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

SynopsisIntro to Optimization Through the Lens of Data Science Pt. 2,...
Intro to Optimization Through the Lens of Data Science Pt. 2  No.1

Intro to Optimization Through the Lens of Data Science Pt. 2, available at Free, has an average rating of 4.74, with 13 lectures, based on 53 reviews, and has 1146 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. 2

Summary

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

Price: Free

Average Rating: 4.74

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 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.

    Course Curriculum

    Chapter 1: Optimization & Data Science

    Lecture 1: Optimization in Data Science

    Lecture 2: Customizing Data Science Models Using Optimization

    Chapter 2: More In-depth Modeling

    Lecture 1: Modeling Example Introduction

    Lecture 2: Recursive Looking Constraints

    Lecture 3: Nonnegativity

    Lecture 4: Variable Substitution and Extra Variables

    Lecture 5: How Optimization Questions are Presented

    Lecture 6: Modeling Note: Quadratic Objectives

    Chapter 3: Reusability, Infeasibility, and Analyzing Sensitivity

    Lecture 1: Reusability of Models

    Lecture 2: Infeasibility and Debugging

    Lecture 3: Using Models to Analyze Sensitivity

    Chapter 4: Classification of Models and Algorithms

    Lecture 1: Classification of Optimization Models

    Lecture 2: Solution Algorithms at a High Level

    Instructors

  • Intro to Optimization Through the Lens of Data Science Pt. 2  No.2
    Dr. Joel Sokol
    Professor and Academic Director at Georgia Tech
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  • 2 stars: 0 votes
  • 3 stars: 2 votes
  • 4 stars: 11 votes
  • 5 stars: 40 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.

    Can I take my courses with me wherever I go?

    Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!