MS in Data Science
Admission
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 746 | Perspectives on Data Science | 3 |
| BSAN 775 | Introduction to Business Analytics | 3 |
| MATH 746 | Introduction to Data Analytics | 3 |
| CS 770 | Machine Learning | 3 |
| CS 896 | Capstone Project in Data Science | 3 |
| 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 Hours | 30 | |
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.