Principal Component Analysis (PCA) and Factor Analysis

Principal Component Analysis (PCA) and Factor Analysis, available at $39.99, has an average rating of 4.2, with 18 lectures, based on 237 reviews, and has 948 subscribers.

You will learn about 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 This course is ideal for individuals who are Analytics Professionals or Research Scholars or Data Scientists It is particularly useful for Analytics Professionals or Research Scholars or Data Scientists.

Enroll now: Principal Component Analysis (PCA) and Factor Analysis

Summary

Title: Principal Component Analysis (PCA) and Factor Analysis

Price: $39.99

Average Rating: 4.2

Number of Lectures: 18

Number of Published Lectures: 18

Number of Curriculum Items: 18

Number of Published Curriculum Objects: 18

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Analytics Professionals
Research Scholars
Data Scientists

Target Audiences

Analytics Professionals
Research Scholars
Data Scientists

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

Course Curriculum

Chapter 1: Principal Component Analysis (PCA)

Lecture 1: Introduction

Lecture 2: How to consume the contents?

Lecture 3: Intuitive Understanding of PCA 2D Case

Lecture 4: Formal defintion of PCs

Lecture 5: Properties of Principal Components – part 1

Lecture 6: Properties of Principal Components – part 2

Lecture 7: Data Treatment for conducting PCA

Lecture 8: Workshop – conduct principal component analysis using SAS

Lecture 9: Workshop – conduct principal component analysis using R

Chapter 2: Factor Analysis

Lecture 1: Introduction to Factor Analysis

Lecture 2: Workshop – conduct Factor analysis using R – part 1

Lecture 3: Workshop – conduct Factor analysis using R – part 2

Lecture 4: Workshop – conduct Factor analysis using SAS

Chapter 3: Using Principal Component Analysis for Variable selection

Lecture 1: Theory for variable selection using PCA

Lecture 2: Demo for variable selection using PCA – Part 01

Lecture 3: Demo for variable selection using PCA – Part 02

Lecture 4: FAQ (will keep growing overtime based on student's queries)

Lecture 5: Closing Note and PDF of course content

Instructors

Gopal Prasad Malakar
Trains Industry Practices on data science / machine learning

Rating Distribution

1 stars: 6 votes
2 stars: 17 votes
3 stars: 33 votes
4 stars: 91 votes
5 stars: 90 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

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