Principal Component Analysis (PCA) and Factor Analysis
Analytics / Machine Learning / Dimensionality Reduction : PCA & Factor Analysis using SAS and R program
4.15 (231 reviews)
928
students
1.5 hours
content
Jun 2018
last update
$39.99
regular price
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
Why take this course?
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
- what is the variance in the data in different dimensions?
- what is principal component?
- Formal definition of PCs
- Understand the formal definition of PCA
- Properties of Principal Components
- Understanding principal component analysis (PCA) definition using a 3D image
- Properties of Principal Components
- Summarize PCA concepts
- Understand why first eigen value is bigger than second, second is bigger than third and so on
- Data Treatment for conducting PCA
- How to treat ordinal variables?
- How to treat numeric variables?
- Conduct PCA using SAS: Understand
- Correlation Matrix
- Eigen value table
- Scree plot
- How many pricipal components one should keep?
- How is principal components getting derived?
- Conduct PCA using R
- Introduction to Factor Analysis
- Introduction to factor analysis
- 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
Screenshots
Reviews
Satvik
August 7, 2022
It was a good experience but I think a little more elaboration on the topics would have been really good. However, if somebody wishes to learn PCA in an easy-going manner, then, one should take this course.
Marina
April 2, 2021
The course was pretty good. The instructor explains it clear and slow, so it is easy to follow him. The bad point is that I think we should have examples with larger datasets since the topic is about dimensionality reduction, but we worked with less than 20 rows in the first lab. Also it would be good to have more practical examples.
Usman
January 21, 2021
the instructor had a very vague and ambiguous way of interpreting and delivering the core concepts including lack of practice before executing the snippets.
Craig
October 6, 2020
Looked good in the first section, but degenerated into unclear explanations for the rest of the course. The instructor will show you how to use the methods in R, but whether or not you will understand them is a different story.
Oded
September 26, 2020
Describes the essence of principal components analysis in a very basic simple manner, which everyone can understand. Bravo!
Mary
June 16, 2020
I found the walk through very helpful and appreciated the examples and explanation of the analytics. I would have appreciated a bit more on different use cases for PCA and factor analysis.
Aditya
May 13, 2020
The course is crisp and clear. It clearly explains the intuition behind PCA and Factor Analysis, use cases for each and the difference between the two for example latent variables in Factor and calculated variable in PCA etc. I would recommend this course to anyone who is interested in getting these two techniques understood and practiced in one sitting. Thanks.
Jorge
October 19, 2018
Me gusta el tema, las explicaciones son intuitivas pero deficientes y poco detalladas. Esperaba un curso de mayor nivel.
Cuesta trabajo entender el inglés del expositor, quizá por su fuerte acento. No hay presentaciones disponibles, no hay ejercicios, y las presentaciones descritas en los videos dejan mucho que desear.
Matt
August 8, 2018
There is a good introduction, but course is pretty short and could really benefit from a more thorough example. We get through the examples and are left with a thought of- okay, now what? I've seen a few online examples or PCA and FA with R (for free), and all go into as much or more detail. For a paid course, this should go above those.
Mathias
August 2, 2018
Nicely explained. Language could be a bit clearer (f.E. z (to the?) squared) for Z2 is not quite clear.
RIA
July 28, 2018
Yes,initial lecture was good.,but there was problem in understanding min perpendicular distance of all points in the direction of 1st principal component.
Osama
June 20, 2018
The language can be a bit clearer, I would suggest more improvement in explaining the meaning of PCA from theoretical point of view. Additionally, if the instructor can provide more scenarios where the data need normalization (scaling), and where the data are mixed (numerical and categorical). This will enhance the course greatly.
Deepak
March 24, 2018
More detailed or some industry related examples will make course more useful and easy to understand.
I believe this course should be added with credit card scoring through logistic regression and can be explained better by taking data set used in that session rather than 16 observation data used here. This would have made understanding a little better of PCA and Factor Analysis stuff.
Franklin
September 5, 2017
Excellent course. I never really understood PCA or Factor Analysis until this course, and I have a PhD in Economics.
Robert
June 21, 2017
I have been struggling with PCA, especially how to use artificial values to determine the importance of raw variables.
I can now say that I confidently understand how to interpret the results of a PCA! I could not be happier with this course.
Charts
Price
Rating
Enrollment distribution
Related Topics
1147252
udemy ID
3/16/2017
course created date
11/22/2019
course indexed date
Bot
course submited by