Data Science Minor

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The minor in data science provides students with an introduction to data science. Students learn to extract meaning from data leveraging statistics, computer science, machine learning, and data engineering skills. That meaning is used to predict new outcomes, and to identify patterns that may not previously have been identified within the data. No previous knowledge of computing or statistics is required. The minor consists of three foundational courses and then three electives that range from software engineering to operations research, among others.

Minor Requirements

18 credits:

DATA 200 Introduction to Data Science (3)
DATA 210 Exploratory Data Analysis (3)
DATA 220 Machine Learning Models (3)

Choose three courses from the following list of electives:

CMSCI 120 - Introduction to Computer Science I Credits: (3)
CMSCI 125 - Introduction to Computer Science II Credits: (3)
CMSCI 349 - Software Engineering Credits: (3)
CMSCI 449 - Introduction to Artificial Intelligence Credits: (3)
CMSCI 359 - Database Management Systems Credits: (3)
DATA 201 - ST: Special Topics in Data Science Credits: (1)
MATH 364 - Linear Algebra Credits: (3)
MATH 387 - Probability Credits: (3)
MATH 388 - Operations Research Credits: (3)
MATH 489 - Modeling and Simulation Credits: (3)
CMSCI 489 - Modeling and Simulation Credits: (3)
Alternately, students may choose two courses from the elective list and a statistical methods course from the following list:
BIOL 205 Biostatistics (3)
ECON 403 Econometrics (3)
BUS 344 Operations and Supply Chain Management (3)
PSCI 355 Political Research Methods and Analysis (4)
PSYCH 200 Research Methods and Statistics (3)
SOC 404 Statistics (4)

Note

Students minoring in Data Science and are majoring in a related major are allowed to double count three of the six courses. Students must take three courses that are not also counted for their major.