Emphasizes techniques of predictive analytics and introductory applications to actuarial science, finance, and economics. This course will give 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. We will cover case studies in different fields in finance and insurance. For instance, auto/home insurance claims, stock and option price prediction, mortality trends, health data analytics, etc.
- The intuitive examples and theoretical (optional for undergraduate students) components in this course will help actuarial science students develop the programming skills for the Exam PA: Predictive Analytics https://www.soa.org/education/exam-req/edu-exam-padetail/ offered by the Society of Actuaries (SOA).
- Build finance and insurance domain knowledge. Students receiving a grade of A+ will be enthusiastically recommended to the Illinois Risk Lab.
- Students will be given 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, and to communicate the results in a scientific report.
- Students will have a comprehensive understanding of supervised (regression and classification) and unsupervised (clustering) learning problems.
- Students will be able to implement these methods in practice using a statistical computing environment.