Master of Data Science

Collaborative program with the Department of Applied Mathematics

This professional master’s degree program consists of 33 credit hours of coursework, including a practicum, in data science. The program is designed primarily for those with previous degrees or experience in computer science, statistics, mathematics, natural sciences, or business, who are interested in preparing for a career as a data science professional in business and industry. Full-time students may complete the program in one year, including one summer term.

Admission Requirements

Applicants should have a bachelor’s degree from an accredited university with a minimum cumulative GPA of 3.0/4.0. A combined verbal and quantitative GRE examination score of at least 304 and an analytic writing score of at least 3.0 (for the post-October 2002 test) are required. The GRE requirement is waived for students with a bachelor’s degree from an accredited college or university in the United States with a cumulative GPA of at least 3.0/4.0.

Prerequisites include knowledge of a high level programming language at the level of CS 201 (Java or C/C++programming is required), a data structures course at the level of CS 331, experience with database programming at the level of CS 425, linear algebra at the level of MATH 332, and probability and statistics at the level of MATH 474. Information on these courses is available in this catalog.

Students with an insufficient background in computer science and/or mathematics will be required to take the relevant prerequisite courses and earn at least a B grade in each. These prerequisite courses do not count toward the 33 credit hour requirement.


Coursework includes 18 credit hours of required core courses and 6 credit hours of CSP 572/MATH 572 Data Science Practicum. At least 9 credit hours must be taken of 500-level CS or CSP courses and 9 credit hours of 500-level MATH courses, not including the CSP 572/MATH 572 Data Science Practicum.

Up to 6 credit hours of 400-level undergraduate coursework may be used toward degree requirements.

Data Science Core Courses (18)
CS 525Advanced Database Organization3
or CS 554 Data-Intensive Computing
MATH 563Mathematical Statistics3
or MATH 564 Applied Statistics
SCI 511Project Management3
SCI 522Public Engagement for Scientists3
CS 584Machine Learning3
or MATH 569 Statistical Learning
CSP 571Data Preparation and Analysis3
or MATH 571 Data Preparation and Analysis
Data Science Capstone (6)
CSP/MATH 572Data Science Practicum6
Data Science Electives (9)
Select 9 credit hours of Data Science Electives9
Total Credit Hours33

Data Science Electives

Computational Fundamentals (27)
CS 425Database Organization3
CS 430Introduction to Algorithms3
CS 450Operating Systems3
CS 525Advanced Database Organization3
CS 535Design and Analysis of Algorithms3
CS 546Parallel and Distributed Processing3
CS 553Cloud Computing3
CS 554Data-Intensive Computing3
CS 589Software Testing and Analysis3
Computer Science Applications (33)
CS 422Data Mining3
CS 512Computer Vision3
CS 513Geospatial Vision and Visualization3
CS 522Advanced Data Mining3
CS 529Information Retrieval3
CS 556Cyber-Physical Systems: Languages and Systems3
CS 557Cyber-Physical Systems: Networking and Algorithms3
CS 579Online Social Network Analysis3
CS 583Probabilistic Graphical Models3
CS 584Machine Learning3
CS 585Natural Language Processing3
Mathematics, Probability, and Statistics (33)
MATH 454Graph Theory and Applications3
MATH 486Mathematical Modeling I3
MATH 532Linear Algebra3
MATH 540Probability3
MATH 542Stochastic Processes3
MATH 553Discrete Applied Mathematics I3
MATH 554Discrete Applied Mathematics II3
MATH 565Monte Carlo Methods in Finance3
MATH 567Advanced Design of Experiments3
MATH 569Statistical Learning3
MATH 574Bayesian Computational Statistics3
Mathematical and Scientific Computing (15)
BIOL 550Bioinformatics3
MATH 577Computational Mathematics I3
MATH 578Computational Mathematics II3
MATH 590Meshfree Methods3
PHYS 440Computational Physics3