|
|
Graduate Record 2022-2023 [ARCHIVED RECORD]
Doctor of Philosophy in Data Science
|
|
Return to: School of Data Science: Programs & Courses
The Doctor of Philosophy (Ph.D.) in Data Science is designed to impart the skills and knowledge necessary to enable research and discovery in data science methods. Because the end goal is to extract knowledge and enable discovery from complex data, the program has robust applied training that is geared toward interdisciplinary collaboration. Doctoral candidates will master the computational and mathematical foundations of data science, and develop competencies in data engineering, software development, data policy and ethics.
Doctoral students in our program apprentice with faculty and pursue advanced research in an interdisciplinary, collaborative environment that is often focused on scientific discovery via data science methods. By serving as teaching assistants for the School’s undergraduate and graduate programs, they learn to be adroit educators and hone their critical thinking and communication skills.
Pursuing a Ph.D. in Data Science prepares students to become an expert in the field and work at the cutting edge of a new discipline. A Ph.D. in Data Science from the University of Virginia opens career paths in academia, industry or government. Graduates of our program will:
- Understand data as a generic concept, and how data encodes and captures information
- Be fluent in modern data engineering techniques, and work with complex and large data sets
- Recognize ethical and legal issues relevant to data analytics and their impact on society
- Develop innovative computational algorithms and novel statistical methods that transform data into knowledge
- Collaborate with research teams from a wide array of scientific fields
- Effectively communicate methods and results to a variety of audiences and stakeholders
- Recognize and respect the generalizability of data science methods and models
Graduates of the Ph.D. in Data Science will have contributed novel methodological research to the field of data science, demonstrated their work has impactful interdisciplinary applications and defended their methods in an open forum.
|
Time to Degree
All students in the Ph.D. in Data Science program are required to attend full-time per the University credit requirements for graduate students. Ph.D. students will be expected to advance to candidacy by the conclusion of their third year. Requirements for the Ph.D. must be completed within seven years of the date of admission to the degree program. Prerequisite Courses and Minimum Qualifications
An applicant must have a baccalaureate degree from a recognized college or university. Undergraduates from all majors and programs who are interested in learning about and developing data science methods are encouraged to apply. Multivariable Calculus
A course or courses from an accredited college or university that covers concepts through multivariable calculus and functions in more than one dimension. In the U.S., this is typically a three-course sequence (Calculus I, Calculus II, Calculus III). Matrix Algebra or Linear Algebra
Evidence of proficiency in matrix algebra via a linear algebra or similar mathematics course from an accredited college or university, or completion of Linear Algebra for Data Scientists. Statistics
At least one course from an accredited college or university that covers concepts in probability and statistical inference. Programming Experience
This experience can be demonstrated by completion of a course in computer science from an accredited college or university or substantial experience working with a programming language (such as Python, R, Matlab, C++, or Java). We will ask you to detail this experience in your application. Receiving Credit for Prior Graduate Coursework A request for credit transfer must be submitted separately and must include the following documents: a petition form, a description of course content or syllabus, and an official transcript. Up to 9 credits are allowed to be transferred; core courses may not be substituted and must be taken by all students. Academic Requirements for Ph.D. in Data Science
The program requires a minimum of 81 credits resulting from research and graduate-level course work beyond the baccalaureate. Classes at the 4000-level or below do not count toward the graduate degree requirements. A maximum of nine (9) credits may be transferred from other schools of recognized standing; however, only courses with a grade of B or better may be transferred. After matriculation, the student and program director will work on a course plan for the degree allowing for the transfer of up to 9 credits. These credits may not satisfy Core requirement courses. A candidate for the Ph.D. degree must fulfill the general requirements of the School of Data Science listed below and the following specific requirements: - Complete an approved plan of study consisting of graduate-level coursework and research credits.
- Pass comprehensive exams.
- Pass a Doctoral Qualifying Oral Examination.
- Pass a dissertation defense.
