3.0

credits

Average Course Rating

Risk management has always been in the vanguard of data analytics because risk measurement is a critical element in calculating risk/return tradeoffs. This course will examine both qualitative and quantitative analytical methods commonly used in risk management. Qualitative tools include impact/likelihood analysis; event and fault trees; threats, vulnerability, and consequences (TVC); and failure mode and effects analysis (FMEA). However, a key lesson in risk management is that what gets measured gets managed. As a result, a major part of the course will focus on quantitative tools, including modeling and stochastic simulations. We will use the @Risk software to build realistic risk models, including one in assessing project management risks. The objective of the course is to equip students with practical tools they can apply in risk-based decision making. Prerequisites: 470.681 Probability and Statistics; working knowledge of Excel