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.

Bridge Courses (Up to 7 credits)

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

Core Courses (21 credits)

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 5622 Machine Learning (3 credit)
Two of the following:
- STAT 5600 Intro to 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)