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

Intro to Optimization Through the Lens of Data Science Pt. 4, available at Free, has an average rating of 4.86, with 11 lectures, based on 20 reviews, and has 986 subscribers.

You will learn about 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 This course is ideal for individuals who are Data scientists and problem solvers curious about mathematical optimization/prescriptive analytics. It is particularly useful for Data scientists and problem solvers curious about mathematical optimization/prescriptive analytics.

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

Summary

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

Price: Free

Average Rating: 4.86

Number of Lectures: 11

Number of Published Lectures: 11

Number of Curriculum Items: 11

Number of Published Curriculum Objects: 11

Original Price: Free

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Putting It All Together: Hands-on (Including Two Real-World) Examples

Lecture 1: Mobile Retail Location: Introduction

Lecture 2: Mobile Retail Location: Model and Jupyter Notebook

Lecture 3: Power Generation Example: Introduction

Lecture 4: Power Generation Example: The Model

Lecture 5: Power Generation Example: The Solution

Lecture 6: Power Generation Example: Jupyter Notebook

Lecture 7: Food Distribution Example: Introduction

Lecture 8: Food Distribution Example: The Model

Lecture 9: Food Distribution Example: The Solution

Lecture 10: Food Distribution Example: Jupyter Notebook

Chapter 2: Wrapping Up: Whats Next in Your Optimization Learning Journey?

Lecture 1: Wrapping Up

Instructors

Dr. Joel Sokol
Professor and Academic Director at Georgia Tech

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5 stars: 18 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

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