ASRM 455

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Credit: 3 OR 4 hours.

Emphasizes techniques of predictive analytics and introductory applications to actuarial science, finance, and economics. Gives an overview of the different statistical learning methods and algorithms that can be employed to discover useful information from datasets, to explain how to build a predictive model using computational software packages (R and Python), and to effectively communicate the results in a scientific report. Topics include identifying the business problem, data preparation, data visualization, model building processes (generalized linear models, decision trees, cluster and principal component analyses, etc.), model selection, refinement, and validation.

3 or 4 undergraduate hours. 3 or 4 graduate hours. Prerequisite: ASRM 401 or STAT 200 or STAT 361.

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