Jul 29, 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.
Sep 24, 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!
par Soma S K•
Sep 29, 2018
Good Place to learn
par Anup K M•
Sep 27, 2018
par Praveen k•
Oct 02, 2018
Nov 02, 2018
par Julien N•
Jul 13, 2018
A good start for data analysis, this course covers the basics of plotting with the three most common packages (base R, lattice, and ggplot2).I liked the assignment which difficulty is nicely measured (it is not just applying the videos concept, you have to look around the web to find tools and documentation about what functions to use).On a less positive aspects:- I am not sure this course was the best place to introduce kmean and PCA sections...- a lot of content is outdated (wrong links, old R command parameters, ...), look likes a quick freshup update would not do harm given the number of people that keeps registering...
par Guilherme B D J•
Jun 09, 2016
The only missing point I would say about this course is how to deal with skew data and/or outliers. Although it is not specific to "cleaning data", I think there is a good opportunity there to at least give some hints on this subject
Apr 25, 2018
Nice course, very useful. I wish the links were updated more often, however.
Aug 27, 2016
Hopefully it could be clearer on dimension reduction.
par Ankush K•
Jun 15, 2017
Useful for learning how to make plots, but not too detailed. Further analysis would have been helpful.
par Carlos R•
May 29, 2017
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 Zhang S•
Jul 09, 2018
Week 3 content is difficult to understand without background knowledge in clustering and component analysis. Hope the instructor can provide some materials or web links for cluster and component analysis at the beginning of Week 3. Other weeks' contents are good and helpful!
par Adur P•
Dec 28, 2017
par Migdonio G•
Apr 10, 2018
You should give more datasets for independent practice! Something we can play with.
par Harshitha R•
Feb 22, 2016
The course did a good overview of the different plotting systems in R, but it rushed through clustering. I had to watch the videos of k-means and hierarchical clustering at least 3 times to sort of understand it. The matrix concepts went completely over my head. Otherwise, the projects were very interesting, and I would highly recommend this course to other people.
par Phillip K•
Mar 20, 2018
Good stuff just as I have come to expect from this University and the courses that are part of this Signature Track.
A great deal of the lectures and work on assignments/quizzes/projects was learning and using the various plotting systems in R. Certainly this is important, but to put it into perspective, I spent four hours creating six plots for the final project, when I was able to use Tableau Desktop to create all six plots in under five minutes.
So formally learning the data exploration techniques was good, but expect much of this course to be about learning the R plotting systems.
That said, there is a point in this course (and the first time for all the courses to this point) where the topic suddenly got very, very technical. When clustering techniques were introduced it felt as if you were turned on your head as the focus suddenly went from various ways of plotting data in R to being neck deep in the explanation of clustering techniques that require a great degree of Linear Algebra knowledge.
Don't panic though. While there are questions in the guided assignments that are difficult, you don't really need to recall all of your Linear Algebra courses from college to pass this course. After all, R "has a package for that."
par Deepak R•
Oct 02, 2016
par Štefan Š•
Apr 17, 2016
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 Jeff B•
Mar 04, 2018
The plotting aspects of this course appealed to my visual sense.
par Ng B L•
Mar 09, 2017
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 Raviprakash R S•
Feb 14, 2017
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 Praveen K B•
Sep 14, 2017
Good One to start with
par Philip E W J•
Mar 21, 2017
Would have needed a litle more in depth explanation of the clustering analysis
par Ratnikov Y•
May 27, 2017
Clustering is overwhelming field of knowledge.
par Chuxing C•
Dec 03, 2015
I have taken the course earlier, so am somewhat familiar with the layout and the materials. Overall it is a very good course and covers a wide range of subject matters. Roger has done a very good job explaining the concepts. I certainly would recommend this course to all who's interested in the subject.
I realize that there's limitation on the time people suppose to spend each week, however, I would like to suggest adding homework, in addition to quizzes.
Several video clips have some audio issues, not sure if that's fixable.
par Subramanya N•
Dec 12, 2017
ggplot should have been given more emphasis. It warrants a course on its own!