The implementation of risk management models is a central preoccupation of financial institutions, in particular because of the recent regulatory environment (Basel II and III) and the increase awareness of their clients about financial risks. The course emphasizes risk-modelling tools used in the industry to monitor risks both at the bank level and at the client level in wealth management. Using an intuitive teaching approach with interactive training sessions based on case studies solved through a web platform allowing for online data analysis, we explain key data-analytic tools for risk management in a non technical way and we demonstrate their application in selected real-data problems. The concepts studied in the course cover loss distributions, risk measures, and risk aggregation. We discuss essential tools like Value-at-Risk and Expected Shortfall, and how to estimate and use them. A crucial theme is the modelling and measuring of the dependence among risks. We investigate in particular applications to risk forecasting, portfolio selection (robot-advisor), and hedging. Techniques draw from extreme value theory and GARCH models among others as well as nonparametric techniques. We further discuss use of risk measures in the design of new asset management methods known as risk parity.