Master of Science in Data Science
Academic Requirements: Students who apply to our M.S. program should have 3 semesters of calculus, a semester of linear algebra, and a semester of computer programming.
We state the last day to apply for international students is April 15, however we can still take applications until June 15, 2022 for the fall session.
The Master of Science program in data science requires 30 s.h. of graduate credit. It aims to train the next generation of data scientists with the analytical and technical skills to explore, formulate and solve complex data-driven problems in science, industry, business and government. The program focuses on the theory, methodology, application and ethics for working with and learning from data. Students will acquire the abilities to develop and implement new or special purpose analysis and visualization tools, and a fundamental understanding of how to quantify uncertainty in data-driven decision-making.
The coursework includes six core courses covering the fundamentals of data science including probability and statistics; data storage, access, and management; and data visualization, exploration, modeling, analysis and uncertainty quantification. Students will acquire hands-on experience in solving real-world problems, communication skills and data ethics via a required capstone project. Students choose three electives (9 s.h.) from a wide variety of courses on specialized data science topics offered by Statistics, Biostatistics, Computer Science and Business Analytics to enhance their skill set, based on their interests and career goals.
The M.S. with a major in data science requires the following coursework.
All of these:
|DATA:4750||Probabilistic Statistical Learning||3|
|DATA:5890||M.S. Data Science Practicum||2|
|STAT:3120||Probability and Statistics||4|
|STAT:3200||Applied Linear Regression||3|
|STAT:4580||Data Visualization and Data Technologies||3|
|STAT:5400||Computing in Statistics||3|
At least 9 s.h. from these:
|STAT:3210||Experimental Design and Analysis||3|
|STAT:4560||Statistics for Risk Modeling I||3|
|STAT:5810||Research Data Management||3|
|STAT:6530||Environmental and Spatial Statistics||3|
|STAT:6550||Introductory Longitudinal Data Analysis||3|
|STAT:6560||Applied Time Series Analysis||3|
|BIOS:6720||Machine Learning for Biomedical Data||3|
|CS:4310||Design and Implementation of Algorithms||3|
|CS:4470||Health Data Analytics||3|
|CS:5110||Introduction to Informatics||3|
|CS:5630||Cloud Computing Technology||3|
|BAIS:6210||Data Leadership and Management||3|