Specialized Applications of Predictive Analytics

Catalog description

Explores the potential of machine learning techniques in making data-driven decisions applicable across various industries and business fields. Leverages open-source software and real-world data to provide hands-on expertise in constructing models such as linear and logistic regression, discriminant analysis, naive Bayes, decision trees, and ensemble methods like random forest, bagging, and boosting. Focuses on automated feature selection, model regularization, and parameter optimization, aiming to develop essential skills for optimizing operations and delivering value in dynamic business landscapes. Enrollment Restrictions: Enrollment restricted to students in the Supply Chain Analytics, M.S. and Business Analytics, M.S. programs.