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!
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.
par Thomas G•
A lot of broken swirl(), which wouldn't be so bad except *a lot* of this course is based entirely on swirl(). Also the swirl() text was almost verbatim of the lectures one has just watched.
All in all, good information, but the swirl() badly needs an update.
par Ray O C•
The first two weeks were good. The third was a bit confusing and the 4th one just felt like padding. A more in depth study of ggplot would probably be more beneficial as I felt like we were only scratching the surface with it
par Toby K•
Excellent overview of plotting and clustering. However, there were a few bits that were required for good completion of the projects that weren't covered in detail. Overall an excellent course and specialization.
par Ralph M•
Good course overall. There tends to be many lectures that are just lists of commands. Also, they don't seem to be updating the material. Many lectures are several years old and still have typos in them.
par Samer A•
It's pity that the final assignment doesn't involve the clustering and the principal component analysis. It was quite a demanding topic and I was looking forward to practicing it through solving tasks.
par Fabiana G•
Course feels somewhat abandoned by instructors. Content is okay, but can't help the feeling that it's basically a cash cow - students would benefit a lot if instructors were move involved.
par Ashish T•
Great introduction to the plotting libraries in R and visualization of data.
However the introduction to hierarchical clustering, and Principle component analysis was extremely vague.
The course content applies to R. The teachers focused on the programming language rather than the application of the existing graphs to explore data.
par Gianluca M•
A nice introduction to the three plotting systems in R. The second part is devoted to clustering, but it is not detailed enough to be really useful.
par Andreas S J•
Important and interesting stuff - but lots of it is repeated too much, which make it seem like 4 weeks is too much for the material.
par Dylan P•
I would have liked an assignment to focus on the clustering methods and I think dimension reduction was reviewed way too quick.
par ozan b•
Course is good in general but "HIERACHICAL CLUSTERING" part is hard to understand and is not clear, should be explained more.
par Casey B•
Good class - links and slides have not been updated recently. Frustrating to finish without the exact linkts to the data.
par Katharine R•
Good course, but the SWIRL exercises (and a few quiz questions) needed to be updated for the latest version of ggplot2.
par Johnny C•
In general was good, but there were some lectures and exercises which were disorganized ("plots with colors")
par Erkan E•
I wish there several comprehensive examples of exploring some real data as guided by the course instructors.
par Mehrdad P•
The course was overall ok, but I wish discussions about k-means, PCA and SVD were divided into two courses.
par Daniel P•
I've learned plotting in R. I expect to learn more in four weeks of "Data Science" specialization.
par Stuart A•
Course hasn't been updated in a long time, some of the data needed for the projects has migrated.
par Francisco M R O•
The third and fourth week were a big leap in knowledge and not really well explained, for me.
par sandeep d•
Excercises are very good. But I believe lecture could be more interesting and easily taught.
par Guy P•
It misses an assignment which will allow to practice the clustering skills.
par Alex s•
It focus too much on the tools and a little bit on the analysis
par Amit O•
faced many technical difficluties in pratcice exerices in swirl
par Eduardo V K•
There seems to be some outdated info in several tests.