Intro to Optimization Through the Lens of Data Science Pt. 1

Name: Intro to Optimization Through the Lens of Data Science Pt. 1
Rating: 4.6 (758 reviews)

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

Created byDr. Joel Sokol

Last updated 4/2024

English

What you'll 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

Course content

4 sections • 14 lectures • 1h 57m total length

Course Introduction5:43
Introduction to Optimization6:11
Try it! The Burrito Optimization Game1:36
The Burrito Optimization Game is a web-based app that is intended to act as an entry point for data scientists and problem solvers who could benefit from optimization; the game teaches players why optimization is valuable and important, why it’s difficult (by showcasing the scaling and added complexity of optimization throughout round play), and why solvers and other optimization algorithms are essential in finding an optimal solution.
Please check the resources to visit the game's website and the game guide associated with this lecture.

Learning gurobipy6:21
In lecture 7, the Gurobi team introduces the basics of gurobipy. Below, in the resources section, you can find links to members of the Gurobi technical team guiding learners through the basics of gurobipy using the class diet problem example. These lessons are posted to the Gurobi YouTube channel and will be updated periodically as new versions of Gurobi are released.

In this video series, you will learn to import and export data files, be guided through a Jupyter Notebook example of the gurobipy code where you will learn the importance of separating the data from the model, see how to build a full model for the diet problem using gurobipy, and learn to extract solutions to different file types.

There are links to external YouTube lessons and also downloadable gurobipy (.py) code below.

Optimization Modeling Basics12:41
Basic Modeling Examples12:49
Gurobipy Expressions8:30
Notation, Subscripts, Summations6:14
Please check the associated resources to download the exercises (both questions and solutions) to test your skills and reinforce the lessons learned in this lecture.
Multiple Subscripts11:07
Please check the associated resources to download the exercises (both questions and solutions) to test your skills and reinforce the lessons learned in this lecture.
Sets8:47
Please check the associated resources to download the exercises (both questions and solutions) to test your skills and reinforce the lessons learned in this lecture.
Building a Model9:26
Please check the associated resources to download the exercises (both questions and solutions), datasets, and gurobipy code to test your skills and reinforce the lessons learned in this lecture.

Requirements

Basic Python, college-level mathematics, experience with Jupyter Notebooks, and the ability to open python (.py) and Jupyter Notebooks (.ipynb) files on your machine

Description

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.

In part 1, you will see optimization in action using new educational tools and resources and be exposed to a wide variety of successful use cases. Learn the building blocks of mathematical optimization and get comfortable with the key concepts required to create your first optimization models with supplemental material for establishing best practices going forward.

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.

Who this course is for:

Data scientists and problem solvers curious about mathematical optimization/prescriptive analytics.

Intro to Optimization Through the Lens of Data Science Pt. 1

What you'll learn

Explore related topics

Course content

Course Introduction3 lectures • 14min

Optimization Success Stories3 lectures • 17min

More on gurobipy with the Gurobi Experts1 lecture • 6min

Optimization Modeling7 lectures • 1hr 10min

Requirements

Description

Who this course is for: