DATA SCIENCE IN FINANCE AND INSURANCE

The Data Science in Finance and Insurance course explores machine learning models, their theoretical basis, computing implementation and applications in finance and insurance.  Topics include 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.  Prior exposure to linear algebra, calculus and statistics is helpful.  A working knowledge of a spreadsheet program and R is a plus.  Students will use spreadsheets and R for validation and prototyping and Python to implement algorithms and apply models to applicable data. 

Some topics covered are also 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.