Graduate curriculum

The Data Science M.S. 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 (3 credits of electives must be 8000 level); 1 credit of research colloquium; and 3 credits for the capstone project.

Major coursework credits

See courses for a list of classes satisfying each category. Sample graduate program outline for semester planning assistance.

Statistics track credits 6 credits
Algorithmics track credits 6 credits
Infrastructure and large scale computing track credits 6 credits
Elective credits (at least 3 credits of the 9 credits must be 8000 level) 9 credits
Capstone credits (off-campus research must be approved by the Graduate Committee) 3 credits
Colloquium credits
One credit of the Data Science Colloquium (or equivalent in a participating department) is mandatory and must appear on the student’s graduate degree plan form.
1 credit
Total credits for the degree 31 credits
Minimum course credits that must be taken at the University of Minnesota 19 credits


Note: It is acceptable to take only 6 credits of electives and carry out a 6 credit capstone project spread over two semesters if your project advisor agrees on the scope of your project. This "6-6" plan is the one generally followed by students admitted before 2017.  All other students will follow the "9-3" plan unless they explicitly opt for the "6-6" plan with their advisor's concurrence.

Capstone project

One of the key features of the M.S. in Data Science curriculum is a capstone project that makes the theoretical knowledge gained in the program operational in realistic settings.

Under the supervision of a faculty member, students will go through the entire process of solving a real-world problem: from collecting and processing real-world data, to designing the best method to solve the problem, and finally, to implementing a solution. The problems and datasets that students engage with will come from real-world settings identical to what you might encounter in industry, academia, or government. Examples of projects and the wide variety of topics they cover can be found on the research page.

The Plan B project is completed under the guidance of a data science faculty member. Students are responsible for identifying and selecting their faculty advisor.  Data Science M.S. students will present a final poster at the annual Poster Fair on their Plan B project as part of their degree requirements. The annual Poster Fair takes place every spring semester. Questions on this process can be directed to the Graduate Program Coordinator.

A qualified advisor from outside the data science faculty may be selected with DGS approval. You may be asked to provide a CV for that potential advisor. Your final project report will be approved by a committee of three faculty including your advisor and including at least one member of the data science faculty. If not already on the data science faculty, your advisor may like to join (they should send a short CV to the DGS), otherwise you will need to find a current member acting as a co-advisor to approve the final report. In any case, the three committee members should represent at least two different departments. You will also be expected to give a short oral presentation on your project open to faculty, students, and other interested parties.

Academic program information

All credits listed on the Graduate Degree Plan must be 5000 level or above, with a GPA of at least 3.25. You must maintain an overall GPA of 3.0 while a graduate student in this program.

This program may be completed with a minor.

Use of 4xxx courses towards program requirements is not permitted (except as an elective by special petition).