DATA SCIENCE IN FINANCE AND INSURANCE

This course explores machine learning models, their theoretical basis, computing implementation and applications in finance and insurance.  It discusses machine learning models for regression, classification and unsupervised learning;  tools such as cross validation and  techniques such as regularization, dimension reduction and ensemble learning;  and select algorithms for fitting machine learning models.  This course offers students an intensive hands-on experience where they combine theoretical understanding, domain knowledge and coding skills to better inform data-driven decision making.

Some topics covered are relevant to the statistical learning portion of the Society of Actuaries (SOA) and the Casualty Actuarial Society (CAS) curricula, and the quantitative methods section of the Chartered Financial Analyst (CFA) Institute curriculum.  This is a core course of the Actuarial Science program.