Data Science, M.S.

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The residential MSDS full-time program begins in the Fall term and offers two pathways for completion: 1) three consecutive terms of coursework (Fall, Spring, and Summer); or four consecutive terms (Fall, Spring, Summer, Fall), in which the summer is used to complete a pre-approved, non-credit, data science internship. Students who elect to complete the program in three consecutive terms (11 months in total) are required to take Data Engineering II, a capstone course, and one restricted elective (9 total credit hours) during the summer, culminating program requirements in August. Alternatively, students who complete an internship are required to take Data Engineering II and two restricted electives the following Fall (9 credits total – capstone not required), consistent with Fall tuition and fee rates, culminating program requirements in December (15 months total).

The online MSDS part-time program may be started in the Fall or Spring and can be completed in five semesters.

Prerequisite Requirements

An applicant must hold a bachelor’s degree conferred by an accredited college or university. Proof of degree conferral issued by the undergraduate institution is required before an admitted applicant may start the program. Degree conferral is defined as official certification and confirmation of the date of degree completion from the undergraduate institution’s Student Records or Registrar office and is typically signified by receipt of a diploma or degree certificate and/or an official final transcript.

Regardless of the applicant’s undergraduate degree or major, completion of the following prerequisite courses are required:

Single variable calculus
Linear algebra or matrix algebra
An introductory statistics course
An introductory programming course

Prerequisite knowledge may be obtained through summer session courses at UVa, courses at another institution, online, or other forms of instruction as approved by the Office of Admission.

Prospective students must complete all parts of the application and submit it by the stated deadlines on the program website. Upon matriculation to the graduate program, the applicant must provide official transcripts.

Masters Degree

Academic Requirements

MSDS students must complete a minimum of 33 graded credits, which include 27 graded credits in core MSDS courses and 6 graded graduate-level elective or concentration credits with a grade of B- or higher.

The residential MSDS full-time program requires students to enroll in 12 credits in their first and second terms and 9 credits in their third term. Students in the online MSDS part-time program enroll in 3 to 9 credits each term; enrollment in at least 6 credits is required for federal financial aid and for recipients of the Quantitative Foundation Fellowship.

The requirements of the master’s degree must be completed in the original modality of study to which the student applied. Students are not permitted to deviate from their original modality of study unless for extenuating circumstances or compelling reasons. Approval is conditional on School resources such as enrollment limits. Petitions are granted upon approval of each MSDS program director and the Associate Dean for Faculty & Academic Affairs.

Time Limitation

All requirements for the master’s degree must be completed within five years from the first term of enrollment.

MSDS Core Requirements

Course Requirements for the MSDS Program:

Python Boot Camp (self-paced, online) or proficiency demonstrated in successful completion of the assessment. Required to complete in the first term of the program.

R Boot Camp (self-paced, online) or proficiency demonstrated in successful completion of the assessment. Required to complete in the first term of the program.

DS 5012 - Computation for Data Science Credits: 3
DS 5030 - Understanding Uncertainty Credits: 3
DS 5110 - Data Engineering II: Big Data Systems Credits: 3
DS 6001 - Data Engineering I: Data Pipeline Architecture Credits: 3
DS 6002 - Ethics of Big Data I Credits: 3
DS 6015 - Data Science Capstone Credits: 3
DS 6021 - Machine Learning I: Introduction to Predictive Modeling Credits: 3
DS 6030 - Machine Learning II: Data Mining & Statistical Learning Credits: 3
DS 6050 - Machine Learning III: Deep Learning Credits: 3

Electives (Residential Modality)

Students must select two courses (six credits minimum) of elective coursework. Selection of elective courses is done in consultation with the program director. There are a variety of electives available, including (but not limited) to those listed below. Elective courses must be at the 5000-level or higher to count for elective credit. Any DS course not in the core offered at the 5000-level or higher counts for elective credit.

