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Learner Reviews & Feedback for Exploratory Data Analysis by Johns Hopkins University

5,884 ratings
856 reviews

About the Course

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data....

Top reviews

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.

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751 - 775 of 825 Reviews for Exploratory Data Analysis

By Praveen k

Oct 2, 2018

Nice course

By Divvya.T

Oct 29, 2017

good course

By Abhishek S

May 30, 2017

good course

By Ussama N

May 20, 2017

Good course

By 贝叶斯统计

May 23, 2016


By Colin Q

Jun 1, 2017

very good!

By Jeremy O

Mar 9, 2017


By Timothy V B

Dec 29, 2016

good intro

By Johnnery A

Nov 17, 2019


By Khobindra N C

May 18, 2016


By Rohit K S

Sep 20, 2020


By Tae J Y

Mar 31, 2017


By Edward A S M

Dec 5, 2019


By 木槿

Nov 2, 2018


By Anup K M

Sep 27, 2018


By Isaac F V N

Apr 18, 2017


By Chan E

Mar 22, 2016


By Adur P

Dec 28, 2017


By Saurabh K

Apr 27, 2017


By Deepak R

Oct 2, 2016


By Jose O

Feb 11, 2016

Insights delivered by the course were great. However, I think it emphasizes too much the lattice and basic plot systems to the point it is redundant with functionality on ggplot. It should focus more on concepts and techniques for delivering richer and meaningful graphics using ggplot rather than talking that much about technicalities on the basic plot and lattice systems.

Assignments were too basic and don't reflect all the concepts learned in the lessons e.g. clustering, which I think are of great interest for researchers.

By Ahmed M

Aug 24, 2016

The course is quite good and informative in the first two weeks covering a lot of information and a lot of exercises.

Week 3 is very unrelated and hard the videos and exercises are bad, and I had to do this part by myself again.

Also when we get to the final course project doesn't cover any of these techniques.

In my opinion, week 3 should be replaced with something more related to plotting systems and distributions, also one project would be enough.

By Andrew V

Jun 10, 2016

The course covers very limited subset of plots and mostly oriented to R-specific technical routines rather than overall approaches. Case-study example is helpful and contrary to the most comments I do appreciate the final course project: this how most problems are stated in real life. If you would like to cover more fundamental concepts behind exploratory analysis I would recommend other sources.

By Mohammad A A

Mar 11, 2019

It was a very useful course with some meaningful homework. My only criticism is that sometimes the theory and the practice are not well connected. Particularly the discussion of PCA, hierarchical clustering, k-means clustering and others. It would be benefit by providing more meaningful reading for those interesting in better connecting the two

By Arne S

Aug 31, 2019

did not like the swirl-tutorials. they were very tedious and sometimes labelled correct commands as false (e.g. when you typed = instead of <- for assigning a value to a variable)

also I was surprised that for a beginner programming course in R you had to apply specific functions such as grepl without the function being introduced in the course