Principal Component Analysis (PCA) and Factor Analysis
4.6 (11 ratings)
37 students enrolled
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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)
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
• 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
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Curriculum For This Course
17 Lectures
01:40:13
+
Principal Component Analysis (PCA)
8 Lectures 52:26
Preview 01:23

• 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

Preview 01:21

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