Curriculum

About the Program

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 two 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.

Curriculum

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.

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 9 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; 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.  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.

Statistics Track 6
Credits
Algorithmics Track 6
Credits
Infrastructure Track 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

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).

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 4
Colloquium (1cr) 1
Elective 3
Total Credits 14

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
Elective (8000 level) 3
Total Credits 12

Semester 3

Course Title Credits
Elective 3
Capstone Project 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 4
Colloquium (1cr) 1
Total Credits 11

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
Elective (8000 level) 3
Total Credits 6

Semester 4

Course Title Credits
Elective 3
Capstone Project 3
Total Credits 6

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

Refunds