Master of Science in Data Science
Master of Science (MS) in Data Science:
The Master of Science in Data Science is an interdisciplinary, online program designed to integrate computing with statistical methodologies for analysis and interpretation of large datasets. The program focuses on essential analytical and predictive modeling tools to visualize, analyze, and interpret data efficiently from a variety of data acquisition platforms. Our program is geared toward meeting the demand for university graduates with skills needed to manage, analyze, and draw insights from large volumes of data from diverse application domains.
The program is designed for students who hold bachelor of science or bachelor of arts degree and/or professional experience in fields such as statistics, mathematical sciences, computer science, information technology, geographic information science, epidemiology, or quantitative and/or computer programming based field; however, all degree backgrounds will be considered. Students without the requisite background are strongly encouraged to take remedial coursework in elementary statistics, introductory computer programming and advanced math (calculus, linear algebra).
32 credits
Core Coursework Requirement, 8 credits
| CS 621 | Data Analytics | 3 |
| DSCI 601 | Introduction to Data Science and Data Ethics | 1 |
| DSCI 602 | Statistical Methods for Data Science (Probability, Random Variables and Inference, Design) | 4 |
| Total Credit Hours: | 8 |
Select 3 credits from Directed Electives
| MATH 624 | Introduction to Statistical Learning | 3 |
| CS 654 | Data Mining | 3 |
| Total Credit Hours: | 3 |
Capstone Research Project, 3 credits
| DSCI 689 | Capstone in Data Science | 3 |
| Total Credit Hours: | 3 |
Cluster 1--Computing and Analytics: 9 credits
| CS 617 | Introduction to Programming | 3 |
| CS 636 | Advanced Database Systems | 3 |
| DSCI 604 | Data Storage and Management | 3 |
| | or | |
| ICS 655 | Special Problems Seminar | 3 |
| DSCI 605 | Data Visualization | 3 |
| DSCI 607 | Data Analytics for Environmental Sciences | 3 |
| DSCI 608 | Data Analytics for Bioinformatics | 3 |
| DSCI 609 | Data Analytics for Social Sciences | 3 |
| DSCI 610 | Data Analytics for Health Sciences | 3 |
| ICS 664 | Cloud Technologies | 3 |
| Total Credit Hours: | 9 |
Cluster II--Statistical Modeling: 6 credits
| MATH 610 | Statistical Programming: Base SAS 9 | 3 |
| MATH 627 | Generalized Linear Models with Applications | 3 |
| MATH 628 | Computational Methods in Statistics | 3 |
| MATH 629 | Introductory Survival Analysis | 3 |
| MATH 656 | Bayesian Methods and Linear Mixed Models | 4 |
| SOC 686 | Categorical Data Analysis | 3 |
| Total Credit Hours: | 6 |
One additional elective from Cluster I or II
Total Credit Hours: 32