Principal Component Analysis (PCA) and Factor Analysis (Udemy.com)

Interesting introduction into some difficult topics. Would be nice if more effort is put into explaining the concepts and less into R and SAS, in other words in tool agnostic ways with some simple data values for a greater grasp on the concepts.

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.

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.

The course is written in an intuitively manner, pariticularly for those not interested in digging deeper into the process of computing Eingenvectors and Eigenvalues using Matrix Algebra. The professor does a good job at this level.

Great content in a very simplified form. Worth the time. Will surely recommend this to my friends and colleagues. Thanks sir for this wonderful course.

I found this course a nice introduction to Principal Component Analysis (PCA) and Factor Analysis. I found the instructor engaging and easy to follow. That is until the last lecture, on choosing variables, when some rules of thumb were applied to the process that were not at all explained. When I reached out to the instructor I did not feel he answered my questions. I also wish he would have provided a section on reverse PCA (once you have reduced the PCs you work backwards to reconstruct the dataset with reduced variance). I would recommend the course to others interested in an introduction to PCA in R and SAS. It is nice that there are course materials for download for later reference.

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.

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.

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.

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.