Nice course, but too much focus on "R" as a tool.... Industries don't use R as much... The course must be made more generic and independent of R - understand it is not easy to do but ....

Good introduction. The swirl exercises kind of reproduce the lectures though- felt like it might not have been the most efficient use of time to go over the exact same example again.

When it comes to hierarchical and K-means clustering, the theory wasn't explained clearly. When do we use U and V for what purpose? How does D come in? I'm left confused after this.

I found it very useful.

Some space for improvement are better coding skills (naming variables) and

some more complex topics like SVD / PCA should be explained in a more intuitive way.

It's a very good course. Week 3 was a little bit more challenging than expected, as well as assignment 2, but you get a good idea of how to use all the different plotting systems

The course is interesting and the content is relevant. I do think that there are some issues with project 2 though. I did provide feedback on that to the course administrators.

I learned a lot on this course, it helped me to understand and identify some of the situations I experience at work. Totally recommended if you want to apply it right away.

Seems this would type of course in an online learning MOOC would be better if it was more direct hands on "how to" and less focused on explanatory fluff (academic style) .

Good introduction to patterns recognition. I found principal components analysis technique very useful. It would be great to provide more lectures about this topic.

I enjoyed the course and learned a good deal. However, the level of challenge of the projects is much higher than the scant explanation provided by Dr. Peng.

We learn very useful things. However, there is little emphasis on the statistical part (singular value decomposition) which I think deserved more exercises.

Interesting. But I would prefer the differences between comparison plots. What do they are useful and why is it better to plot with bars rather than lines.

A quiz or project question on k-means clustering or PCA would be nice. Overall the course provided solid coverage of the three main plotting systems in R.

Interesting course to know plotting systems and to have a first view on clustering and dimensions reduction. This part should be however more developed !

Great lecture. I hope there were more assignments. (1 per a week maybe).

I learned many statistical concepts and rcodes by taking this course.

Thank you:)

Course material, lectures, exercises are excellent.

There was not enough theory, and there was too much specific to R and graphing packages covered.

The course was fantastic. It was very challenging. I could do with some additional opportunities for exploratory analysis to reinforce some concepts.

Good course on plotting libraries and useful plots in R. Wished there was more coverage of ggplot and less on lattice, but overall a useful course.

though presentation was good ,felt it should have been better in small sessions , lost interest half way through , continued later to complete

It delves into many important topics. I would advice to explore the topics in much more depth on your own. Overall a good breadth of topics.

The course is useful with a lot of learning.

The second half needs more of improvement, I think the pace is quite fast compared to others.

Awesome course ! It reaches you the crux of exploration of data . Although the SVD section could have been more thorough and detailed.

Great introduction! I am eagerly awaiting the opportunity to apply clustering and dimension reduction on real data in future courses.

Really helpful. I really enjoyed getting familiar with plotting systems and also increasing my abilities dealing with data frames

Very interesting and insightful course. I enjoyed it.

Assignment was okay, could have provided more challenge and depth though.