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Ph.D. Track in Data Science

The Ph.D. track in Data Science at William & Mary is offered as a specialization within Applied Science, with the core mission of training students in the use of exceptionally large, heterogeneous datasets to drive decisionmaking across a wide range of fields (from Physics to the social sciences).  Graduate students complete a core sequence of coursework as a cohort, and then work closely with an advisory committee to complete the degree program.  Competitive stipends and tuition are provided to selected students (stipends for AY23-24 are $29,000).

To receive a Doctor of Philosophy in Applied Science with a Specialization in Data Science, the candidate must:

  • Complete a sequence of coursework, normally lasting two years.
  • Pass a comprehensive qualifying examination designed to demonstrate competence.
  • Produce and defend a dissertation prospectus which details anticipated research after coursework is completed, including preliminary quantitative works.
  • Carry out a substantive original research project, and produce a dissertation describing this research which is approved by the student’s advisory committee and successfully defended in a public oral examination.
What to Expect & What We Expect

Be prepared for a rigorous program that emphasizes the analysis of large datasets, frequently in applied domains using machine learning techniques. You will take courses in both the underlying mathematical foundations and computational techniques used to define, implement, and validate models across a range of disciplines. 

Generally, we expect students applying to this program will have a background in computer programming, probability and statistics.  Most successful candidates will also have some experience working with large datasets in applied contexts.  Python is the most commonly used language, though some courses and laboratories use alternatives such as R, Scala, or compiled languages.

Most students will start their program during the Fall semester (while spring admissions are possible, they will only occur under exceptional circumstances).  In many cases, a Ph.D. student might expect a schedule similar to:

  • Year 1: Coursework (Mathematical and Computational Methods, Applied Machine Learning, Bayesian & Frequentist Statistics, Deep Learning, Network Analysis), first research lab experiences, TAing a course, Doctoral Research Seminar.
  • Year 2: Coursework (Mathematical and Computational Methods II, Data Engineering, Natural Language Processing, Probabilistic Programming, Reinforcement Learning, Directed Research), qualifiers, dissertation prospectus defense, TAing a course.
  • Year 3+: Dissertation, full time research in a research lab, annual evaluation of progress to dissertation by the graduate committee.

See the Graduate Catalog for details.

How to Apply

Data Science Ph.D. students are admitted to the Graduate program in Applied Science, in which they will earn a specialization of Data Science.  Applications can be submitted online at the link below, or by clicking here. Note that we do not today require GRE scores, but they can be optionally submitted for consideration.

Applications are reviewed twice annually for admission - once in the Fall for applications submitted by November 15th, and a second time in the Spring for applications submitted by March 1st.  We aim to provide students with decisions within approximately 4 weeks of these deadlines.

The application process includes:

  1. Registering for an Account, and clicking "Start New Application"
  2. Choosing to apply to the "Graduate Arts and Sciences" school, and then the "Applied Science" program.  
  3. Providing various background and demographic information.
  4. Providing a 3500 character (or less) synopsis of your past project experience, including (if any) publications.
  5. Providing a 3500 character (or less) synopsis of your background, including extracurricular activities and general experience.
  6. Writing a personal essay describing your career plans and rationale for the pursuit of graduate study in Data Science - we recommend you identify members of our faculty that may match your interests in this essay.
  7. For your "Applied Science Research Interest", you will choose "Data Science".
  8. For the advisor you are interested in working with, you will choose the graduate director, "Dan Runfola"; graduate advisor assignment will occur after admission.  You will leave your second choice blank.

If you have any questions about the admissions process, you can contact Dr. Dan Runfola, the Graduate Director of Applied Science (danr@wm.edu).

Apply Now