I liked the course. I learnt a lot while working on its project. Instructor's way of teaching is very engaging.

The material in this course is explained very well. However it requires one has the knowledge in using R.

Contents are easier compared with other courses in this series. Quite systematic and easy to understand.

Clearly presented. R instruction is pretty minimal, so there is a lot of trial and error and googling.

I liked, but I guess it could improve little by including more topics in linear regression analysis.

The Lab could be better if there was a video that goes with it. But overall I love this course.

I learnt quite a bit. One of the most useful courses! I would suggest add more exercises in R.

This course has provided me with a good and simple understanding on the concept

This is a great course for this specialization but don't expect much depth.

nice lecture, but it is really too short, not into too much details.

Not for beginners but overall ideas are explained quite clearly

Excellent course, I think it still could include more content!

Great course and lots of useful knowledge!

Not covered entire regression technique

A good course for Bayesian statistics.

Great model with clear explanations

Nice overview and R examples!

Very useful insights and lea

Could be more informative

Awesome course.

A good course

very nice.

excellent

good

Other nits about the depth and breadth of the course aside, I thought it was a good course. The main critique I have to offer is the lack of emphasis of using the power of R. When teaching model selection, the course should have at least provided instruction—or at least a written resource—on how to write the R code for automating forward/backward selection by R^2.* Being a course about using R as well as about linear regression and modeling, it seems like the appropriate thing to do.

(*A classmate whose final project I peer-reviewed used for loops to run the forward model selection based on R^2. That's how I learned about it.)