Optimization for Decision Making

Go to Course: https://www.coursera.org/learn/optimization-for-decision-making

Introduction

### Course Review: Optimization for Decision Making on Coursera In an increasingly data-driven world, the ability to make informed decisions based on analytical insights is crucial for businesses across various sectors. Coursera's "Optimization for Decision Making" is a comprehensive course that dives deep into the realm of prescriptive analytics, equipping learners with essential tools to determine the best courses of action based on available data. #### Course Overview The course is designed for individuals eager to understand how optimization techniques can be employed to address complex decision-making issues. Emphasizing practical applications, it covers critical aspects of linear programming—one of the most widely used optimization techniques. Whether you’re a business analyst, a decision-maker in a manufacturing firm, or simply someone interested in refining your analytical skills, this course offers valuable insights. #### Syllabus Breakdown 1. **Module 1: Introduction to Linear Programming** - This foundational module sets the stage by introducing the principles of prescriptive analytics. You'll learn to identify scenarios where optimization is applicable, which is vital for businesses that need to prescribe solutions to their decision-making problems. The focus on linear optimization prepares students for more complex modeling in future modules. 2. **Module 2: Solving Linear Programs** - Here, the course dives into practical problem-solving techniques. You will engage in graphical methods for basic linear optimization problems, which is critical for understanding the mechanics behind these solutions. The introduction of tools like Excel Solver in subsequent modules makes this course especially relevant, as Excel is widely used in the business world. 3. **Module 3: Alternative Specifications & Special Cases in Linear Optimization** - This module encourages a deeper understanding of the implications of changing model parameters. By examining special cases within linear optimization, learners can appreciate the adaptability required when working with real-world data, helping enhance their critical thinking and problem-solving skills. 4. **Module 4: Modeling & Solving Linear Problems in Excel** - The culmination of the course focuses on practical application, as students learn to model and solve significant decision problems using Excel Solver. Not only does this reinforce previous lessons, but it also provides students with hands-on experience that is directly applicable in professional settings. #### Overall Impression "Optimization for Decision Making" stands out for its structured approach and practical relevance. The modules build progressively, starting with foundational concepts and evolving towards hands-on applications, ensuring learners can fully grasp complex ideas. Moreover, the use of Excel as a primary tool for solving real-world problems is a significant advantage, aligning with industry practices. #### Recommendation I wholeheartedly recommend this course for anyone looking to enhance their decision-making skills through data analysis. Industry professionals, students of business analytics, or even enthusiasts eager to handle complex decision-making scenarios will find immense value in the content. Not only does it foster a deeper understanding of optimization principles, but it also equips learners with practical tools that are critical in the modern workforce. If you are serious about stepping up your analytical game and making data-driven decisions in your career or business, enrolling in "Optimization for Decision Making" on Coursera is a decision you won’t regret!

Syllabus

Module 1: Introduction to Linear Programming

Prescriptive analytics is a part of business analytics that is aimed at prescribing solutions to decision problems. The most important modeling technique within prescriptive analytics is optimization. In this module, we will learn how to recognize contexts where it can be applied and get introduced to the basics of linear optimization.

In order to solve linear optimization problems (i.e., linear programs), we can use graphical methods for basic example problems. For higher dimensional problems, we will use tools like Excel Solver later in the course. The benefit of using graphical methods is that it gives us an intuition into how these problems can be solved.

In this module we will explore what happens when the model parameters are changed. We will also look at special cases of linear optimization problems.

Having learned how to formulate linear optimization problem and the graphical methods for solving them, we are now going to start solving larger problems using Excel Solver. This module provides an overview of how to set up and solve these decision problems using Excel.