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Data and Decisions teaches you how to use data and quantitative reasoning to make sound decisions in complex and uncertain environments. The course draws on probability, statistics, and decision theory. Probabilities provide a foundation for understanding uncertainties, such as the risks faced by investors, insurers, and capacity planners. We will discuss the mechanics of probability (manipulating some probabilities to get others) and how to use probabilities to make decisions about uncertain events. Statistics allows managers to use small amounts of information to answer big questions. For example, statistics can help predict whether a new product will succeed or what revenue will be next quarter. The third topic, decision analysis, uses probability and statistics to plan actions, such as whether to test a new drug, buy an option, or explore for oil. In addition to improving your quantitative reasoning skills, this class seeks to prepare you for later classes that draw on this material, including finance, economics, marketing, and operations. At the end we will discuss how this material relates to machine learning and artificial intelligence.

This course aims to develop students' skills in analytics and quantitative modeling for managerial decision-making: 1) Constructing a model that can be used to address a relevant business question, 2) Implementing the model in a spreadsheet environment, such as Microsoft Excel, and 3) Using optimization, Monte Carlo simulation and sensitivity analysis to generate and interpret recommendations. The class is taught in a lab style, with real-time in-class exercises done in small teams, focusing on a variety of applications drawn from online advertising, healthcare, finance, pricing and revenue management. We will primarily be using Excel for modeling building and analysis, with appropriate packages for optimization (Solver) and simulation (Oracle Crystal Ball).

This course is offered for students requiring specialized training in an area not covered by existing courses. To register, a student must obtain permission from the faculty member who is willing to supervise the reading.