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This is the second in a series of four required courses in the Master of Risk Management program. It can be broadly considered to belong to the area of risk analytics, albeit with an emphasis on its application to the risk management process in different applications. The course reviews key concepts of statistics and looks at a number of data sources used to analyse risks in various disciplines. Applications such as forecasting, modelling extreme events and dependencies are illustrated through their implementation in a number of practical problems. These are aimed at making students aware of the power of statistics in quantitative risk analysis, and its areas of applicability. Traditional risk measures and models routinely used to analyse financial, insurance, environmental, health and safety, engineering reliability and security risks data are reviewed. Emphasis is placed on the use of these tools in practical applications, which is achieved through the presentation of real-life examples.Simulation is discussed as a tool to analyse risks in complex systems, where data are not available or are impossible to obtain. It is shown how achievements in the field of computer science can be used to develop intricate software on which simulations of complex systems can be used to study the possible effects on the system when various stressors (external or internal) are artificially applied to the system. The example of Physiologically-based Pharmacokinetic Model used to predict the effects of drug interaction in humans will be used to illustrate the approach and to highlight the power of complex system simulation in the field of risk analysis.