The course aims at students interested in investment decisions, portfolio theory and risk management while delving into both theoretical and empirical aspects of the topics. Topics covered include: portfolio and diversification theory, equilibrium capital markets and its implications, portfolio performance measures, fixed income instruments, value-at-risk, and credit risk.

Introductory course to Data Science with applications in the R programming environment. Special focus is put on data visualization, data & text mining, and machine learning methods.