Predictive Modeling and Applications

Catalog description

This course covers a broad range of predictive models in the areas of parametric and non-parametric statistical methods. Examples include time series models and data mining. It focuses on building theoretical foundations underlying these methods and their applications to empirical data such as forecasting time series and classifications. Students are expected to acquire advanced predictive modeling skills and be comfortable with using statistics software R after taking this course. This is a required course for Actuarial Science students in preparation for the Society of Actuaries' “Statistics for Risk Modeling" exam, and an elective course for Business Data Analytics major and minor students who are interested in deepening their skills in predictive analytics.