Master of Science in Data Science
The Master of Science in Data Science (MSDS) is an interdisciplinary, online program designed to integrate computing with statistical methodologies to analyze and interpret moderate to 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 and to make data-driven decisions. The graduates from our program can apply these skills at their workplace right away. All courses in the MSDS program are delivered asynchronously online.
Curriculum Overview
The curriculum for the MSDS program is divided into four modules. There are five courses in the core module through which students gain a solid background of the field of data science, statistical methods, data visualization, programming, and data mining. The computational analytics module dives deep into the computational skills including data analytics, database management, and cloud commutating. The courses in the statistical computing and modeling module cover methods for categorical data analysis, statistical learning and predictive modeling, and foundations of statistical computing. Finally, courses in the applied research module provide an opportunity for the students to select two courses and gain in-depth hands-on research experience with cutting-edge data analytics methods in an application domain of their interests.
33 credits
The MSDS program consists of a total of 33 credit hours. You will complete 11 courses from four modules – 1) Core or foundational, 2) Computational Analytics, 3) Statistical Computing and Modeling, and 4) Applied Research.
Core Coursework (15 Credit Hours)
The courses DSCI 601, DSCI 605, and CS 617 build the foundations for you to take two other advanced core courses DSCI 602 and CS 654 in the program. DSCI 602 is a prerequisite for most courses in the statistical computing and modeling and the applied research modules.
| DSCI 601 | Introduction to Data Science | 3 |
| DSCI 602 | Statistical Methods for Data Science | 3 |
| DSCI 605 | Data Visualization | 3 |
| CS 617 | Introduction to Programming | 3 |
| CS 654 | Machine Learning and Data Mining | 3 |
| DSCI 692 | Data Science Exit Survey | 0 |
Computational Analytics (6 Credit Hours)
Students can enhance their computational and database management skills by taking courses from the computational analytics module. At least six credit hours are required from this module.
| CS 621 | Data Analytics | 3 |
| CS 636 | Modern Database Systems with Applications | 3 |
| DSCI 604 | Data Storage and Management | 3 |
| ICS 625 | Artificial Intelligence and Machine Learning | 3 |
| ICS 664 | Cloud Technologies | 3 |
| DSCI 669 | Special Studies in Data Science | 1-6 |
Statistical Computing and Modeling (6 Credit Hours)
Courses in the statistical computing and modeling module are designed to give students advanced statistical methodologies applicable to the field of data science. Students can enhance their statistical reasoning and computing skills by taking courses from this module. At least six credit hours are required from the statistical computing and modeling module.
| DSCI 612 | Programming with SAS Base for Data Science | 3 |
| or | |
| MATH 610 | Statistical Programming: Base SAS 9 | 3 |
| MATH 624 | Introduction to Statistical Learning | 3 |
| MATH 627 | Generalized Linear Models with Applications | 3 |
| MATH 628 | Computational Methods in Statistics | 3 |
| DSCI 686 | Categorical Data Analysis | 3 |
Applied Research (6 Credit Hours)
Courses in the applied research module are designed to give students hands on content knowledge of data analytics methods and research experience in a specific application domain. Students will develop a research project and learn how to disseminate findings from their projects. At least six credit hours are required from this module.
| 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 |
| ECON 624 | Econometric Methods and Applications | 3 |
| DSCI 679 | Research Topics in Data Science | 3-6 |
| ACC 610 | Financial Analytics | 3 |
Total Credit Hours: 33