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

4.7
stars
5,860 ratings
851 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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576 - 600 of 821 Reviews for Exploratory Data Analysis

By Robert J C

Jul 25, 2019

Good.

By Adán H

Oct 5, 2017

Good!

By Rafael L G

Jun 6, 2017

great

By zhao m

Nov 1, 2016

good.

By Marco A P N

Jun 1, 2016

Great

By Yusuf S

May 19, 2016

great

By Sameeksha S

May 12, 2021

good

By Souvik P

Aug 5, 2020

good

By Pitak P

Oct 4, 2019

Good

By Razib A K

Dec 18, 2018

good

By Ganapathi N K

Apr 30, 2018

Nice

By Jay B

Aug 15, 2017

good

By Saurabh G

Apr 13, 2017

nice

By Larry G

Feb 7, 2017

Nice

By 刘治

Jul 17, 2016

good

By Prakash M S G C

May 24, 2016

Good

By 朱荣荣

Mar 9, 2016

good

By 丁雪松

Jun 15, 2020

💯

By Amit K R

Nov 21, 2017

ok

By Ganesh P

Nov 28, 2017

V

By Wei W

Sep 11, 2017

C

By Balinda S

Dec 11, 2016

T

By 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."

By Ruggero B

Feb 29, 2016

My congratulations to all those people who worked to create this course although I have to pick up something I've found a bit annoying:

1- there were two video where the audio were nearly unintelligible

2- I would link the link proposed by the video to be possible to be clicked

3- Some exposition imperfection (even if they make these video more "real and human")

4- Since quiz are not so difficult to be evaluated automatically I found it a bit annoying to notice them locked by not-purchsing, even if I understand there have to be something which would make the customer to purchase.

I've found the swirl experience great although a bit annoying sometimes but I've no clue on how to possibly improve it so.

Keep up with this great work!

Bye

By Jamison C

Jul 4, 2018

You'll learn some cool things like K-means clustering and creating dendrograms, as well as dimension-reduction techniques. The assignments are very easy if you have basic familiarity with R's base plotting system and the "ggplot2" package. I will say I'm very happy with this course in the overviews of R's major plotting systems (though no "ggvis" package), as well as working with color palettes. However, I wish there was more hands-on or peer-graded practice with K-means, heatmaps, dendrograms, and dimension reduction techniques like Singular Value Decomposition (SVD). If these are new to you (they were to me!), you'll certainly walk away from the course more knowledgeable.