DS 5001 - Text as Data Credits: 3
DS 5023 - Interactive Applications Credits: 3
DS 5220 - Advanced Cloud Computing Credits: 3
DS 5320 - Design of Artificial Intelligence Products & Services Credits: 3
DS 6040 - Bayesian Machine Learning Credits: 3
DS 6042 - Machine Learning in Systems and Network Security Credits: 3
DS 6051 - Decoding Large Language Models Credits: 3
DS 6425 - Time Series Modeling Credits: 3
CS 6501 - Special Topics in Computer Science Credits: 3
(Topics approved by SDS)
ECON 7720 - Econometrics II Credits: 4
ECON 8720 - Time Series Econometrics Credits: 3
EVSC 5020 - Introduction to Geographic Information Systems (GIS) Credits: 4
EVSC 7070 - Advanced Use of Geographical Information Systems Credits: 3
SARC 5400 - Data Visualization Credits: 3
STAT 5170 - Applied Time Series Credits: 3
STAT 5310 - Clinical Trials Methodology Credits: 3
STAT 6260 - Categorical Data Analysis Credits: 3
SYS 5581 - Selected Topics in Systems Engineering Credits: 3
SYS 6050 - Risk Analysis Credits: 3
SYS 6582 - Selected Topics in Systems Engineering Credits: 1 to 3 (Topics approved by SDS)

Electives (Online Modality)

Students must select two courses (six credits minimum) of elective coursework. There are a variety of possible electives available, including (but not limited) to those listed below. Elective courses must be at the 5000-level or higher to count for elective credit in the program unless further approval is obtained.

DS 5001 - Text as Data Credits: 3
DS 5002 - How to Train your LLM: Engineering LLMs for Custom Tasks Credits: 3
DS 5003 - Healthcare Data Science Credits: 3
DS 5004 - Applied Reinforcement Learning Credits: 3
DS 5007 - Don’t Invent The Torment Nexus: The History of Technology & Work Credits: 3
DS 5072 - Data Science for Social Impact Credits: 3
DS 5111 - Streamlining Data Science with Software and Automation Skills Credits: 3
DS 5559 - New Course in Data Science Credits: 1 to 4
DS 6040 - Bayesian Machine Learning Credits: 3
SARC 5400 - Data Visualization Credits: 3

Concentrations (Optional for Residential Modality)

Students must complete two of the courses listed and DS 6015 Data Science Capstone with a project aligned to the concentration area to satisfy the requirement.

Sports Data Science

DS 5559 - New Course in Data Science Credits: 1 to 4
Sports & Data (3 credits)

Practicum in Sports Data Science (3 credits)

Total Required Credits: 33

Requirements for the MSDS/MBA Combination

The School of Data Science offers a combination degree program with the University of Virginia Darden Graduate School of Business Administration, in which the student may obtain the MS in Data Science (MSDS) degree and the Master of Business Administration (MBA) degree in three years. All MSDS/MBA students complete a minimum of 60 credits for the MBA and a minimum of 33 credits for the MSDS. Students who complete all coursework to the satisfaction of each respective school and meet the graduation requirements of each school will be awarded two degrees: the Master of Science in Data Science and the Master of Business Administration.

DS 5012 - Computation for Data Science Credits: 3
DS 5030 - Understanding Uncertainty Credits: 3
DS 5110 - Data Engineering II: Big Data Systems Credits: 3
DS 6001 - Data Engineering I: Data Pipeline Architecture Credits: 3
DS 6002 - Ethics of Big Data I Credits: 3
DS 6015 - Data Science Capstone Credits: 3
DS 6021 - Machine Learning I: Introduction to Predictive Modeling Credits: 3
DS 6030 - Machine Learning II: Data Mining & Statistical Learning Credits: 3
DS 6050 - Machine Learning III: Deep Learning Credits: 3
Darden Required Courses grade of B- or higher required
GBUS 7351 - Decision Analysis Credits: 1.5
GBUS 7352 - Decision Analysis - Part II Credits: 1.5
Elective: 5000-level or higher, at least 3 credits

Transfer Credits

The graduate programs normally require students to complete all requirements of the program during their period of enrollment in the program at the University of Virginia. Students may request transfer credit for the elective requirement using the Transfer Credit Request form. A course used to satisfy a prior degree cannot be transferred to the M.S. in Data Science. No more than 6 credits may be transferred. Only courses with a grade of B- or better may be transferred.