Principal Component Analysis (PCA) and Factor Analysis


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


  • 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

Download Links

Direct Download

Principal Component Analysis (PCA) and Factor (280.8 MB) | Mirror

Torrent Download

Principal Component Analysis (PCA) and Factor Analysis.torrent (32 KB) | Mirror

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