Data Science Minor

Location(s): Brookings Main Campus

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Student standing in front of monitors showing data.

Program Coordinator/Contact

Eun Heui Kim, Department Head
Department of Mathematics and Statistics
Chicoine Architecture, Mathematics and Engineering Hall 209
605-688-6196

Program Information

The Data Science minor provides training in the discipline-specific and profession-specific application of data science concepts and methods to students enrolled in a wide variety of programs and majors. Data analysis methods have become increasingly important in many disciplines such as the life sciences, physical sciences, social sciences, engineering, and business. As more and more data are collected, stored and analyzed, students are finding it increasingly beneficial to gain expertise in analytical methods to bolster their research skills and enhance their career opportunities.

Academic Requirements

A grade of C or better is required for all courses in the minor.

Requirements for Data Science Minor: 18 Credits

Electives

Select nine credits from the following. Credits: 9

CSC 151 - Python Programming for Scientific Computing Credits: 3
CSC 220 - Data Structure and Visualization Credits: 3
DSCI / ECON 453 - Risk Management - Personal and Business Credits: 3
ECON 423 - Introduction to Econometrics (COM) Credits: 3
INFO 101 - Introduction to Informatics [HSDC] Credits: 3
MATH 270 - Computational Linear Algebra Credits: 3 *
MATH 374 - Scientific Computation I Credits: 3
MATH 474 - Scientific Machine Learning Credits: 3
POLS 388 - Research Methods Credits: 3
PSYC 477 - Psychology Testing and Measurement (COM) Credits: 3
SOC 307 - Research Methods I (COM) Credits: 3
SOC 308 - Research Methods II (COM) Credits: 3
STAT 383 - Geospatial Data Analysis Credits: 3
STAT 410 - SAS Programming Credits: 3 *
STAT 414 - Basic R Programming Credits: 1
STAT 415 - R Programming Credits: 3 *
STAT 435 - Applied Bioinformatics Credits: 3
STAT 441 - Statistical Methods II Credits: 3
STAT 442 - Exploratory and Cloud-Based Data Analysis Credits: 3 *
STAT 445 - Nonparametric Statistics (COM) Credits: 3
STAT 451 - Predictive Analytics I Credits: 3
STAT 453 - Applied Bayesian Statistics Credits: 3
STAT 454 - Statistical Machine Learning and AI Credits: 3
STAT 460 - Time Series Analysis (COM) Credits: 3

Total Required Credits: 18

Notes

* The course may only be used to meet requirements in one section.