MS in Data Science


Students may be admitted in full graduate standing to the MS in data science program if they have a bachelor’s degree in computer science or any related engineering discipline (please see required topics below). Students who have a bachelor’s degree in other quantitative disciplines (mathematics, physics or other STEM disciplines) with demonstrated quantitative skills (calculus, linear algebra, etc.) and proficiency in computer programming may be admitted on a conditional basis.

To be considered for admission to the program the minimum requirements are:

  • Student must have earned a GPA of at least 3.000 (or an equivalent score from another country) in the bachelor's degree.
  • Students whose bachelor’s degree is from an institution outside the U.S. are required to submit official scores of the GRE General Test along with the admission application. While we do not set a minimum score, we would like the quantitative portion of the GRE to be above average.

Application materials will be reviewed by the Graduate School and the MS in data science graduate coordinator, after which the student will be notified of their decision. Students entering the MS in data science program are expected to have already completed courses in programming, linear algebra, statistics and data structures. If prior coursework deficiencies exist, then the student may be admitted on a conditional basis. It is recommended that deficiencies are completed prior to beginning graduate studies.

Program Requirements

Course Title Hours
Core Courses
CS 746Perspectives on Data Science3
BSAN 775Introduction to Business Analytics3
MATH 746Introduction to Data Analytics3
CS 770Machine Learning3
CS 896Capstone Project in Data Science3
Data Science Elective Courses
Select 9 credit hours from the list of classes below.9
Introduction to Database Systems
Artificial Intelligence
Advanced Topics in Data Storage
Data Visualization
Introduction to Linear Data Modeling
Neural Networks and Deep Learning
Algorithms and Applications on Graphs
Advanced Topics in Machine Learning
Introduction to Intelligent Robotics
Image Analysis and Computer Vision
Artificial Intelligence for Robotics
Deep Learning
Discipline Elective Courses
Select 6 credit hours from the list of classes below.6
Any of the courses listed in Data Science Electives.
Data Visualization
Applied Regression Analysis
Analysis of Variance
Applied Statistical Methods II
Neural Networks and Machine Learning
Bayesian Statistics and Uncertainty Quantification
Analytics and Decision Making In Sport
Big Data Analytics in Engineering
Introduction to Data Mining and Analytics
Database Planning & Management
Advanced Business Analytics
Total Credit Hours30

The graduate coordinator should be consulted by students who would like to substitute other CS courses for any of the elective courses above (core courses cannot be substituted). Such consultations should be made before taking a course. CS 891, CS 892 and CS 893 cannot be applied under any circumstances to this degree program.

Applied Learning

Students in the MS in data science program are required to complete an applied learning or research experience to graduate from the program. The requirement can be met by completing the mandatory course  CS 896 Capstone Project in Data Science.