0.

Percentages are rounded to the nearest per cent. Figures are rounded to the nearest £. Where percentages are rounded, they may not total 100% because they have been rounded independently.

Age is calculated as of 5 April 2022 for analyses focusing on financial year ending March 2023 and as of the start of the month for analysis focusing on earnings within each month. All other demographic characteristics are recorded as of the time of the Graduate visa application.

A small number of Graduate visa records were missing an expiry date. All held a grant date. Where an expiry date was missing, one was imputed by adding 2 years to the grant date. This will have likely been the correct expiry date for most of these records where an expiry date was missing, however, if the Graduate visa holder was granted the 3-year visa after completing a PhD or had obtained a Graduate visa under a different expiry date, this imputation may be incorrect.

Sectors are based on the UK SIC codes, as defined by the ONS . These codes have been determined from both the IDBR and data from Companies House for each PAYE enterprise. Sector information is included where available in the data. Graduate visa holders may have worked in multiple sectors either concurrently across the financial year or even simultaneously.

Limitations of the data

While figures are derived from HMRC matched data, figures are calculated using a separate methodology to the UK labour market statistics, jointly produced by HMRC and the ONS , and cannot be directly compared to these statistics. Caution is advised when comparing to any other similar data sources of graduate or UK population earning statistics.

As with all administrative data, there will be a small number of cases where data is missing or has been inputted incorrectly. Some information submitted by employers for RTI is late, missing or incorrect.

Data cleaning was performed prior to analysis to allow for optimal matching outcomes. Duplicated visa data was also removed prior to analysis; however, some may remain in the data. We are exploring the use of an alternative data extraction method for visa records to minimise data processing errors and better reflect other published sources.

While standards for a ‘robust’ match to HMRC data have been set high, matches may still not be 100% accurate. Individuals with near identical personal details may be incorrectly identified as the same person.

Unmatched Graduate visa records are counted as unemployed within all figures reporting on proportions of Graduate visa holders in employment. Some Graduate visa holders may have been in employment but were not successfully matched due to discrepancies in the personal identifier information held in either Home Office or HMRC data used for matching. There may be instances where the likelihood of being matched differs by certain demographics or other characteristics

Is this page useful?

• Yes this page is useful
• No this page is not useful

Help us improve GOV.UK

Don’t include personal or financial information like your National Insurance number or credit card details.

To help us improve GOV.UK, we’d like to know more about your visit today. Please fill in this survey .