Curriculum

Capstone Project

Students must complete a capstone project supervised by a faculty member. One of the key features of the MS in Data Science curriculum is a capstone project that makes the theoretical knowledge gained in the program operational in realistic settings. During the project, you 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 you’ll 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.

A qualified advisor from outside Data Science 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.

Data Science Major Coursework Credits

See Courses for a list of classes satisfying each category.

The Data Science program is 31 credits, 18 of which are required courses from three different tracks and 6 of which are electives. 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.

Statistics Track 6
Credits
Algorithmics Track 6
Credits
Infrastructure Track 6
Credits
Elective Credits 6
Credits
Capstone Credits
Off-Campus research must be approved by the Graduate Committee
6
Credits
Colloquium Credits
One credit of the Data Science Colloquium (or equivalent in a participating department) is mandatory and must appear on 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

Academic Program Information

All credits 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).

Sample Program Outline - 3 Semester option

Semester 1

Course Title Credits
CSCI 5523 - Introduction to Data Mining 3
CSCI 5707 - Principles of Database Systems 3
STAT 5302 - Applied Regression Analysis 3
Colloquium (1cr) 1
Elective 3
Total Credits 13

Semester 2

Course Title Credits
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming 3
EE 5239 - Introduction to Nonlinear Optimization 3
STAT 5401 - Applied Multivariate Methods 3
Capstone Project (first half) 3
Total Credits 12

Semester 3

Course Title Credits
Elective 3
Capstone Project (second half) 3
Total Credits 6

Sample Program Outline - 4 Semester option

Semester 1

Course Title Credits
CSCI 5523 - Introduction to Data Mining 3
CSCI 5707 - Principles of Database Systems 3
STAT 5302 - Applied Regression Analysis 3
Colloquium (1cr) 1
Total Credits 10

Semester 2

Course Title Credits
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming 3
EE 5239 - Introduction to Nonlinear Optimization 3
STAT 5401 - Applied Multivariate Methods 3
Total Credits 9

Semester 3

Course Title Credits
Elective 3
Capstone Project 3
Total Credits 6

Semester 4

Course Title Credits
Elective 3
Capstone Project 3
Total Credits 6