Prescriptive Analytics From Data To Decision

AY2023/2024 Semester 2

In this era of big data, many companies and public organizations invest heavily in various flavors of analytics: descriptive, predictive, and prescriptive. Among them, prescriptive analytics goes beyond what happened (descriptive analytics), what will happen (predictive analytics), and provides insights into what to do to help companies and public organizations make better decisions. This course aims to introduce students to the theory and applications of prescriptive analytics: how to go from data to optimal decision-making. It contains two principal components (1) Data-driven optimization theories and techniques and (2) Important business applications in finance, investment, and operations management. Optimization theories such as linear optimization, discrete optimization, network optimization, quadratic optimization, stochastic optimization, and robust optimization will be covered in this course, with applications in portfolio selection, asset allocation, risk management, revenue management, pricing and hedging of options, asset/liability management, appointment scheduling, retail operations, inventory management, and assets repositioning in a sharing economy. We will also place a specific focus on analyzing real data and solving optimization models using Python with commercial solver Gurobi*. * Academic version is free.