Program: Data Science, B.S. - University of Virginia

Mar 29, 2025

Undergraduate Record 2024-2025

Return to: School of Data Science: Degree Programs

The B.S. in Data Science (BSDS) will prepare students to serve in the public and private sectors across industries. Students will gain a strong foundation in core data science tools, methodologies and approaches, alongside a deep understanding of the ethical dimensions of data systems and solutions. The program will educate students in the four domains of data science, as defined by the School of Data Science: value, design, analytics, and systems.

General Education Requirements (33 credits)

The School of Data Science will accept students from any of the College of Arts & Sciences general education curricular programs (Echols Scholars Program, Engagements Pathway, Disciplines Plus Pathway or Forums curriculum) and any other school of enrollment at UVA. Search course attributes in SIS for a list of courses that count toward general education requirements.

Courses from the Data Science core may also be used to satisfy general education requirements.

The School of Data Science waives general education curricular requirements for Echols Scholars.

Rhetoric for the 21st Century: 6 credits required
DS 4022 - Data Science Project - Major Credits: 3
Satifies the Second Writing Requirement.
Quantification, Computation, and Data Analysis: 6 credits required
DS 2026 - Computational Probability Credits: 3
Satisfies 3 credits of the Quantification, Computation, and Data Analysis Requirement.
Artistic, Interpretive, and Philosophical Inquiry: 3 credits required
Chemical, Mathematical, and Physical Universe: 3 credits required
Cultures and Societies of the World: 3 credits required
Historical Perspectives: 3 credits required
Living Systems: 3 credits required
Social and Economic Systems: 3 credits required
Science and Society: 3 credits required
DS 1001 - Foundation of Data Science Credits: 3
Satisfies the Science and Society Requirement.

Prerequisites

Completion of 24 credit hours at the University of Virginia by the end of the Spring of a student’s first year. This may include general education credits, credits toward another major or minor, and electives.

Foundation of Data Science

Must be taken at UVA and completed with a grade of C- or higher.

DS 1001 - Foundation of Data Science Credits: 3

Programming Requirement

There are multiple ways this requirement can be met.

Enrollment in one of the following courses at UVA:
DS 1002 - Programming for Data Science Credits: 3
CS 1110 - Introduction to Programming Credits: 3
CS 1111 - Introduction to Programming Credits: 3
CS 1112 - Introduction to Programming Credits: 3
CS 1113 - Introduction to Programming Credits: 3
PHYS 1655 - Introduction to Python for Scientists and Engineers Credits: 3
OR
Test credit
- AP Computer Science A with a score if 4 or 5
- IB HL Computer Science with a score of 5, 6, or 7
OR
Passing the CS 1110 Place-Out Test

Students who pass the test receive a notation in SIS. The School of Data Science encourages students to complete the place-out test in Python. Students must be proficient in Python prior to the start of classes.

Program Requirements

CREDIT/GPA MINIMUMS

Number of DS Course Credits required: 49
Number of Credits required to be taken at UVA: 60
Minimum CUM GPA to graduate: 2.00
Total Credits required: 120 credits

Core Curriculum (40 credits)

Due to course sequencing and availability, students may not withdraw from a core course. Students must take courses for a letter grade and achieve a grade of D or better.

DS 2022 - Systems I: Intro to Computing - Major Credits: 3
DS 2023 - Design I: Communicating with Data - Major Credits: 3
DS 2024 - Value I: Ethics & Policy in Data Science - Major Credits: 3
DS 2026 - Computational Probability Credits: 3
DS 3021 - Analytics I: Machine Learning I, Foundational Concepts - Major Credits: 3
DS 3022 - Data Engineering Credits: 3
DS 3025 - Mathematics for Data Science Credits: 4
DS 3026 - Principles of Inference and Prediction Credits: 3
DS 4021 - Analytics II: Machine Learning Credits: 3
DS 4024 - Value II: Explainable AI Credits: 3
DS 4022 - Data Science Project - Major Credits: 3
DS 4320 - Data by Design Credits: 3
One of the following Math courses
MATH 1190 - A Survey of Calculus I with Algebra Credits: 4
MATH 1210 - A survey of Calculus I Credits: 3
MATH 1310 - Calculus I Credits: 4
The Math requirement can also be satisfied through test credit (AB or BC Calculus with a score of 4 or 5, or IB HL Mathematics with a score of 5, 6, or 7), transfer credit allowed by the College of Arts & Sciences, and a higher-level Calculus course.

Exceptions & Substitutions

Due to course sequencing, availability, and program learning outcomes, the School does not allow exceptions or substitutions within the core course requirements.

Concentrations (9 credits)

All students must select at least one concentration from among several options offered within the School of Data Science. Students may not double-count courses across concentrations.

Students will select three courses for their chosen concentration.

School of Data Science Concentrations (must select at least 1)

Analytics Concentration

DS 4121 - Foundations of Text Analytics Credits: 3
DS 4125 - Introduction to Deep Learning Credits: 3
DS 4126 - Computer Vision Credits: 3
DS 5122 - Large Language Models Credits: 3

Systems Concentration

DS 4220 - IoT and Sensor Data Credits: 3
DS 4221 - Advanced Databases Credits: 3
DS 5220 - Advanced Cloud Computing Credits: 3
DS 5221 - Stream Processing Credits: 3

Design Concentration

DS 4023 - Data Design II: Interactive Applications Credits: 3
DS 4329 - Data Design Studio Credits: 3
DS 5320 - Human-Centered Design Credits: 3

Value Concentration

DS 4422 - Technology Regulation and Data Science Credits: 3
DS 4423 - Data, Technology, and Society Credits: 3
DS 5420 - Mainframes to Memes (Past, Present, & Future of Information Technology) Credits: 3

Collaborative Concentrations (may select 1 or more)

Educational Analytics Concentration

Educational Analytics, in partnership with the School of Education and Human Development

DS 4121 - Foundations of Text Analytics Credits: 3
OR
DS 4221 - Advanced Databases Credits: 3
EDLF 2080 - Fundamentals of Health and Social Sciences Statistics Credits: 3
EDLF 4110 - Numbers Are People: The Ethics of Data-driven Decision-making in Education Credits: 3

Electives

Students are encouraged to explore courses of interest and majors and minors in other disciplines.

Elective Course Options

Potential electives may include DS concentration courses not used toward a concentration or the following:

AAS 1010 - Introduction to African-American and African Studies I Credits: 4
ARTH 2055 - Introduction to Classical Archaeology Credits: 3 to 4
ASTR 1280 - The Origins of Almost Everything Credits: 3
COMM 2600 - Leadership Across the Disciplines Credits: 3
EDHS 1120 - So You Want to Change the World: Foundations of Community Engagement Credits: 3
EVSC 3020 - GIS Methods Credits: 4
HIEA 2031 - Modern China Credits: 3
MATH 3100 - Introduction to Probability Credits: 3
MDST 2700 - News Writing Credits: 3
PHIL 1410 - Forms of Reasoning Credits: 3
PHYS 1050 - How Things Work Credits: 3
PLIR 2050 - Introduction to Political Economy Credits: 3
PSYC 2600 - Introduction to Social Psychology Credits: 3
SOC 2442 - Systems of Inequality Credits: 3