Graduates of the PhD in Data Science will have contributed novel methodological research to the field of data science, demonstrated their work has impactful interdisciplinary applications and defended their methods in an open forum. Core Courses
18 credit hours. *Will accept equivalent coursework only if course not offered in timely manner. Foundational Courses
Minimum of 18 credit hours from this list or equivalent graduate coursework; DS 6700 or equivalent required for all students. Data Science Elective: 6 credit hours
Elective coursework must be approved by the student’s research advisor. Students may request and receive approval to complete electives from elsewhere in the university, in order to gain specific knowledge or skills necessary for their research. Data Science Research Methodology Requirement: 6 credit hours
Dissertation Research: minimum of 33 credit hours
Doctoral Requirements and Procedures
Research Advisor
The student will select a primary research advisor no later than the end of their fourth semester in the program. The research advisor will supervise the student’s research, from developing a
research project to carrying it out during the dissertation phase. Research advisors’ duties may also include advising on any additional courses needed to reach research goals. The research
advisor will oversee the student’s comprehensive exams and dissertation proposal defense. The dissertation research will be conducted by the student with input from their research advisor,
who is expected to regularly offer guidance on research procedures, give feedback, and read drafts, in addition to overseeing the dissertation defense. A secondary (dual) advisor may be
appointed in situations where specific domain expertise is required. Research Advisory Committee
Following selection of a research advisor, a research advisory committee will be formed that will guide the student through the remainder of their graduate program. This will be composed of no
less than three individuals, including the dual advisor if appointed. The composition of the research advisory committee shall be such that the significant areas of the student’s research
focus are represented. At least one (1) member of the committee shall be from a department other than that of the research advisor(s). Final approval of the research advisory committee
membership shall be by the Graduate Program Committee of the School of Data Science. Following successful completion of the comprehensive exams, students will meet with their
research advisory committee no less than once a year to monitor progress and recommend any additional or remedial actions. The student will meet with their research advisor(s) as needed
during the semester. Admission to Candidacy for Ph.D. Degree
Before admission to candidacy for the Ph.D., students must complete all required coursework and a major research project, pass comprehensive exams, and defend a dissertation proposal. Major Research Project
Students will be required to complete a major independent research project of potentially publishable quality before advancing to candidacy. They will submit this work (roughly 25 pages,
formatted as an academic paper) to the Director of Graduate Studies. If the work has already been accepted for publication by a reputable journal in the field, it may be submitted with evidence of such and will automatically fulfill this requirement. If the piece has not (yet) been accepted for publication, the Graduate Curriculum Committee will assess whether it satisfies norms of research design, methodology, argumentation, conceptualization and writing. It is recommended that this project be submitted no later than the end of the student’s fourth semester in the program, so that progress towards comprehensive exams and the dissertation may be made. If a project submitted for this requirement is not acceptable to the Graduate Curriculum Committee, the student and their advisor(s) will be informed, and given actionable feedback and a three-month window in which to address concerns and resubmit the project. If the research project is not acceptable upon resubmission, the student will be asked to leave the degree program.
Comprehensive Exams
Upon completion of all required graded coursework, students will take part in comprehensive exams, which will consist of three area exams supervised by faculty with expertise in those areas.
It will be customary for two of these exams to be in areas of topical relevance and one of them to comprise a method. For each area, the student and research advisor(s) will construct a bibliography
aimed at comprehensive mastery of that area. The faculty advisor will then craft questions to which the student must respond by drawing upon and synthesizing this literature. Assessment of
the exam will be based on demonstrated mastery of the literature and the quality of the answer to the question. Students who fail to pass the exams(s) on their first attempt will be given actionable
feedback and a three-month window in which to address concerns and resubmit. Failure to pass the exam(s) on the second attempt will result in dismissal from the degree program.
Dissertation Proposal & Defense
Before advancing to candidacy and typically during the sixth semester, students will submit a dissertation proposal to their committee and complete an oral defense that covers: 1) course work
related to the student’s proposed research; 2) the literature cited in or related to the proposal; and 3) the research techniques and procedures presented in the proposal. The committee will assess
the quality of the proposal, including the importance of the research, the research design, conceptualization, plan of work, and the feasibility of the project. Students must address the
committee’s concerns, make required revisions to the proposal, and resubmit it to the committee for approval before advancing to candidacy. Successful completion of the dissertation proposal
advances the student to candidacy for the doctoral degree. If the dissertation proposal is not approved, the student and their research advisor(s) will be informed and given actionable
feedback and a three-month window in which to address concerns and resubmit the proposal. Failure to secure approval upon resubmission will result in dismissal from the degree program.
Dissertation
Students will be required to write a dissertation based on their original research and defend it in an oral examination. Upon completion of their research, students shall prepare a written
dissertation that fully communicates the original research, situates it within a larger literature, and explores larger implications. The final dissertation will be submitted to the Director of Graduate
Studies, the student’s Research Advisor(s), and the Research Advisory Committee. The oral defense of the dissertation will be directed by the student’s primary research advisor and all
committee members will participate. The oral defense will examine the student’s project, intellectual context, and the underlying fundamental knowledge or contribution to data science. Following the defense, all committee members will vote on the acceptability of the dissertation. A student can pass the oral defense, signifying that the committee has accepted the dissertation
project, with no more than one negative vote. A student who passes may still be asked to make minor revisions prior to submitting the dissertation to the School of Data Science. A student
who does not receive a passing vote will be provided with significant feedback and offered the opportunity to revise and have a second examination within the following year. Upon successful
completion of the defense and dissertation, the student may apply for graduation. |
|
|
