B.S. in Data Science Overview

Curriculum

The curriculum will provide students with 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. Students will learn key considerations and approaches for supporting complex data processes by studying computational probability, statistical inference and prediction, and related mathematical concepts. 

The core curriculum will instill strong technical knowledge and skills, alongside a deep understanding of how data policy and ethics impact and inform possible solutions to organizational challenges. Students will also learn to develop data presentations, applications, and dashboards, with a focus on communicating with and about complex datasets and findings.  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. 
 

The Undergraduate Record represents the official repository for academic program requirements. 

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.

Program Requirements 

First-Year: 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: Foundations 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.

*CS 1110/1111/1112/1113: Introduction to Programming, a score of 4 or 5 on AP Computer Science A, or a score of 5, 6, or 7 in IB HL Computer Science will be accepted in lieu of DS 1002. 

BSDS Year 1

Fall Semester
MATH 1210 - A Survey of Calculus (3 credits)
DS 2003 - Data Design I (3 credits)
DS 2006 - Computational Probability (3 credits)
Elective (3 credits)
Elective (3 credits)

Spring Semester
DS 2002 - Data Systems I (3 credits)
DS 2004 - Data Value I (3 credits)
DS 3005 - Mathematics for Data Science (4 credits)
General Education Requirement (3 credits)
Elective (2 credits)

BSDS Year 2

Fall Semester
DS 3001 - Data Analytics I (3 credits)
DS 3002 - Data Systems II (3 credits)
General Education Requirement (3 credits)
Elective (3 credits)
Elective (3 credits)

Spring Semester
DS 3006 - Principles of Inference (3 credits)
DS 4003 - Data Design II (3 credits)
DS 4004 - Data Value II (3 credits)
General Education Requirement (3 credits)
Elective (3 credits)

BSDS Year 3

Fall Semester
DS 4001 - Data Analytics II (3 credits)
General Education Requirement (3 credits)
Elective (3 credits)
Elective (3 credits)
Elective (3 credits)

Spring Semester
DS 4005 - Final Project (3 credits)
General Education Requirement (3 credits)
Elective (3 credits)
Elective (3 credits)
Elective (3 credits)

Concentrations

All students will select at least one concentration (3 classes/ 9 credits) from among several different options offered within the School of Data Science and in collaboration with other schools across Grounds. Students may elect to complete more than one concentration. 

Concentration Options (select 3 courses for each concentration): 

Analytical Concentration (Data Analytics) 
DS 400x – Computer Vision  
DS 400x – Remote Sensing  
DS 4008 – Data Science Algorithms and Computational Principles 
DS 500x – Natural Language Processing  
DS 5559 – Exploratory Text Analytics  
DS 6050 – Machine Learning: Deep Learning  
 
Systems Concentration (Data Engineering) 
CS 4444 – Introduction to Parallel Computing 
DS 5011 – Big Data Systems 
DS 400x – Advanced Data Engineering/DevOps 
DS 4008 – Data Science Algorithms and Computational Principles 
 
Design Concentration (Data Design) 
DS 400x – Computer Vision  
DS 400x – Geospatial Mapping 
DS 400x – High Dimensional Data Visualizations 
DS 5559 – Exploratory Text Analytics 
 
Value Concentration (Ethical/Data Activist) 
DS 400x – Explainable Machine Learning 
DS 400x – Reproduceable Research 
DS 5559 – Exploratory Text Analytics  
DS 6050 – Machine Learning: Deep Learning 
 
Educational Analytics, offered in partnership with the School of Education and Human Development  
DS 5559 – Exploratory Text Analytics  
ED 400x – Learning Analytics and Data Visualizations 
ED 400x – Educational Data Ethics 
 

General Education Requirements

In addition to the BSDS curriculum, all BSDS students must complete the following general education courses:

General Education Requirements: 33 credit hours 

  • Rhetoric for the 21st Century: 6 credits

  • Quantification, Computation, and Data Analysis: 6 credits

  • Artistic, Interpretive, and Philosophical Inquiry: 3 credits 

  • Quantification, Computation, and Data Analysis: 6 credits 

    • DS 2006 (3 credits) and DS 3005 (3 credits) satisfy the QCD requirement

  • The Chemical, Mathematical, and Physical Universe: 3 credits

  • Cultures and Societies of the World: 3 credits

  • Historical Perspectives: 3 credits

  • Living Systems: 3 credits

  • Social and Economic Systems: 3 credits

  • Science and Society: 3 credits

BSDS students are not required to complete the World Languages literacy; any foreign language courses are optional and would count toward electives. 

View All