Data Science, Ph.D.

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). 

Admission Requirements

Submit an online application by the stated deadlines on the Ph.D. program website and pay the non-refundable application fee for admission consideration.

Students must have an excellent command of the English language to enroll at the University. The Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS) is required of all applicants if the language first learned and spoken in the home is not English. The minimum TOEFL (iBT) score requirement is 100 (including minimum section scores of 22 in speaking, 22 in writing, 23 in reading and 23 in listening). The minimum IELTS score requirement is 7.0 (including minimum section scores of 6.5).  For more information, see the University Regulations - International Student Admission section of this Record.

Financial Assistance

Students receiving financial assistance from the School of Data Science must be registered as full-time students in the semester in which they are receiving financial assistance.  Continuation of funding throughout the program is contingent upon satisfactory academic performance, successful fulfillment of assigned duties as a teaching or research assistant, and compliance with all applicable University, School, and departmental policies, including but not limited to those governing student conduct, academics, and the Honor Code.

Assistantships

Students should consult PROV-001: Graduate Assistantships for policies and procedures governing Qualified Graduate Assistantships, including graduate research assistantships and graduate teaching assistantships.  Hourly-wage master’s teaching assistant assignments are not considered Qualified Graduate Assistantships.

Graduate Research (GRA) and Teaching Assistants (GTA) may be enrolled in DS 6097 Graduate Assistantship in each term in which they are enrolled with assistantship funding. A student’s research advisor (if GRA) or the instructor of the course (if GTA) serves as the course instructor for DS 6097 and assigns the grade. Whether enrolled in a course or not, students on an assistantship are encouraged to meet with their supervisor throughout the term to ensure they are meeting expectations for satisfactory performance.

Employment Restrictions For Funded Students

Students receiving School or graduate program funding through graduate assistantships or fellowships are not permitted to have other employment without approval of the Ph.D. Program Director.  Students are awarded financial assistance to enable them to devote maximum effort to graduate studies.

Fellowships

Fellowships offered by UVA School of Data Science are intended to allow graduate students to devote their time to learning opportunities in the classroom and in research. 

General Enrollment Requirements

Full-Time Enrollment Enrollment in a Ph.D. program requires full-time registration each semester. Full-time enrollment in the Fall and Spring semesters is a minimum of 12 and a maximum of 15 credit hours per term. Optional full-time enrollment for summer semester is at least 6 credit hours. Full time enrollment is required if receiving financial aid.

Affiliated Status Limits Students who are not required to be enrolled in a term but who need to retain a minimal affiliation with the University on a temporary basis may apply to be on Affiliated Status. Ph.D. students may be on Affiliated Status for Doctoral Completion for up to 4 semesters. Once approved for Affiliated Status, students may not return to full-time study in their degree program.

Minimum Length of Study Full-time Ph.D. students must enroll for at least six regular semesters (Fall and Spring) of graduate study after the baccalaureate degree.

Residency Graduate degree programs require a period of residency to fully engage in the UVA academic community and to actively contribute to intellectual discourse within the School of Data Science. For students coming into a Ph.D. program with a master’s degree, at least six regular semesters must be in full residence at UVA in Charlottesville. For students coming into a Ph.D. program with a bachelor’s degree only, at least eight regular semesters in full residence at UVA are required.

Consecutive Enrollment Ph.D. students must enroll in courses or research for all terms (fall and spring) from the matriculation term until degree conferral, including the term in which the dissertation/thesis is submitted. The only exception occurs when the student is granted an official leave of absence. Failure to enroll in courses for a term without taking an approved leave of absence results in denial of further enrollment unless and until readmission to the degree program is granted.

Time Limit for Degree All requirements for the Ph.D. degree must be completed within seven years after matriculation to the program. A student may petition to extend their time-to-degree beyond the allotted timeframe to the program director who will seek input from the student’s advisor. Such a petition must be filed before the end of the allotted time frame.

The time to degree limit can be extended beyond the normative time limitation for Data Science graduate students for 1) parental leave or 2) serious personal or family illness upon notification to and approval of the Ph.D. Program Director. The time extension will be for a period of up to one year. Use of this policy should be invoked as soon as the need for additional time becomes known.

Expiration of Credits Credits used to fulfill the requirement for the Ph.D. degree must be earned within 10 years of the semester of the degree conferral. Courses taken at UVA to fulfill the degree requirements expire 10 years after the completion of the course. Courses taken outside UVA for transfer credit that have not been approved for transfer within 10 years after completion of the course are considered expired. Automatic bulk transfers are excluded from the expiration of credits.

Expired credits cannot be counted toward degree requirements without revalidation. Expired credits may be revalidated if the current instructor of the course reviews the syllabus of the expired course and affirms that the content is still relevant and must be approved by the student’s advisor and the program director to count toward the student’s degree requirements.

Receiving Credit for Prior Graduate Coursework

Students may request transfer credit using the Transfer Credit form.  Core courses may not be substituted and must be taken by all students. 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. A course used to satisfy a prior degree cannot be transferred to the Ph.D. in Data Science. 

Overview of Degree Milestones and Academic Requirements

Students begin the Ph.D. program 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 on an area of data science or research topic.  There are four major milestones to earning the degree, each described in greater detail in subsequent sections:

1. Completion of Core courses (18 credit hours)
2. Successful completion of the qualifying exam
3. Successful dissertation proposal
4. Successful defense of dissertation research

While in pursuit of the major milestones listed above, students complete other minor milestones along the way.  These include:

1. Completion or waiver of Foundation courses (18 credit hours)
2. Completion of elective and research methods requirements (9 credit hours)
3. Completion of dissertation research credit requirements (33 credit hours)
4. Completion of total credit requirements (81 credit hours)

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. These credits may not satisfy Core requirement courses.

Coursework Requirements for Ph.D. In Data Science

Research Requirements for Ph.D. in Data Science