phd data science virginia

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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.

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.

Admission Requirements

Submit an online application by the stated deadlines on the Ph.D. program website. A non-refundable application fee of $85 is payable at the time of application.

The online application requires answering an essay and several short answer questions; submitting transcripts of all academic work; three letters of recommendation (preferably from faculty or other individuals with meaningful knowledge of the applicant’s potential to succeed in graduate school), and a CV or resume. Official transcripts are not required for review purposes but are required upon matriculation. The Ph.D. in Data Science does not review standardized test scores (i.e.,  GRE ,  GMAT ,  MCAT ) in its holistic evaluation of applicants.

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). 

Rescinding an Offer of Admission

It is expected that all admitted students uphold the intellectual, ethical, and professional standards of the School of Data Science and UVA. All applicants offered admission to the School of Data Science agree to abide by the principles laid out in the  UVA Honor Code  and the  Standards of Conduct . The School of Data Science may rescind an offer of admission up until the date of matriculation for, but not limited to, the following reasons:

• An admitted applicant is found to have presented misleading or fraudulent information during the application process.
• An admitted applicant fails to uphold the principles of the Honor Code and the Standards of Conduct mentioned above.

Students admitted to the School of Data Science may request a deferral of their enrollment to the next cohort start date if facing extreme hardship or mitigating circumstances. To request a deferral, students must accept their offer of admission and submit the deferral request form located in the Application Status Portal. The Admissions team will review deferrals within 72 business hours. If granted, students must accept a revised offer of admission for the next cohort date within two weeks of the date of offer. Deferrals are not guaranteed and are granted at the discretion of the School of Data Science. Students admitted from the wait list may not request a deferral. Students who are not granted a deferral can reapply to the program in the future. 

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, successfull 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.

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. Satisfactory academic progress, including research for the thesis or dissertation, is essential.

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 Ph.D. Program Director with prior approval by 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 Ph.D. Program Director to count toward the student’s degree requirements.

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.

Overview of Degree Milestones and Academic Requirements

Students will engage in coursework and research. 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 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:

• Completion of Core courses (18 credit hours)
• Successful completion of the qualifying exam
• Successful dissertation proposal
• Successful defense of dissertation research

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

• Completion or waiver of Foundation courses (18 credit hours)
• Completion of elective and research methods requirements (9 credit hours)
• Completion of dissertation research credit requirements (33 credit hours)
• 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. 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.

Coursework Requirements for PH.D. In Data Science

Foundational courses.

Minimum of 18 credit hours

Foundation courses cover the topics that are typically included in a master’s degree in data science. If a student has completed previous graduate coursework or has relevant work experience in a foundational course topic, the foundational course requirement may be waived. Typically, students take foundational courses before enrolling in core courses.

• DS 6200 - Computation I: Fundamentals Credits: 3
• DS 6210 - Computation II: Numerical Analysis & Optimization Credits: 3
• DS 6300 - Theory I: Probability & Stochastic Processes Credits: 3
• DS 6310 - Theory II: Inference & Prediction Credits: 3
• DS 6400 - Machine Learning I: Introduction Credits: 3
• DS 6410 - Machine Learning II: Methods & Application Credits: 3
• DS 6600 - Data Engineering I: Data Management & Visualization Credits: 3
• CS 5012 - Foundations of Computer Science Credits: 3

Core Courses

18 credit hours

Core courses cannot be waived or substituted.

Students must pass each core course with a minimum grade of B-.

*Will accept equivalent coursework only if course not offered in a timely manner.

• DS 6700 - Value I: Data Ethics, Policy and Governance Credits: 3
• DS 7200 - Computation III - Distributed Computing Credits: 3
• DS 7400 - Machine Learning III: Deep Learning Credits: 3
• DS 7700 - Value II: Data and Society Credits: 3

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, to gain specific knowledge or skills necessary for their research.

The current Ph.D. elective courses offered within the School of Data Science are:

• DS 6234 - Uncertainty in Artificial Intelligence Credits: 3
• DS 6559 - New Course in Data Science Credits: 1 to 4

Topic: Remote Sensing

Topic: Data Science in Brain Science

Topic: ML in Sys & Network Security

• DS 7406 - Machine Learning Systems Credits: 3
• DS 7540 - Machine Learning IV Credits: 3
• DS 8104 - Network Science Credits: 3

Data Science Research Methodology: 3 credit hours

• DS 7800 - Research Methods in Data Science Credits: 3

Data Science Research Rotation: up to 9 credit hours

Students typically spend the first summer in the program working in one or more faculty research labs, experiencing different research topics and environments. 

• DS 8998 - Master’s Level Thesis Research Credits: 1 to 12

Research Requirements for Ph.D. in Data Science

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 PhD 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.  If the first attempt is not successful, students may retake the exam once.  Typically, students will complete the exam within a semester of completing the core courses.  Unless an exemption is granted from the program director, the exam must be completed within one year of completing the core courses.   

Dissertation Research: minimum of 33 credit hours

• DS 9999 - Dissertation Research Credits: 1 to 12

Dissertation Proposal

Successful completion of the qualifying exam marks the start of the research phase. The student will form a dissertation committee of 4 faculty, including a research advisor. After crafting a research proposal, the student will publicly present the plan to the committee.  Students should aim to complete the dissertation proposal within one year of completing the qualifying exam. Following the oral proposal, the examiners will decide if the student passed, conditionally passed, or failed the exam.  Students who fail the exam may retake the exam one time.

Dissertation Defense

During the research phase, the student will meet regularly with the research advisor and twice yearly with the dissertation committee. Upon successful execution of the dissertation proposal and authorship of the dissertation document, the student will present the research to the dissertation committee and the UVA community. The PhD 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.  Following the oral exam, the committee will decide if the student passed, conditionally passed, or failed the exam.  Students who fail the exam may retake the exam one time. 

PhD Dissertation Upload to LIBRA

After successful completion of the Ph.D. dissertation defense and submission of the associated forms, the student must submit the approved final dissertation along with the Thesis/Dissertation Cover and Approval Pages Form to  Libra , the online archive of UVA by the date specified in the academic calendar.

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