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