Description
Our ’Factor Analysis’ e-Learning course will help you to understand Factor Analysis and its link to linear regression. See how Principal Components Analysis is a cookie cutter technique to solve factor extraction and how it relates to Machine Learning. Supplemental Materials included!
What is Factor Analysis?
Factor analysis helps to cut through the clutter when you have a lot of correlated variables to explain a single effect. In this course, you will follow along with expert instructors to learn about topics such as Mean & Variance, Eigen Vectors, Covariance Matrices, and much much more!
Modules
Course Duration: 1.75 Hours
Chapter 01: Introduction
You, This Course, & Us!
Chapter 02: Factor Analysis & PCA
Factor Analysis & the Link to Regression
Factor Analysis & PCA
Chapter 03: Basic Statistics Required for PCA
Mean & Variance
Covariance & Covariance Matrices
Covariance vs Correlation
Chapter 04: Diving into Principal Components Analysis
The Intuition Behind Principal Components
Finding Principal Components
Understanding the Results of PCA – Eigen Values
Using Eigen Vectors to find Principal Components
When not to use PCA
Chapter 05: PCA in Excel
Setting up the data
Computing Correlation & Covariance Matrices
PCA using Excel & VBA
PCA & Regression
Chapter 06: PCA in R
Setting up the data
PCA and Regression using Eigen Decomposition
PCA in R using packages
Chapter 07: PCA in Python
PCA & Regression in Python
System Requirements
Minimum specifications for the computer are:
Windows:
Microsoft Windows XP, or later
Modern and up to date Browser (Internet Explorer 8 or later, Firefox, Chrome, Safari)
MAC/iOS:
OSX/iOS 6 or later
Modern and up to date Browser (Firefox, Chrome, Safari)
All systems:
Internet bandwidth of 1Mb or faster
Flash player or a browser with HTML5 video capabilities (We recommend Google Chrome)
