# Principal Component Analysis (PCA) and Factor Analysis

## Description

The course explains one of the important aspect of machine learning – Principal component analysis and factor analysis in a very easy to understand manner. It explains theory as well as demonstrates how to use SAS and R for the purpose.

The course provides entire course content available to download in PDF format, data set and code files. The detail course content is as follows.

• Intuitive Understanding of PCA 2D Case
1. what is the variance in the data in different dimensions?
2. what is principal component?
• Formal definition of PCs
1. Understand the formal definition of PCA
• Properties of Principal Components
1. Understanding principal component analysis (PCA) definition using a 3D image
• Properties of Principal Components
1. Summarize PCA concepts
2. Understand why first eigen value is bigger than second, second is bigger than third and so on
• Data Treatment for conducting PCA
1. How to treat ordinal variables?
2. How to treat numeric variables?
• Conduct PCA using SAS: Understand
1. Correlation Matrix
2. Eigen value table
3. Scree plot
4. How many pricipal components one should keep?
5. How is principal components getting derived?
• Conduct PCA using R
• Introduction to Factor Analysis
1. Introduction to factor analysis
2. Factor analysis vs PCA side by side
• Factor Analysis Using R
• Factor Analysis Using SAS
• Theory for using PCA for Variable Selection
• Demo of using PCA for Variable Selection

## Who this course is for:

• Analytics Professionals
• Research Scholars
• Data Scientists

## Requirements

• The course will start with elementary concepts but knowledge of basic statistics will help
• For execution – it will help to know basic SAS or R programming

Last Updated 6/2018