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

4.7
stars
5,814 ratings
842 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

CC
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.

Y
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!

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651 - 675 of 811 Reviews for Exploratory Data Analysis

By Ankush K

Jun 15, 2017

Useful for learning how to make plots, but not too detailed. Further analysis would have been helpful.

By Marcela Q

Nov 26, 2019

Really good if you do not have any background in the field. But even if you do it is a great review

By Joe D

May 19, 2019

Some of the links in the lectures are out of date, the forums usually have an updated link though.

By Lindy W

Nov 24, 2016

Interesting learning more about ggplot and base plotting system, as well as clustering techniques.

By SUDIPTO M

Oct 26, 2017

Thjis one of the best courses gives a great idea about plotting and exploratory data analysis !

By kajal s

Oct 3, 2019

Exploratory data analysis is a very important skill and it is a very good course to learn it.

By Eric J S

May 29, 2019

Best of your courses yet. Doesn't suffer from difficulty spikes when you hit the projects.

By Gerardo M F G

Nov 15, 2020

PCA and SVD are not included in any assignment, it will be great if they are in the future

By Huang-Hsiang C

Jun 9, 2020

Plotting is very usefulIt would be great to have a step by step breakdown of PCA and SVD.

By Mark F

Jul 5, 2017

SVD could be explained a little better i think. I am still not exactly sure how it works.

By Irmgard T

Jul 23, 2017

great course...though I would have preferred less focus on cluster and k means analysis.

By Manuel M M

Sep 27, 2019

It is a good course but in my opinion it is basically support with the R swirl() guide

By Ross D

Sep 4, 2019

Was a little perplexed that we did not address clustering at all in the assignments.

By Prathamesh

Jul 9, 2017

SVD & PCA videos need improvement in terms of background knowledge and understanding

By Migdonio G

Apr 9, 2018

You should give more datasets for independent practice! Something we can play with.

By Sawyer W

Aug 1, 2017

Good course. Mostly focuses on how to visualize statistics from the data quickly.

By Shuwen Y

Jun 11, 2016

great course but wish to have more materials or explanation on svd and PCA part.

By Philip E W J

Mar 21, 2017

Would have needed a litle more in depth explanation of the clustering analysis

By Subramanya N

Dec 12, 2017

ggplot should have been given more emphasis. It warrants a course on its own!

By Greg R

May 30, 2016

Pretty good course. Nice content. Middle section on clustering felt random.

By Pavel B

Feb 18, 2016

I like the course and it was helpful in understanding how graphics work in R.

By RobinGeurts

Feb 21, 2019

End assignement was relatively easy compared to the examples in the lectures

By Mario S P G

Sep 17, 2018

Good beginners course with helpful tools to take a first glance to your data

By Polina

Apr 25, 2018

Nice course, very useful. I wish the links were updated more often, however.

By Jan W

Feb 14, 2021

Learned a lot, the cluster and kmeans could have been more explained though