Course Description
This course introduces students to the rapidly growing field of data science and its applications in predictive modeling within Agriculture, Food, and Life Sciences. Students will learn techniques to access, explore, validate, prepare, manipulate, analyze, and report on data. They will be able to create analysis flow diagrams for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision trees, ensembles of trees like forests and gradient boosting, regression, logistic regression, neural networks, and support vector machines) in an interactive and exploratory manner.
Spring 2026
Instructors
Meeting Patterns
Classes Start:
January 12, 2026
Classes End:
April 28, 2026
Distance Education:
Yes
Class Days:
[TBA]
Class Type:
Lecture
Credits:
3.00
Restrictions:
Prerequisite: ST 525 or ST 512