3 credits
Fall 2026 Lecture Upper DivisionData from last updated 6/5/2026
Fall 2026
Students build and evaluate predictive models to support regression-type and classification-type business problems using popular industry tools (e.g., RStudio, SAS Enterprise Miner). Methods covered include multiple linear regression, ridge regression, lasso, CART, logistic regression, LDA/QDA, SVM, and ensemble methods such as random forests.
Learning Outcomes1Generate a valid predictive analytics solution by following a structured process.
2Demonstrate practical understanding of frequently used predictive analytics techniques.
3Demonstrate a working knowledge of RStudio and SAS Enterprise Miner to build an access predictive models.
4Demonstrate project poster and presentation of a predictive analytics solution to a business problem.
Prerequisites
Restrictions
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