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.

Application is found here

Updated 4/27/2022

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:4540 Statistical Learning 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:4520   Bayesian Statistics   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:4400 Database Systems    3
CS:4440 Web Mining 3
CS:4470 Health Data Analytics  3
CS:5110 Introduction to Informatics 3
CS:5430 Machine Learning 3
CS:5630 Cloud Computing Technology 3
BAIS:6100 Text Analytics 3
BAIS:6130 Applied Optimization 3
BAIS:6210 Data Leadership and Management  3
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