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Intro to Optimization Through the Lens of Data Science Pt. 2
Rating: 4.8 out of 5(168 ratings)
2,337 students

Intro to Optimization Through the Lens of Data Science Pt. 2

A comprehensive introduction to mathematical optimization and gurobipy 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

Explore related topics

Course content

4 sections • 13 lectures • 1h 40m total length

  • Optimization in Data Science8:15

    Discover how optimization underpins data science by finding best parameter values for regression, classification, clustering, and time series models.

  • Customizing Data Science Models Using Optimization8:26

    Explore how optimization customizes data science models, adjusting regression loss, adding constraints like lasso, ridge, and elasticnet, and directly selecting a best subset of features with binary variables.

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 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.

Who this course is for:

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

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