Predictive Analytics

Catalog description

In this course, students will apply the concepts previously learned about statistics, algorithms, and models to interact with data for the purpose of predictive analytics. Predictive analytics has the capability to help organizations identify potential impacts to their business and to support business decisions. Concepts that will be covered include: bias/variance trade-off, over-fitting and model tuning, regression models – linear, nonlinear (SVMs, K-nearest neighbors), regression trees, classification models – logistic regression, random forest, dealing with unbalanced data, feature selection, and predictor importance.