Mathematics Minor (15 credits)

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The Interdisciplinary Data Science minor is designed to serve the broader university community by providing foundational data science skills that are applicable across diverse fields. With core courses like Introduction to Data Engineering (MATH 1050), foundational statistics (MATH 1600), and Applied Machine Learning (MATH 2830), students from various disciplines gain hands-on experience with data-driven decision-making tools, equipping them to leverage data science within their respective domains.

Minor in Mathematics: Data Science

The requirements for minors must be completed prior to graduation in order for the minor to appear on the transcript. For more information about minors, see the department advisor.

Required Courses (9 credit hours)

MATH 1050 - Introduction to Data Engineering Credit Hours: 3
MATH 1600 - Basic Prob/Statis I Credit Hours: 3
or a foundational statistics course from any discipline
CPTR 2320 - Introduction to Applied Machine Learning Credit Hours: 3

Elective Courses (6 credit hours)

Choose two junior or senior-level courses from the following options:

Advanced courses in statistics or databases from any discipline
Courses in Bioinformatics or Health Informatics
Other courses approved by the Mathematics advisor with significant data science applications.