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.


The Data Science MS is a plan B track program with a capstone project culminating in a final written report and oral presentation. 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; 9 credits in approved electives; 1 credit of research colloquium; and 3 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 (9 cr) 3 courses from any track or any other course related to Data Science with advisor & DGS approval (9 cr) (3 credits must be 8xxx level: a regular 8xxx course or a second semester of your capstone project).
  • Capstone Project (3-6 cr) This project (one or two semesters) 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.

Post-Baccalaureate Certificate

Students enrolled in the Certificate Program must complete one Tier I course from each track, plus one course from any track to complement the student's background, approved by the DGS, for a total of 4 courses (12 credits). Transfer courses are not allowed. Courses taken as part of the Certificate program may be used toward the Data Science M.S. or any other U of MN masters or doctoral degree that will accept them.

Data Science Graduate Handbook

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

Graduate Handbook (2019-2020) (PDF) *for Computer Science and Data Science graduate programs

Public Disclosures

Student Complaint Resolution