MS in Data Science Curriculum

The Curriculum

The master’s in data science equips students with advanced technical training in machine learning, visualization, AI, and database management, as well as a deep understanding of algorithms and data structures.

Paired with courses in advanced calculus, linear algebra, and statistics, you’ll develop strong analytical skills crucial for modeling complex problems, understanding data patterns, and making informed decisions based on quantitative analysis. You’ll then work with departments across the College of Arts & Sciences to apply data science techniques to specialty fields such as: bioinformatics, psychology, econometrics, communications, or business intelligence.

Students with undergraduate coursework in Calculus, Linear Algebra, Statistics, and Programming with grades of B or better will be given direct entry into the program. Students without this coursework but with at least 2 years of industry experience will also be eligible for direct entry on a case-by-case basis.

For this program, 9 credits (three, 3-credit courses) will count as full time. Students earn 30 credits to complete this degree.

Requirements

Core Requirements 21 Credits (7 Courses)
Electives 9 Credits (3 Courses)
Total 30 Credits (10 Courses)

The course sequence for the program is as follows:
All courses are 3 credit courses

Fall Spring Summer 1 Summer 2
Programming for Data Science Databases for Data Science Ethics in Data Science Applications 1
Advanced Calculus and Linear Algebra for Data Science Applications Advanced Statistics Applications 2 Applications 3
Intro to Data Science Machine Learning & AI    


Data Science Leveling Courses (3 courses, 12 credits maximum)

Candidates who have not completed an undergraduate program of study in Mathematics, Computer Science, or Statistics or have not successfully completed (with a grade of B or better) academic courses in introductory Computer Programming in Python, Calculus, Linear Algebra, Probability and Statistics, are required to complete leveling courses. The graduate program director evaluates the unique background of each student at the time of acceptance into the graduate program to determine the number and type of leveling courses that are required. Some students may be required to complete up to 12 credits of leveling courses; others will be able to waive some number of these leveling courses based on prior undergraduate experience. An additional 30 credits of graduate-level coursework is then required to earn the MSDS degree.

Spring Leveling
Introductory Math for Data Science
Probability and Statistics
Intro to Python Programming

Q&A with Program Director

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