Udemy
UdemyIntro to Optimization Through the Lens of Data Science Pt. 2
4.7(170)2k studentsBEGINNEREnglish
Korshub's take
Overview
Developed with input from Dr. Joel Sokol and the Gurobi Optimization team, this is the second installment of a free four-part series aimed at adding optimization to a data scientist's toolbox. Part 2 goes deeper than the introduction, focusing on more complex constraints and the practical realities of working optimization models.
Who It's For
Data scientists who have some grounding in optimization basics and want to understand when and how to apply it to more complex, real-world problems.
Key Takeaways
- Working with more complex constraints in an optimization model
- Understanding model reusability and running sensitivity analysis
- Diagnosing and understanding infeasibility
- Classifying different types of optimization problems at a high level
Exercise and solution files are included in the resource section of many lectures, giving you a way to self-assess as you move through the material.
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 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.
Advertisement
