Curriculum
The on-campus data science master's degree seeks to shape tomorrow’s leaders by providing learners with the skills, competencies, and knowledge necessary to fuel creative problem-solving, adaptability, and the capability to communicate effectively across diverse organizations.
Courses
The MS-DS is a non-thesis degree that requires 30 credit hours of coursework. You must complete 21 credits of core coursework in statistics, computer science, and general core concepts as well as 9 credits of elective coursework.
Students in the Bridge to Data Science Pathway may be required to complete one or more of the following courses (up to 7 credits). Courses should be taken in the first year and are subject to Graduate School grade and cumulative GPA standards. Up to 3 credit hours of bridge courses which meet applicable standards can count toward the degree in the electives category.
- DTSC 5003 Programming for Data Science - Python for Data Science
- INFO 5651 Fundamental Concepts in Data Science
- INFO 5652 Statistical Programming in R
- DTSC 5301 Data Science as a Field (1 credit)
- DTSC 5302 Ethical Issues in Data Science (1 credit)
- DTSC 5303 Cybersecurity for Data Science (1 credit)
- STAT 5000 Statistical Methods and Applications 1 (3 credit)
- STAT 5010 Statistical Methods and Applications 2 (3 credit)
- CSCI 5502 Data Mining (3 credit)
- CSCI 5612 Machine Learning for Data Science (3 credit)
- Two of the following:
- STAT 5600 Methods in Statistical Learning (3 credit)
- DTSC 5501 Data Structures and Algorithms (3 credit)
- *CSCI 5253 Datacenter Scale Computing (3 credit)
- *ATLS 5214 Big Data Architecture (3 credit)
- INFO 5602 Information Visualization (3 credit)
- CSCI 5454 Design and Analysis of Algorithms (3 credit)
Note: Only one course between ATLS 5214 and CSCI 5253 will count towards core requirements.
Choose from available courses in computer science, information science, geography, business, and more.
Click the Topic Area cards below to learn more about individual courses.
