Current Students

The MS in Data Science program provides a strong foundation in the science of Big Data and its analysis by gathering in a single program the knowledge, expertise, and educational assets in data collection and management, data analytics, scalable data-driven pattern discovery, and the fundamental concepts behind these methods. Students who graduate from this 31 credit hour, 2 year master's program will learn the state-of-the-art methods for treating Big Data, be exposed to the cutting edge methods and theory forming the basis for the next generation of Big Data technology, and will complete a project demonstrating that they can use fundamental concepts to design innovative methods for new application areas arising from business, government, security, medicine, biology, physical sciences, and the environment.

Program Information

Orientation Information


The Data Science MS is a plan B track program with a final oral exam and capstone project.The program requires a total of 31 credits consisting of 6 credits each from the three emphasis areas: statistics, algorithms, and infrastructure and large scale computing; 6 credits in approved electives; 1 credit of research colloquium; and 6 credits for the capstone project.

Credit Requirements

Students take two courses from each of three tracks for a total of 18 credits:

Students must take the remaining 13 credits from the following areas:

  • Electives (6 cr) 2 courses from any track or any other course related to Data Science with advisor & DGS approval (6 cr).
  • Capstone Project (6 cr) This year-long project would be supervised by a faculty member, with approval by a faculty committee.
  • Colloquium (1cr) Research Colloquium (1 cr). This seminar would have a mix of outside speakers and capstone project presentations.

TOTAL CREDITS: 31 credits

Capstone Project

Students must complete a capstone project supervised by a faculty member.

Data Science Graduate Handbook

This handbook is intended to be a focal point of information for data science graduate students and their advisors.

Data Science Graduate Handbook (2016-2017) (PDF)