Data Science Minor

Student Learning Outcomes

In the Data Science Minor, students will:

• master basic statistical methods.
• master intermediate statistical methods.
• conduct statistical analyses in discipline/profession-specific contexts.
• interpret and report on statistical analyses in discipline/profession-specific contexts.

Academic Requirements

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

Requirements for Data Science Minor: 18 Credits

• STAT 101 - Introduction to Data Science Credits: 3
• STAT 281 - Introduction to Statistics (COM) [SGR #5, HSDC] Credits: 3

• or STAT 381 - Introduction to Probability and Statistics (COM) Credits: 3  

• STAT 442 - Exploratory and Cloud-Based Data Analysis Credits: 3

Electives

Select nine credits from the following. Credits: 9

• DSCI /  ECON 453 - Risk Management - Personal and Business Credits: 3
• ECON 423 - Introduction to Econometrics (COM) Credits: 3
• GEOG 382 - Quantitative Research Methods in Geography Credits: 2
• GEOG 382L - Quantitative Research Methods in Geography Lab Credits: 1
• INFO 101 - Introduction to Informatics 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 445 - Nonparametric Statistics (COM) Credits: 3
• STAT 451 - Predictive Analytics I Credits: 3
• STAT 453 - Applied Bayesian Statistics Credits: 3
• STAT 460 - Time Series Analysis (COM) Credits: 3

Total Required Credits: 18