Academics — School of Data Science

B.S. in Data Science Overview

Curriculum

The BSDS curriculum provides a solid foundation in the key data science principles and applications necessary to support organizational data needs and strategies. Students will gain proficiency in the fundamentals of data science programming, mathematical and analytical algorithms, data systems and pipelines, and data visualization and presentation.

With an emphasis on developing students’ ability to tackle real-world problems, the core coursework will equip students to understand and correctly apply data science in a broad range of organizations and contexts.

Learning Outcomes

Pursuing a B.S. in Data Science will prepare you to become an expert in the field and work at the cutting edge of a new discipline. According to LinkedIn’s most recent Emerging Jobs Report, data science is booming and data scientist is one of the top three fastest-growing jobs. A B.S in Data Science from the University of Virginia opens career paths in public or private industry. Graduates of our program will:

Identify, formulate, and solve complex problems by applying principles of data analytics, mathematics, systems, value, and design
Effectively communicate data products and findings to a range of audiences
Assess and diagnose ethical and professional conflicts in data science to make informed judgments
Appreciate the benefit of diverse perspectives when working within and leading data science teams
Lead and complete data-driven projects by establishing clear goals, planning tasks, and meeting objectives

The B.S. in Data Science degree program will require 120 credit hours. A final project course will be required. The Undergraduate Record represents the official repository for academic program requirements.

Program Requirements

Prerequisites for Admission

Prerequisites

The BSDS program has prerequisites of the following two courses, which must be completed or in progress at the time of application:

DS 1001: Foundation of Data Science (3 credits)
DS 1002: Programming for Data Science (3 credits)*

Both courses are offered in the fall and spring semesters, do not have prerequisites, and may be taken concurrently. Students interested in pursuing the BSDS program must take both courses in their first-year to be eligible to apply.

Learn more about the application process on our Admissions page.

*Refer to our FAQs for alternatives to DS 1002.

BSDS Curriculum

Once admitted to the program, BSDS students will follow a three-year curriculum. Courses used to satisfy requirements in the Data Science Minor (DS 2002, DS 2003, DS 2004, DS 3001, & DS 4002) do not fulfill requirements in the B.S. of Data Science.

First Year of Major: Understand

Fall

DS 2022 – Systems I: Intro. to Computing
DS 2023 – Design I: Communicating with Data
DS 2026 – Computational Probability
MATH 1190/1210/1310 or APMA 1090 – Calculus I*

Spring

DS 2024 – Value I: Ethics & Policy in DS
DS 3021 – Analytics I: Machine Learning I, Foundational Concepts
DS 3025 – Mathematics of DS

Second Year of Major: Apply

Fall

DS 3022 – Data Engineering
DS 4021 – Analytics II: Machine Learning

Spring

DS 3026 – Principles of Inference & Prediction
DS 4320 – Data by Design
DS 4024 – Value II: Explainable AI

Third Year of Major: Analyze, Evaluate, Create

Fall

Concentration Course
Concentration Course
Concentration Course

Spring

DS 4022 – Data Science Project
2nd Concentration Course (Optional)
2nd Concentration Course (Optional)

*AB or BC Calculus with a score of 4 or 5, or IB HL Mathematics with a score of 5, 6, or 7 will also meet this requirement.

Concentrations

Tailor your degree to match your interests and career goals with one or more dynamic concentrations. These concentrations not only prepare you for industry roles and research opportunities, but also empower you to make your degree as unique as your ambitions.

All students will select at least one core concentration from the School of Data Science. Students may elect to pursue multiple concentrations, including adding a collaborative concentration. All concentrations require 3 classes/ 9 credits; students may not double-count courses across concentrations.

School of Data Science Core Concentrations
All students are required to select one of the following:

Analytics
Systems
Design
Value

Collaborative Concentrations
Students may elect to add one or more of the following collaborative concentrations:

Analytical Accounting
Astronomy
Environmental Science
Human Movement and Physiology
Mathematics
Neuroscience

Visit the Concentrations page for more information.

General Education Requirements

First-year prospective BSDS students are advised to stay on track with the curricular requirements of their home school; any course completed that does not count toward the School of Data Science's General Education Requirements will count toward overall degree credits. Additionally, we strongly encourage first-year students to take their First Writing Requirement and Calculus I. Refer to the BSDS FAQs for more information. The Undergraduate Record is the official repository for all academic information, including the full list of BSDS General Education Requirements.

Careers in Data Science

According to the U.S. Bureau of Labor Statistics, employment of data scientists is expected to grow by 36% over the next decade with about 20,800 annual job openings. The B.S. in Data Science prepares students to become experts in the field, work at the cutting edge of a new discipline, and thrive in a data-centric world.

In addition to UVA Career Center, students in the BSDS have access to the School of Data Science's career resources, including: