In today’s world, managerial decisions are increasingly based on data-driven models and analysis using statistical and optimization methods that have dramatically changed the way businesses operate in most domains including service operations, marketing, transportation, and finance.
The main objectives of this course are the following:
- Introduce fundamental techniques towards a principled approach for data-driven decision-making.
- Quantitative modeling of dynamic nature of decision problems using historical data, and
- Learn various approaches for decision-making in the face of uncertainty
Topics covered include probability, statistics, regression, stochastic modeling, and linear, nonlinear and discrete optimization.
Most of the topics will be presented in the context of practical business applications to illustrate its usefulness in practice.
- Fundamental concepts from probability, statistics, stochastic modeling, and optimization to develop systematic frameworks for decision-making in a dynamic setting
- How to use historical data to learn the underlying model and pattern
- Optimization methods and software to solve decision problems under uncertainty in business applications
Vineet Goyal is an Associate Professor in the Industrial Engineering and Operations Research department at Columbia. He received his PhD in Algorithms, Combinatorics and Optimization (ACO) in 2008 from Tepper School of Business, CMU. Before joining Columbia, he spent a couple of years as a postdoctoral associate at the Operations Research Center, MIT working with Dimitris Bertsimas.
Dr Goyal’s research interests include dynamic optimization and decision making under uncertainty. He is also further interested in the design of tractable approaches for dynamic optimization problems under uncertainty and their applications in electricity markets and revenue management problems.