Data, Models and Decisions in Business Analytics

Description

In this course, you will learn:

• To construct systematic frameworks for decision-making in a dynamic situation, fundamental principles from probability, statistics, stochastic modelling, and optimization are used.
• How to learn the underlying model and pattern using historical data.
• In business applications, optimization methods and software are used to address decision issues under uncertainty.

Syllabus:

1. Introduction to Probability: Random variables; Normal, Binomial, Exponential distributions; applications
2. Estimation: sampling; confidence intervals; hypothesis testing
3. Regression: linear regression; dummy variables; applications
4. Linear Optimization; Non-linear optimization; Discrete Optimization; applications
5. Dynamic Optimization; decision trees