Fundamentals of Quantitative Modeling

Start Date: 07/05/2020

Course Type: Common Course

Course Link:

About Course

How can you put data to work for you? Specifically, how can numbers in a spreadsheet tell us about present and past business activities, and how can we use them to forecast the future? The answer is in building quantitative models, and this course is designed to help you understand the fundamentals of this critical, foundational, business skill. Through a series of short lectures, demonstrations, and assignments, you’ll learn the key ideas and process of quantitative modeling so that you can begin to create your own models for your own business or enterprise. By the end of this course, you will have seen a variety of practical commonly used quantitative models as well as the building blocks that will allow you to start structuring your own models. These building blocks will be put to use in the other courses in this Specialization.

Course Syllabus

In this module, you will learn how to define a model, and how models are commonly used. You’ll examine the central steps in the modeling process, the four key mathematical functions used in models, and the essential vocabulary used to describe models. By the end of this module, you’ll be able to identify the four most common types of models, and how and when they should be used. You’ll also be able to define and correctly use the key terms of modeling, giving you not only a foundation for further study, but also the ability to ask questions and participate in conversations about quantitative models.

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Course Introduction

Fundamentals of Quantitative Modeling In this course, you will learn the mathematical foundations of most of the most important modern quantitative models. You'll learn why most modern quantitative models are mathematically difficult, and how these difficulties can make it difficult to apply the most important insights. We'll focus on the main limiting assumptions in most modern models, and how these assumptions can introduce random errors. We'll also discuss how these errors can cause underperformance. We’ll also discuss how these errors can introduce "black swans" in order to amplify the impact of the model on the real world. We’ll also discuss how these errors can introduce "misalignments" in order to amplify the impact of the model on the real world. Lastly, we’ll discuss how these errors can introduce "feedbacks" in order to amplify the impact of the model on the real world. This course covers the following topics: * Linear equations and fundamental concepts of probability * Logistic regressions and basic concepts of normal distribution * Continuous integration and basic assumptions * The random variable calculus and the concept of random variables * The random variable test and the concept of random variables * The random variable model and the random variable test Learning Outcomes: After taking this course, you will be able to: * Calculate most important constraints in most modern linear models * Solve most important natural experiments * Identify most important random

Course Tag

Modeling Linear Regression Probabilistic Models Regression Analysis

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