Statistics is about using data to estimate certain values and evaluate certain hypotheses; this makes perfect sense for passively studying how the world works (i.e., the scientific method). More often than not, however, we find ourselves wanting to use this statistical information to make decisions regarding the systems involved. Suppose we estimate that the demand for jet fuel next month will be greater than normal. How does this information affect the decision of an oil refinery to purchase crude oil from their various sources? How does an airline company decide how many flight crews to employ based on the current flight schedule? How does past sales information across the U.S. influence a company's decision over where to place its warehouses?
The quantification and mathematical solution of these types of decision-making problems are known broadly as optimization. The general features of an optimization problemare a set of quantifiable decisions that have a quantifiable effect that should be minimized or maximized (think cost or revenue) and a set of constraints on the possible values of those decisions. There are many different optimization branches, but the most prominent, due to its widespread applicability and computational efficiency, is linear programming, where the objective function and constraints are all linear.
In this course, you will explore the mathematics of linear programs, how to solve them, and how to evaluate your model. You will implement these techniques using packages in the free and open-source statistical programming language R to solve real-world logistical business problems. The focus will be on making these methods accessible for you in your own work.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Understanding Data Analytics
- Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis
- Finding Patterns inData Using Cluster and Hotspot Analysis
- Regression Analysis and Discrete Choice Models
- Supervised Learning Techniques
- Neural Networks and Machine Learning
