Overview
Our Ph.D. curriculum integrates foundations of computation, data engineering, data modeling, theory, data policy, and ethics. The program generates graduates that are talented data handlers, expert modelers, competent theorists, and engaged, collaborative scientists. The Ph.D. curriculum is designed around four Domains of Data Science:
- Analytics (statistical and machine learning, stochastic modeling, decision making)
- Design (human-computer interaction, data engineering, visualization, networks)
- Systems (software and hardware, cloud computing, high performance computing)
- Values (privacy, ethics, governance, society)
Pathway to the degree
Students begin with coursework to establish a common language and acquire a broad knowledge of the foundations of data science. Students then transition into research by focusing in an area of data science or research topic. There are four milestones to earning the degree:
Completion of Core courses
Completion of the qualifying exam
Dissertation proposal
Defense of dissertation research
Coursework
Foundation Courses
Before enrolling in Core classes, students may enroll in preparatory, foundation-level courses. Students will consult with the Ph.D. Program Director for individualized advice on the timing and preparation for the Core courses.
Core Courses
These are required 7000-level courses that all students must successfully pass with a grade of B or better. The courses span the domains of data science.
Elective Courses
In addition to the Core courses, we offer electives for deeper specialization within the domains or for research experiences. For example, those specializing in analytics may enroll in electives such as Bayesian machine learning, reinforcement learning, or deep learning. There are electives in networks and human-computer interaction for those in the design space.
View the current list of course offerings.
Research
The Ph.D. in Data Science is a research focused degree. Students are expected to generate new knowledge and push the boundaries of data science in their domain of choice, as well as demonstrate the impact of, and need for, these ideas in comprehensive application. After completing the Core courses, students continue with a mixture of research hours and elective course credits, with the following milestones.
Qualifying Exam
After completing the Core courses, the next milestone is completing the qualifying exam. The qualifying exam is both a written and oral exam to assess the research readiness of Ph.D. candidates. The exam is administered by a qualifying committee of three faculty members, including the student's faculty advisor. The exam covers topics proposed by the student and vetted by the qualifying committee.
Dissertation Proposal
Successful completion of the qualifying exam marks the start of the research phase. The student will form a dissertation committee of four faculty, including a research advisor. After crafting a research proposal, the student will present the plan to the committee.
Dissertation Defense
During the research phase, the student will meet weekly with the research advisor and twice yearly with the dissertation committee. Upon successful execution of the dissertation proposal, the student will present the research to the dissertation committee and the campus community.
Community
Data science is a rapidly changing field. We encourage our students to actively engage with others in the research community through national and international conference attendance and participation in professional organizations. Students are required to attend school-wide seminars and conferences throughout the year. See examples of conference session topics here and here.
Funding
Doctoral students receive full financial support from the School of Data Science for the duration of their enrollment in the program, pending satisfactory completion of requirements and progression through the program. This includes full remission of tuition, fees, and the premium for single-person coverage through the University of Virginia’s student health insurance plan. Read more on the Tuition and Fees page.