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 to 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 2, you will dive deeper into the relationship between optimization and data science. Work with more complex constraints, understand model reusability, analyze sensitivity, and understand infeasibility. Classify types of optimization problems and see how they are solved at a high level.

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.



Optimization & Data Science

Optimization in Data Science
Customizing Data Science Models Using Optimization

More In-depth Modeling

Modeling Example Introduction
Recursive Looking Constraints
Variable Substitution and Extra Variables
How Optimization Questions are Presented
Modeling Note: Quadratic Objectives

Reusability, Infeasibility, and Analyzing Sensitivity

Reusability of Models
Infeasibility and Debugging
Using Models to Analyze Sensitivity

Classification of Models and Algorithms

Classification of Optimization Models
Solution Algorithms at a High Level