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
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.
Introduction to Probability: Random variables; Normal, Binomial, Exponential distributions; applications
Estimation: sampling; confidence intervals; hypothesis testing
Regression: linear regression; dummy variables; applications
Linear Optimization; Non-linear optimization; Discrete Optimization; applications
Dynamic Optimization; decision trees
Undergraduate probability, statistics and linear algebra. Students should have working knowledge of Python and familiarity with basic programming concepts in some procedural programming language.