Master of Science in Data Science
Master of Science in Data Science:
Core Coursework Requirement: 14 credits
· Required (8 Credits): DSCI 601, DSCI 602, and CS 621
· Directed Elective (3 Credits): MATH 624 or CS 654
· Capstone Research Project (3 Credits): DSCI 689
General Electives (18 credits) from Two Different Clusters:
· Cluster I: Computing and Analytics (9 Credits)
· Cluster II: Statistical Modeling (6 Credits)
· One additional elective from Cluster I or II
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 |
Select 3 credits from Directed Electives
Capstone Research Project, 3 credits
DSCI 689 Capstone Research Methods
Candidates of Master of Science in Data Science degree will conduct a capstone research project (3 credits).
Cluster 1--Computing and Analytics: 9 credits
| 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 |
Cluster II--Statistical Modeling: 6 credits
| MATH 627 | Generalized Linear Models with Applications | 3 |
| MATH 629 | Introductory Survival Analysis | 3 |
| MATH 610 | Statistical Programming: Base SAS 9 | 3 |
| MATH 656 | Bayesian Methods and Linear Mixed Models | 4 |
| MATH 628 | Computational Methods in Statistics | 3 |
| SOC 686 | Categorical Data Analysis | 3 |
One additional elective from Cluster I or II
Total Credit Hours: 32