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

Name: Intro to Optimization Through the Lens of Data Science Pt. 1
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Korshub's take

What you'll learn

This is part one of a free 4-part series introducing optimization to data scientists, developed with Dr. Joel Sokol and the PhD team at Gurobi Optimization. Rather than treating optimization as a separate discipline, the course frames it as another tool for your existing toolbox, focused on recognizing when optimization is the right approach and how to translate a real-world problem into an optimization model.

Part 1 specifically covers the building blocks of mathematical optimization and the key concepts needed to build your first models, illustrated with a range of real use cases and supplemental material on best practices.

Who it's for

Data scientists and analysts who want to add optimization to their problem-solving skill set.

Key takeaways

When optimization is the right tool versus other approaches
How to translate real-life problems into optimization models
The core building blocks of mathematical optimization

About this course

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.