Data, Models and Decisions in Business Analytics

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

• Undergraduate probability, statistics and linear algebra. Students should have working knowledge of Python and familiarity with basic programming concepts in some procedural programming language.