This is the second course I have taken from Roger Peng and both were outstanding. I have a strong math background, but not much of a background in stats, but this course was very approachable for me.
Very good course! It provide me the foundation in learning how to plot and interpret data. This will definitely strengthen my "R programming" to generate publication type figure for my genomics data!
By Terry L J•
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) .
By Igor T•
Good introduction to patterns recognition. I found principal components analysis technique very useful. It would be great to provide more lectures about this topic.
By Carlos G W•
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.
By Diego T B•
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.
By Robert W S•
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.
By Guillaume S•
Interesting course to know plotting systems and to have a first view on clustering and dimensions reduction. This part should be however more developed !
By Hyun J K•
Great lecture. I hope there were more assignments. (1 per a week maybe).
I learned many statistical concepts and rcodes by taking this course.
By Hank C•
Course material, lectures, exercises are excellent.
There was not enough theory, and there was too much specific to R and graphing packages covered.
By Robin S•
The course was fantastic. It was very challenging. I could do with some additional opportunities for exploratory analysis to reinforce some concepts.
By Steven C•
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.
By Ramakumar A•
though presentation was good ,felt it should have been better in small sessions , lost interest half way through , continued later to complete
By Ashutosh K S•
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.
By Bijan S•
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.
By Piyush D•
Awesome course ! It reaches you the crux of exploration of data . Although the SVD section could have been more thorough and detailed.
By Marc T•
Great introduction! I am eagerly awaiting the opportunity to apply clustering and dimension reduction on real data in future courses.
By Andres U•
Really helpful. I really enjoyed getting familiar with plotting systems and also increasing my abilities dealing with data frames
By Tony W•
Very interesting and insightful course. I enjoyed it.
Assignment was okay, could have provided more challenge and depth though.
By Nils M•
Very good course. I liked the clustering examples. They were a little bit detached from the rest, but they were also great.
By Christopher L•
great intro to the plotting system. could be better with a dimension reduction assignment or quiz. this is very important!
By Luiz E B J•
I would rate 5 if the course wasn´t so focused on graphic analysis. But, even Like that it´s a very good experience.
By David B•
It was a lot of material in a short time frame, but I feel like I really have a good grasp of creating graphs in R.
By Olav N•
A very well organized course with video lessons that inspire to further exploration of the data analysis topic.
By Jean-Philippe M•
More practical exercises using ggplot2 and clustering would be beneficial. Maybe need to be a 8 weeks module.
By Saurabh M•
Quantitatively assignments were less with no assignment for Week 3, however, the swirl exercises were good
By Anang S A•
this course is more about creating chart for EDA, need more material for reading/interpreting the charts