This course is very interesting and Professor Caffo is very good at teaching. The proposed material is not very well organised and even if there are multiple sources available (videos, book, YT videos) they all say the exact same things (literally): it isn't helpful, only redundant. Moreover, I felt a lack of context: yes, it is only a 6 weeks course and yes there is a strict relationship with at least other 5 courses on Coursera (as a prerequisite) so it may be hard to contextualise. Nevertheless the material seems to be taken here and there from other courses/specialisations so you often have the feeling that you are missing something that may have been said in a previous lesson that does not belong to this course (and I am not talking about basic linear algebra stuff) or you wonder "what are we trying to prove? And why?". Material desperately lacks homogeneity, and it's easy to lose focus. Last but not least: R is a prerequisite, which is a bit strange since the topic is very theoretical (and there are no practical references throughout the course). R is mainly used by the professor to prove that theory is right (Wouldn't be more interesting to take advantage of R's plotting capabilities to have a visual result of theory?) or there are quiz questions that require the usage of R to get the answer (Why? Am I supposed to learn R or Least Squares?). Sometimes you feel like this is not a standalone, focused course, but an appendix of other specialisations. Overall it's a good course, very interesting topic, made harder by material that is a bit collected here and there and put together without the care the subject would require. My suggestion is to enroll only after completing Statistical Inference and Regression Models (both by B. Caffo) so that language and context are the same.

We need more advanced, theoretical courses on Coursera, like this one, in order to deeply understand the more general courses like Regression Models and Linear Models.

The topics covered by this course were really relevant, and it allowed me to better understand many things I have been using blindly for years. That being said, the course preparation by the lecturer appears quite careless. The videos are difficult to follow, with the electronic pen not helpful to comprehend the handwriting and full of mistakes the lecturer needs to correct constantly. Moreover, the quizs have included errors apparently for a long time, as the forums reflect, and no one has corrected them so far. The book recommended for the course is still in a very poor state, not only unfinished but also full of mistakes, making the task of linking the content it includes with the lecturer's explanations challenging at times. Fortunately, I did not pay for it, as it can be obtained for free if the student desires. However, those who spend the money the platform recommends to pay will have heavy reasons to be upset.

This course was revelation upon revelation (in addition to refreshing/re-envisioning some basics). I appreciated the connections between Principal Component Decomposition, Eigenvalue Decomposition, and Singular Value Decomposition.

My only issue was with vector notation, but otherwise I recommend this course for anyone who wants an intimate understanding in Least Squares regression.

I really enjoyed the course. It was well explained and the quizzes at regular intervals were helpful. It would be great if there were some practice exercises though...

As the name says it's an advanced course. Take the challenge though! In my opinion the content is a must if you want to perform competently in data science.

Well-designed math-oriented course about OLS. The instructor is great. It was a good occasion to strengthen my skills in linear algebra.

Lectures are monotonous. Not enough exercises.

I was expecting more practical examples in R.

The teacher is like explaining to himself with no idea of who is in the other side. He knows the material but the course is just so boring and not becuse of the material itself but because of the way it is tought.

Good mathematical rigour for the analysis of linear models. Builds some good intuition for the geometry of least squares which helps in model result interpretation.

This is a very good lecture for a understanding the regression in the view of linear algebra. However, a prior understanding of some linear algebra is needed.

Excellent experience. I have learnt a lot in different aspect of linear models as well as the coding skills from this course. Thank you.

Great refresher of linear algebra

understood many things about linear models that I just knew superficially from its cores

It was my first time but this was an awesome and interesting course,thanks for Brian Caffo and John Hopkins University

chapter on bases showing four equivalent forms was brilliant! Hoping to learn BLUE, GAMs in part 2.

good course that teaches these kind of hard work in an easier understanding way.

A wonderful course to study! Prof. Brian Caffo explains so well!

Thank you. A very challenging and deeply insightful course.

Very thorough and rigorous. A great review for me.