Data Science Minor

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The minor in data science is designed to impart fundamental principles and core methodology of the discipline to enrolled students. Recent advances in technology have revolutionized data acquisition and analysis across industries. The challenges and opportunities of modern data analysis have come to define data science as a discipline and requires a unique skill set. The balanced instructional approach of this program to the mathematical, statistical, and computational aspects of data science meets those requirements and provides students with the analytical and technical framework sought both in industry and graduate programs across the sciences.

Program Requirements

Minimum number of credits required for the minor: 21

Required courses: (15 credits)

Note: Some of these courses have prerequisites; see course descriptions.

MAT 281 - Introduction to Probability Credits: 3
MAT 282 - Statistical Inference Credits: 3
MAT 486 - Introduction to Big Data Analytics Credits: 3
MAT 488 - Introduction to Data Mining Credits: 3
MAT 496 - Introduction to Data Science Credits: 3
Note: MAT 380 - Probability and Statistics may replace MAT 281 - Introduction to Probability and MAT 282 - Statistical Inference .

Elective courses: (6-8 credits)

Choose one of the following mathematics and statistics courses:

MAT 295 - Linear Algebra Credits: 4
MAT 485 - Introduction to Applied Regression Credits: 3

Choose one of the following computer science and statistical programming electives:

COS 184 - Python Programming Credits: 4
COS 285 - Data Structures Credits: 4
MAT 264 - Statistical Software Packages Credits: 3