Principal Component Analysis (PCA) and Factor Analysis
4.6 (11 ratings)
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Principal Component Analysis (PCA) and Factor Analysis

Analytics / Machine Learning : Principal Component Analysis and Factor Analysis using SAS and R program
4.6 (11 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
37 students enrolled
Last updated 5/2017
English
Price: $20
30-Day Money-Back Guarantee
Includes:
  • 1.5 hours on-demand video
  • 1 Article
  • 12 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Understand Principal Component Analysis and Factor Anallysis in crysal clear manner
  • Will know how to coduct principal component analysis and factor analysis using SAS / R
  • Will understand, how PCA helps in dimensionality reduction
  • Will understand the difference and similarity between PCA and factor analysis
  • Students will be able to use PCA for variable selection
View Curriculum
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
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 is the target audience?
  • Analytics Professionals
  • Research Scholars
  • Data Scientists
Students Who Viewed This Course Also Viewed
Curriculum For This Course
17 Lectures
01:40:13
+
Principal Component Analysis (PCA)
8 Lectures 52:26

  • what is the variance in the data in different dimensions?
  • what is principal component?
Intuitive Understanding of PCA 2D Case
07:52

Understand the formal definition of PCA
Formal defintion of PCs
03:43

  • Understanding principal component analysis (PCA) definition using a 3D image
Preview 02:36

  • Summarize PCA concepts
  • Understand why first eigen value is bigger than second, second is bigger than third and so on
Properties of Principal Components - part 2
07:25

  • How to treat ordinal variables?
  • How to treat numeric variables?
Data Treatment for conducting PCA
07:52

Understand

  • Correlation Matrix
  • Eigen value table
  • Scree plot
  • How many principal components one should keep?
  • How is principal components getting derived?
Workshop - conduct principal component analysis using SAS
15:11

Workshop - conduct principal component analysis using R
06:24
+
Factor Analysis
4 Lectures 34:08
  • Introduction to factor analysis
  • Factor analysis vs PCA side by side
Preview 05:00

Workshop - conduct Factor analysis using R - part 1
07:02

Workshop - conduct Factor analysis using R - part 2
12:16

Workshop - conduct Factor analysis using SAS
09:50
+
Using Principal Component Analysis for Variable selection
5 Lectures 13:39
Theory for variable selection using PCA
03:52


Demo for variable selection using PCA
06:23

FAQ (will keep growing overtime based on student's queries)
00:33

Closing Note and PDF of course content
01:30
About the Instructor
Gopal Prasad Malakar
4.3 Average rating
1,594 Reviews
20,174 Students
16 Courses
Credit Card Analytics Professional - Trains on Data Mining

I am a seasoned Analytics professional with 16+ years of professional experience. I have industry experience of impactful and actionable analytics. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios. My expertise is in Credit Card Business, Scoring (econometrics based model development), score management, loss forecasting and MS access based database application development.