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Intro to Optimization Through the Lens of Data Science Pt. 4
Rating: 4.9 out of 5(68 ratings)
2,000 students

Intro to Optimization Through the Lens of Data Science Pt. 4

A comprehensive introduction to mathematical optimization and gurobipy tailored to data scientists and problem solvers
Created byDr. Joel Sokol
Last updated 4/2024
English
English [Auto],

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

2 sections • 7 lectures • 53m total length

  • Mobile Retail Location: Introduction2:53

    Explore how to model and solve a burrito truck placement problem to maximize profit, using Gurobi pi in a Jupyter notebook and considering distance-based demand and daily conditions.

  • Mobile Retail Location: Model and Jupyter Notebook13:09

    Develop a Gurobi optimization model for mobile burrito trucks that maximizes profit by locating trucks using x and y variables and demand, embedded in a Jupyter notebook.

  • Power Generation Example: Introduction9:02

    Explores how data science and atmospheric and economic models feed an optimization of electricity generation using Gurobi, balancing cost and health impacts under hourly demand and emissions.

  • Power Generation Example: The Model12:54

    Construct a Gurobi pi optimization model to decide hourly electricity generation by plant, enforcing demand, capacity, minimum generation, and ramp constraints, while minimizing fuel, health, operating, startup, and shutdown costs.

  • Power Generation Example: The Solution9:01

    Learn how an optimization model integrates data on demand, emissions, and health costs to guide power generation with flexible plants, using a rolling hourly solution and Gurobi's optimality gap.

  • Power Generation Example: Jupyter Notebook2:22

    Learn to build and compare a data science and optimization workflow for power generation in Georgia using a Jupyter notebook, with large-scale variables, constraints, and emissions considerations.

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 him develop this comprehensive introduction to mathematical optimization.

In Part 4, we put everything from parts 1 through 3 together: you will solve real-world examples, working from problem statements to mathematical formulations, to code, and to solutions.


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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