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.
What you'll learn
- 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
- 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