Intro to Optimization Through the Lens of Data Science Pt. 3

Intro to Optimization Through the Lens of Data Science Pt. 3, available at Free, has an average rating of 4.82, with 13 lectures, based on 35 reviews, and has 1022 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. 3

Summary

Title: Intro to Optimization Through the Lens of Data Science Pt. 3

Price: Free

Average Rating: 4.82

Number of Lectures: 13

Number of Published Lectures: 13

Number of Curriculum Items: 13

Number of Published Curriculum Objects: 13

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 develop this comprehensive introduction to mathematical optimization.

In part 3, you will model yes/no decisions and complex logical constraints with binary variables and link them to continuous variables. You will also explore classic optimization model archetypes.

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: Adding Complexity: Binary Variables, Constraints, and Troubleshooting

Lecture 1: Fixed Costs, Linking to Continuous Variables

Lecture 2: Yes/No Decisions and Related Constraints

Lecture 3: Using Truth Tables to Troubleshoot Constraints

Lecture 4: Modeling Logic with Binary Variables

Lecture 5: Building Complex Logical Constraints

Lecture 6: Example: Rolling-Horizon Power Generation

Chapter 2: The Archetypes: Learning Classic Optimization Models

Lecture 1: Knapsack and Covering Models

Lecture 2: Blending Model

Lecture 3: Cutting Stock and Bin Packing Models

Lecture 4: Network Model

Lecture 5: Shortest Path Model

Lecture 6: Assignment Model

Lecture 7: Modeling Note: Dealing with Roundoff Error

Instructors

Dr. Joel Sokol
Professor and Academic Director at Georgia Tech

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4 stars: 6 votes

5 stars: 29 votes

Frequently Asked Questions

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

Can I take my courses with me wherever I go?

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