Sep 23, 2017
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!
Jul 28, 2016
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.
By Ashish T•
May 5, 2018
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.
Mar 10, 2016
The course content applies to R. The teachers focused on the programming language rather than the application of the existing graphs to explore data.
By Gianluca M•
Oct 13, 2016
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.
By Andreas S J•
Oct 4, 2017
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.
By Dylan P•
May 13, 2018
I would have liked an assignment to focus on the clustering methods and I think dimension reduction was reviewed way too quick.
By ozan b•
Feb 5, 2017
Course is good in general but "HIERACHICAL CLUSTERING" part is hard to understand and is not clear, should be explained more.
By Casey B•
May 12, 2016
Good class - links and slides have not been updated recently. Frustrating to finish without the exact linkts to the data.
By Katharine R•
May 3, 2016
Good course, but the SWIRL exercises (and a few quiz questions) needed to be updated for the latest version of ggplot2.
By Johnny C•
Mar 6, 2018
In general was good, but there were some lectures and exercises which were disorganized ("plots with colors")
By Erkan E•
Jun 24, 2016
I wish there several comprehensive examples of exploring some real data as guided by the course instructors.
By Mehrdad P•
Aug 25, 2019
The course was overall ok, but I wish discussions about k-means, PCA and SVD were divided into two courses.
By Daniel P•
Dec 8, 2019
I've learned plotting in R. I expect to learn more in four weeks of "Data Science" specialization.
By Stuart A•
Jul 18, 2020
Course hasn't been updated in a long time, some of the data needed for the projects has migrated.
By Francisco M R O•
Jan 8, 2019
The third and fourth week were a big leap in knowledge and not really well explained, for me.
By sandeep d•
Mar 10, 2018
Excercises are very good. But I believe lecture could be more interesting and easily taught.
By Guy P•
Mar 26, 2016
It misses an assignment which will allow to practice the clustering skills.
By Alex s•
Jan 17, 2018
It focus too much on the tools and a little bit on the analysis
By Amit O•
Sep 30, 2017
faced many technical difficluties in pratcice exerices in swirl
By Victor M C T•
Jan 4, 2022
The swirl labs failed, I never could load the "field" module.
By Eduardo V K•
Jun 28, 2020
There seems to be some outdated info in several tests.
By Rafael A•
Mar 23, 2017
First two weeks are too repetitive with other courses
By Kevin F•
Jul 15, 2020
pretty brief and basic. no assessment on clustering.
By Erwin V•
Mar 12, 2016
Interesting stuff, but not a lot of detail
By Oscar P G P•
Sep 17, 2020
It's necessary for more examples!!!!
By Lidiya N•
Apr 28, 2019
Absolutely No technical help, like insane amounts of homework for each week. People have jobs and businesses to run. Incredibly short duration. Like literally this should have been spread out several more weeks. I would have dropped the class but I can’t. It’s so difficult to get i to the first set of practice assignments and these several sets. Honestly, I am literally getting no help on it and probably won’t pass because I am missing the deadline. I finished 5 coursera courses working on them for 24 none stop. I’ve literally been at this class all day. Besides all the insane amounts of assignments there’s tons of videos to watch and uploads to do. Go buy some books or take another class unless you are unemployed or have nothing better to do.