Master of Data Science

Collaborative program with the Department of Computer Science

This professional master’s degree program consists of 33 credit hours of coursework including a six credit hour practicum project. The program is designed primarily for those with previous degrees or experience in computer science, statistics, mathematics, the natural or social sciences, or business, and 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 analytical writing score of at least 3.0 are required. The GRE requirement may be 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 (object-oriented programming is required), a data structures and algorithms course at the level of CS 331, multivariate calculus at the level of MATH 251, 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. Proficiency and placement exams are also available.

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 15 credit hours of required core courses, 12 credit hours of elective courses, and six credit hours of Data Science Capstone (see below). At least nine credit hours must be taken of 400- or 500-level CS or CSP courses and nine credit hours of 400- or 500-level MATH courses, not including the Data Science Capstone.

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

The Data Science Capstone comprises three options:

  • Practicum track: Students take CSP 572 Data Science Practicum, working in small teams on real-world data science problems for external clients, advised by faculty.
  • Research track: Students work on a research project with a faculty advisor, taking 6 credits of CS 597 or MATH 594 over two semesters. A project proposal needs to be approved in advance by the director of the Master of Data Science program.
  • Coursework track: Students take 6 credits of Application Courses or Data Science Electives.
Data Science Core Courses (15)
MATH 563Mathematical Statistics3
or MATH 564 Regression
CS 584Machine Learning3
or MATH 569 Statistical Learning
SCI 522Public Engagement for Scientists3
CSP 571Data Preparation and Analysis3
Select a minimum of one course from the following:3
Advanced Database Organization3
Data-Intensive Computing3
Big Data Technologies3
Data Science Capstone (6)
6 credit hours of capstone, depending on track6
Data Science Electives (12)
12 credit hours of Data Science Electives12
Total Credit Hours33

Data Science Capstone

Practicum Track (6)
CSP 572Data Science Practicum6
Research Track (6)
CS 597Reading and Special Problems6
or MATH 594 Professional Master's Project
Coursework Track (6)
6 credits of Applications Courses or Data Science Electives6

Data Science Electives

Computational Fundamentals
Database Organization3
Introduction to Algorithms3
Operating Systems3
Data Integration, Warehousing, and Provenance3
Advanced Database Organization3
Data Privacy and Security3
Design and Analysis of Algorithms3
Combinatorial Optimization3
Parallel and Distributed Processing3
Cloud Computing3
Data-Intensive Computing3
Software Testing and Analysis3
Big Data Technologies3
Computer Science Applications
Data Mining3
Computer Vision3
Geospatial Vision and Visualization3
Advanced Data Mining3
Information Retrieval3
Cyber-Physical Systems: Languages and Systems3
Cyber-Physical Systems Security and Design3
Deep Learning3
Interactive and Transparent Machine Learning3
Online Social Network Analysis3
Advanced Artificial Intelligence3
Probabilistic Graphical Models3
Machine Learning3
Natural Language Processing3
Mathematics, Probability, and Statistics
Graph Theory and Applications3
Introduction to Stochastic Processes3
Design and Analysis of Experiments3
Mathematical Modeling I3
Mathematical Modeling II3
Mathematical Modeling3
Linear Algebra3
Optimization I3
Stochastic Processes3
Introduction to Time Series3
Mathematical Statistics3
Monte Carlo Methods3
Multivariate Analysis3
Advanced Design of Experiments3
Statistical Learning3
Machine Learning in Finance: From Theory to Practice 3
Bayesian Computational Statistics3
Mathematical Methods for Algorithmic Trading3
Mathematical and Scientific Computing
Partial Differential Equations3
Stochastic Dynamics3
Computational Mathematics I3
Computational Mathematics II3
Meshfree Methods3
Computational Physics3
Professional Skills
Project Management3
Public Engagement for Scientists3
Fundamentals of Design3
User Experience Research and Evaluation3

Applications Courses

BIOL 440Neurobiology3
BIOL 550Bioinformatics3
BME 433Biomedical Engineering Applications of Statistics3
BME 504Neurobiology2
BME 506Computational Neuroscience II: Vision3
BME 507Cognitive Neuroscience2
BME 538Neuroimaging3
BME 545Quantitative Neural Function3
BUS 510Strategic Management3
BUS 550Business Statistics3
CAE 576Applications of Unmanned Aerial Vehicles (UAVs or "Drones") for Construction Projects3
CHE 560Statistical Quality and Process Control3
COM 501Introduction to Linguistics3
COM 583Social Networks3
COM 584Humanizing Technology3
ECE 563Artificial Intelligence in Smart Grid3
FDSN 401Nutrition, Metabolism, and Health3
FDSN 408Food Product Development3
FDSN 410Food Plant Operations3
FDSN 435Performance Management in Food Operations3
MAX 501Digital Marketing3
MAX 522Predictive Analytics3
MAX 523Social Media Marketing Analytics3
MAX 526Quantitative Marketing Models3
MMAE 440Introduction to Robotics3
MMAE 500Data Driven Modeling3
MMAE 540Robotics3
MSF 502Statistical Analysis in Financial Markets3
MSF 503Financial Modeling3
PHIL 551Science and Values3
PHIL 574Ethics in Computer Science3
PSYC 423Learning Theory3
PSYC 426Cognitive Science3
PSYC 503Cognitive and Affective Bases3
SSCI 422Complex Organizations3

Master of Data Science Curriculum

Year 1
Semester 1Credit HoursSemester 2Credit HoursSemester 3Credit Hours
CS 525, 554, or CSP 5543CS 584 or MATH 5693CSP 5726
MATH 563 or 5643CSP 5713 
SCI 5223Data Science Elective3 
 9 9 6
Year 2
Semester 1Credit Hours  
SCI 5113  
Data Science Elective3  
Data Science Elective3  
Total Credit Hours: 33