Master of Science in Data Science

Degree Requirements

10–13 courses, 30–42 credits

To earn the master’s degree, candidates must complete a total of 30 to 42 credits. Students who have fulfilled the required leveling courses can typically complete the degree with 30 credits of graduate study. Enrollment in 9 or more credits per semester is considered full-time for this program.

Data Science Leveling courses (3 courses, 12 credits)

Applicants with undergraduate coursework in Calculus, Linear Algebra, Statistics, and Programming, each completed with a grade of B or higher, are eligible for direct entry into the program. Candidates without this background but with at least two years of relevant industry experience may also be granted direct entry, pending review.

Applicants who do not meet these criteria are required to complete up to 12 credits of leveling coursework. The graduate program director evaluates each student’s academic and professional background at the time of admission to determine the appropriate number and type of leveling courses. Based on prior experience, some students may be eligible to waive one or more of these requirements.

Students complete or waive the following courses:

MATH-255	Probability and Statistics	4
MATH-270	Calculus and Linear Algebra for Data Science	4
CMPSC-F131	Computer Science I	4

MSDS Degree Requirements

7 Courses, 21 Credits.

CMPSC-701	Programming for Data Science	3
CMPSC-710	Introduction to Data Science	3
CMPSC-740	Machine Learning & AI	3
CMPSC-763	Database Systems	3
MATH-755	Advanced Calculus and Linear Algebra for Data Science Applications	3
MATH-765	Advanced Statistics	3
DS-710	Ethics in Data Science	3

MSDS Additional Degree Requirement

There is an additional Data Science Applications Requirement of 3 courses, 9 credits.

DS-701, DS-702 and DS-703.

About the Degree

Learn more about the experiences and opportunities available within this master's program.

View the Program Page

Learning Goals and Objectives

Learning Goals	Learning Objectives
Students will...	Students will be able to…
Acquire Core Data Science Methodologies	- Understand, implement, and adapt statistical, machine learning, and algorithmic techniques to analyze structured and unstructured data across domains.
Develop Proficiency in Computational Tools and Programming	- Write efficient, maintainable code and use software engineering best practices to build robust data workflows and models.
Master Mathematical Foundations for Data Analysis	- Apply linear algebra, calculus, and probabilistic reasoning to model, optimize, and interpret data-driven phenomena.
Demonstrate Domain-Specific Data Science Competence	- Translate complex domain-specific problems into data science tasks and develop tailored solutions in interdisciplinary contexts.
Practice Ethical and Responsible Data Science	- Evaluate the ethical, legal, and societal implications of data-driven decisions and incorporate principles of fairness, transparency, and accountability into data science practice.