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!
par Greg A•
This is a very good course, at times it felt like the instruction was to do things mechanically without understanding the motivation. Perhaps this should come after or in conjunction with Statistical Inference
par Carlos R•
I love the course. However, the treatment of PCA, SVD, and colors seems to me very long and slow. Maybe a more direct and quick overview would be better. Even with that expection I really enjoy the course.
par Ben K•
It was fun and interesting learning how to explore the data. For the final project I missed a assignment about clustering, PCA and SVD. It could be useful for a better understanding of the concepts.
par Bill S•
The course on Exploratory Data Analysis was highly enjoyable. I used to do a lot of this sort of thing in my job, but now spend more of my time managing people. It is fun to get "hands-on" again.
par Jukka H•
Great in-depth content about techniques related to exploratory data analysis and implementation in R language using R Studio. Definitely recommend this course to any aspiring data scientist!
par Raviprakash R S•
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 ....
par Luke S•
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.
par Ng B L•
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.
par Štefan Š•
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.
par Diego P G•
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
par Christian B•
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.
par Hernan D S P•
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.
par 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) .
par 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.
par 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.
par DESIREE P•
We learn very useful things. However, there is little emphasis on the statistical part (singular value decomposition) which I think deserved more exercises.
par 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.
par 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.
par 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 !
par 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.
par 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.
par Robin S•
The course was fantastic. It was very challenging. I could do with some additional opportunities for exploratory analysis to reinforce some concepts.
par 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.
par Ramakumar A•
though presentation was good ,felt it should have been better in small sessions , lost interest half way through , continued later to complete
par 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.