Critical Thinking Processes and Modeling for Effective Decision Making

3 units

This course equips you with rigorous technical skills that are necessary for developing data-driven and value-based solutions for large-scale complex business problems. In particular, the course covers two fundamental and indispensable tools that form the backbone of all analytical approaches: 1. Mathematical Optimization is the backbone of virtually all applied models with vast applications in engineering, business, statistics (including machine learning), computer science (including artificial intelligence), biology, medicine, etc. The critical step in modeling a practical problem is to express it in mathematical language. This enables us to use the computational power of computers to analyze the problem, extract insights, and devise implementable solutions. The course teaches this critical skill by carrying out this step for a large set of examples. The examples are carefully chosen to be both realistic and novel in at least one aspect. They are taken from various fields including operations management, marketing, finance, and machine learning. 2. Statistical Simulation is a primary computational tool for modeling complex systems that exhibit random behavior (such as service time in a service system) or interact with random events (such as demand in a retail setting or price movement in a stock market). This course covers the basics of Monte Carlo simulation through examples and showcases its application in estimating relevant performance metrics and predicting a system's response to exogenous or endogenous changes. If time permits, select topics related to simulation-based optimization will also be discussed. In addition to modeling and problem formulation, the course also covers problem implementation. The primary implementation tool in this class is Microsoft Excel. However, the topics covered in this class are independent of the implementation platform.