Data Science Majors and Minors


Today's world is data-driven. Data science is an exciting field, with its emphasis on using data to describe and understand the world around us. It applies ideas and methods from statistics, computer science, and optimization to find innovative solutions to important problems in many areas. Graduates that combine data science knowledge with fields like management science, humanities, social sciences, or engineering are in high demand.

The major in data science requires a minimum of 36 credit hours (12 courses). Given the interdisciplinary nature of the study of data science, students are required to take a second major in any discipline. Students obtaining a B.S. degree in data science may not also obtain a B.A. degree in mathematics.

The minor in data science requires a minimum of 24 credit hours (8 courses).

See the Interdisciplinary Programs section of the Undergraduate Catalog at for the most up-to-date information.

Faculty who wish to submit a course for inclusion within the undergraduate Data Science curriculum may do so using the Data Science Course Proposal Form.

Peter K. Moore
Senior Advisor to the Provost, Director of the Data Science Institute ad interim, Professor of Mathematics, Data Science Committee Chair
Amit Basu
Professor of Information Technology and Operations Management, Carr P. Collins, Jr. Chair in Management Information Science, Department of Information Technology and Operations Management Chair
Sila Çetinkaya
Professor of Operations Research and Engineering Management, Cecil H. Green Professor of Engineering, Department of Operations Research and Engineering Management Chair
Ira Greenberg
Professor of Art and Creative Computation
Tom Hagstrom
Professor of Mathematics
Michael Hahsler
Clinical Associate Professor of Computer Science; Clinical Associate Professor of Operations Research and Engineering Management
Eric Larson
Assistant Professor of Computer Science
Monnie McGee
Associate Professor of Statistics
Chul Moon
Assistant Professor of Statistics
Lynne Stokes
Professor of Statistics, Dedman Family Distinguished Professor of Statistical Science
Jia Zhang
Professor of Computer Science, Cruse C. and Marjorie F. Calahan Centennial Chair in Engineering

Data Matters

In this talk, Dr. Melissa Cefkin will discuss how data is made and used in industry research and product design contexts. What kinds of data are used, and how? How are data made trustworthy and useful? Does big data always trump small? Based on her career working as a social scientist amongst computer and data scientists, she will draw on examples from a diversity of application areas, from the development of non-human drivers (for automated cars) to understanding the employee experience, from interface design to policy.

Date: Thursday, April 28, 2022
Time: 3:00PM - 4:00PM
Location: Dallas Hall, McCord Auditorium
Open to: SMU Faculty, Staff, and Students

Driving, Data and Design: Anthropological Forays in Industry

Self-driving cars. Smart cities. On-demand work platforms. ‘Intelligent’, sensor-based technical innovations are everywhere, promising great change. Though peoples’ participation with such innovations may be fleeting and invisible, their societal implications are potentially vast. Dr. Melissa Cefkin will reflect on her experience working as an applied social researcher, designer and manager at the center of such developments. Dr. Cefkin has been at the forefront of advancing a critical engagement between the practices of business and technology and the social and human sciences.

Date: Thursday, April 29, 2022
Time: 10:00AM - 11:00AM
Location: CROW 190
Open to: SMU Faculty, Staff, and Students