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.

• 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