A comprehensive introduction to mathematical optimization and gurobipy tailored to data scientists and problem solvers

What you will learn

What is optimization and how can it be applied to complex problems?

How to identify an optimization problem and translate real life into optimization models

Learn about solvers and algorithms

Introduction to Gurobi/gurobipy and using it in exercises and real-world problem solving


Welcome toΒ Introduction to Optimization Through the Lens of Data Science!

This free 4-part course was developed to help teach data scientists how to add optimization to their toolbox and when to use it in their advanced problem-solving. We will cover a comprehensive introduction to optimization, when optimization is the best tool to solve a problem, and how to translate real-life problems into optimization.

We will introduce you to world-class tools to help you problem solve, and provide everything from basic hands-on exercises to more advanced full real-world use cases to reinforce all new concepts of prescriptive analytics as you learn them. We look forward to having you learn optimization (and gurobipy) with expertise from Dr. Joel Sokol and the team of Ph.D. experts from Gurobi Optimization, who helped develop this comprehensive introduction to mathematical optimization.

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In part 3, you will model yes/no decisions and complex logical constraints with binary variables and link them to continuous variables. You will also explore classic optimization model archetypes.

Hands-on Exercises:

Please check the resource section of many of the lectures to find self-assessments in the form of exercise files and solution files. You will also notice we have data and code files available to help you work your way through these practice exercises.



Adding Complexity: Binary Variables, Constraints, and Troubleshooting

Fixed Costs, Linking to Continuous Variables
Yes/No Decisions and Related Constraints
Using Truth Tables to Troubleshoot Constraints
Modeling Logic with Binary Variables
Building Complex Logical Constraints
Example: Rolling-Horizon Power Generation

The Archetypes: Learning Classic Optimization Models

Knapsack and Covering Models
Blending Model
Cutting Stock and Bin Packing Models
Network Model
Shortest Path Model
Assignment Model
Modeling Note: Dealing with Roundoff Error