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

4.7
4,573 ratings
653 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 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.

Y

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!

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1 - 25 of 639 Reviews for Exploratory Data Analysis

By Rok B

May 15, 2019

This course is basically plotting with R and clustering/dimensionality reduction. There's is not enough emphasis on the later in my opinion. The final assignment focuses only on plotting, which is a shame.

By Daniel H

May 13, 2019

Provides a solid overview of the base plotting system and a discussion (better elsewhere) of others. Introduces some higher level exploratory methods, without much information on either the theory or application (simply walks through the recipe). Assessments do not match the lecture material, so the credential is essentially meaningless. Read the associated book, watch the video lectures if you'd like. Don't bother with paying for the certificate.

By Paul R

Mar 12, 2019

This course covers plotting (base, lattice, ggplot) then takes a confusing tour into heavy topics of clustering and dimension reduction, then flips back to coloring in charts. The order of the lectures is confusing and PCA/SVD needs more background, clearer explanation and treatment (gets covered a bit more later under regression). Assignments are good and swirl courses helped solidify the lectures.

By Faben W

Feb 04, 2019

This lesson could have been significantly improved if there was at least one assignment on clustering/dimensional reduction. Those are probably the hardest concepts thought thus far, so it would have been extremely useful to have at least one challenge to work through.

By Dale O J

Oct 16, 2018

This has been a challenging course for me, for whatever reasons. I have devoted a great deal of time in reading Dr. Peng's books as well as reviewing work product of other students to get a better grasp of the logic and methodology. I have enjoyed this course more than any of the preceding courses. And, the struggle I believe will be worth the effort and facilitate my completion of the data science specialization program.

By Roman

Aug 30, 2018

Cons:

# Too much focus on hopelessly outdated R functions.

# Lectures are mostly powerpoint karaoke along the lines of "You can do that thing. And you can also do that other thing. And also you do this third thing" without much real-world application.

# ggplot2 is the only modern viz package that gets mentioned

Pros:

# The swirl exercises are great (but very buggy on Mac)

By JM

Jul 11, 2018

Once it got to the clustering section the lessons were inscrutable. Extremely difficult to understand and not explained well.

By Dilyan D

Feb 12, 2018

This is the worst of the Data Science courses so far (they've all been pretty good up to this point).

It's called Exploratory Data Analysis, but is actually all about the graphics systems in R. And it does a botched job on those as well.

All quizzes and assignments are about the graphics systems. The only portion of the course that deviates from that is Week 3 (for which there is no quiz or project) where we "learn" about clustering and dimension reduction. However, that material is presented really poorly: not enough depth for someone who is already familiar with the subject matter; and not nearly well enough explained for newbies.

On the graphics side, none of the systems is explored in great depth. The lattice system is essentially just mentioned in passing.

To cap it all off, the brief for the last assignment is really ambiguous, which often causes perfectly valid work to be graded poorly by peers. (Just look at the forums, if you need proof.)

By Luca R

Jun 10, 2017

The videos were merely repeating the content from swirl, with absolutely no added values.

By Beverly A

Sep 20, 2016

When it comes down to it, there's simply not the support to assist a student that has a really hard problem, "hacker mentality" seems to equate to "figure it out on your own cuz nobody's going to help you". If things do not work perfectly for you then you are likely never to be able to finish because your "peers" don't know any better either. The way this class is set up makes me angry every time I have to deal with it. I would probably be just as well served doing just the swirl() exercises. I would quit if I hadn't paid all the way through in advance. I can't believe this is the type of school John Hopkins is to produce a course of this quality, but I guess I have to.

By Sergey K

May 10, 2016

This course mostly about how to use plotting libraries in R.

By Anang S A

Jul 16, 2019

this course is more about creating chart for EDA, need more material for reading/interpreting the charts

By Alex B

Jul 13, 2019

This series has been life changing for me. Thank you.

By José S C S

Jul 07, 2019

This course teaches how to use three different plotting systems in R. Given the dominance of the tidyverse/ggplot2 paradigm, I really appreciate the opportunity to learn the base plotting system and the lattice plot system.

By Elimane N

Jul 03, 2019

That's a great and very usefull course!

By Marta R

Jun 28, 2019

Really good course, with amazing videos and examples. I have learned a lot and I think the projects were really interesting.

By Maria A P d S

Jun 24, 2019

Great content!

By Amy B P

Jun 18, 2019

Very well formed course. Enjoyed the course and projects.

By Diego A Q

Jun 18, 2019

Great course, it teaches you a lot about how to create plots, charts and other tools using R code. This course is focused on "get to know your data" by using all this tools during a research process. It is like the previous step you have to do before going into any analytics.

By Jean-Philippe M

Jun 16, 2019

More practical exercises using ggplot2 and clustering would be beneficial. Maybe need to be a 8 weeks module.

By William B B

Jun 12, 2019

Excellent course and applicable in my work right now

By Santi M

Jun 12, 2019

Good course

By Jamie R

Jun 07, 2019

Just an extended course on using R. There was little strategies for Exploratory Data Analysis, infact the example jumped from a high level view of the data to then start looking at individual counties. There are multiple tools in the market that will deliver in a better and faster way for exploratory data analysis. This course should be more targeted at developing a skill set that is tool agnostic.

By Eric J S

May 29, 2019

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

By Jorge B S

May 28, 2019

Very useful course with interesting contents.