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

4.7
stars
5,859 ratings
850 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

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!

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.

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626 - 650 of 819 Reviews for Exploratory Data Analysis

By Raviprakash R S

Feb 13, 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 ....

By Luke S

Oct 31, 2019

Good introduction. The swirl exercises kind of reproduce the lectures though- felt like it might not have been the most efficient use of time to go over the exact same example again.

By Ng B L

Mar 9, 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.

By 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.

By Diego P G

Jan 7, 2018

It's a very good course. Week 3 was a little bit more challenging than expected, as well as assignment 2, but you get a good idea of how to use all the different plotting systems

By Christian B

Dec 11, 2016

The course is interesting and the content is relevant. I do think that there are some issues with project 2 though. I did provide feedback on that to the course administrators.

By Hernan D S P

Mar 6, 2018

I learned a lot on this course, it helped me to understand and identify some of the situations I experience at work. Totally recommended if you want to apply it right away.

By Terry L J

Oct 18, 2018

Seems this would type of course in an online learning MOOC would be better if it was more direct hands on "how to" and less focused on explanatory fluff (academic style) .

By Igor T

Jan 30, 2017

Good introduction to patterns recognition. I found principal components analysis technique very useful. It would be great to provide more lectures about this topic.

By Carlos G W

Sep 6, 2020

I enjoyed the course and learned a good deal. However, the level of challenge of the projects is much higher than the scant explanation provided by Dr. Peng.

By DESIREE P

Apr 19, 2021

We learn very useful things. However, there is little emphasis on the statistical part (singular value decomposition) which I think deserved more exercises.

By Diego T B

Nov 17, 2017

Interesting. But I would prefer the differences between comparison plots. What do they are useful and why is it better to plot with bars rather than lines.

By Robert W S

Feb 14, 2016

A quiz or project question on k-means clustering or PCA would be nice. Overall the course provided solid coverage of the three main plotting systems in R.

By Guillaume S

Jun 8, 2018

Interesting course to know plotting systems and to have a first view on clustering and dimensions reduction. This part should be however more developed !

By Hyun J K

Apr 17, 2018

Great lecture. I hope there were more assignments. (1 per a week maybe).

I learned many statistical concepts and rcodes by taking this course.

Thank you:)

By Hank C

Sep 13, 2020

Course material, lectures, exercises are excellent.

There was not enough theory, and there was too much specific to R and graphing packages covered.

By Robin S

Feb 28, 2017

The course was fantastic. It was very challenging. I could do with some additional opportunities for exploratory analysis to reinforce some concepts.

By Steven C

Mar 15, 2017

Good course on plotting libraries and useful plots in R. Wished there was more coverage of ggplot and less on lattice, but overall a useful course.

By Ramakumar A

Jul 3, 2020

though presentation was good ,felt it should have been better in small sessions , lost interest half way through , continued later to complete

By Ashutosh K S

Feb 7, 2017

It delves into many important topics. I would advice to explore the topics in much more depth on your own. Overall a good breadth of topics.

By Bijan S

Jan 30, 2016

The course is useful with a lot of learning.

The second half needs more of improvement, I think the pace is quite fast compared to others.

By Piyush D

May 15, 2019

Awesome course ! It reaches you the crux of exploration of data . Although the SVD section could have been more thorough and detailed.

By Marc T

Mar 31, 2019

Great introduction! I am eagerly awaiting the opportunity to apply clustering and dimension reduction on real data in future courses.

By Andres U

Feb 21, 2017

Really helpful. I really enjoyed getting familiar with plotting systems and also increasing my abilities dealing with data frames

By Tony W

Jun 21, 2016

Very interesting and insightful course. I enjoyed it.

Assignment was okay, could have provided more challenge and depth though.