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

Excercises are very good. But I believe lecture could be more interesting and easily taught.

It misses an assignment which will allow to practice the clustering skills.

It focus too much on the tools and a little bit on the analysis

faced many technical difficluties in pratcice exerices in swirl

There seems to be some outdated info in several tests.

First two weeks are too repetitive with other courses

pretty brief and basic. no assessment on clustering.

Interesting stuff, but not a lot of detail

It's necessary for more examples!!!!

Absolutely No technical help, like insane amounts of homework for each week. People have jobs and businesses to run. Incredibly short duration. Like literally this should have been spread out several more weeks. I would have dropped the class but I can’t. It’s so difficult to get i to the first set of practice assignments and these several sets. Honestly, I am literally getting no help on it and probably won’t pass because I am missing the deadline. I finished 5 coursera courses working on them for 24 none stop. I’ve literally been at this class all day. Besides all the insane amounts of assignments there’s tons of videos to watch and uploads to do. Go buy some books or take another class unless you are unemployed or have nothing better to do.

More than Exploratory data analysis, the course is only focused on how to make graphs in R. That is actually fine, but the name of the course is not suited to the content. In addition, the lectures were too boring. The lack of pedagogy is stunning. The most useful part of the course was the swirl exercises that were the same examples shown in the lectures. That is why it seems that watching video lectures is an incredible waste of time.

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.

Clustering topic is covered superficially, too much time spend on employing ggplot graphs, not very useful since making graphs is straightforward on other software, like excel, once you aggregate datasets correctly. I had not found it very enriching as a course. I would merge this class within R-Programming section and call it Part 2 rather than categorizing into "Exploratory Data Analysis".

Some parts of material is good quality, but some is bad - also some show bad practices in R. Extensively use swirl as assignments over self work. It is better to go through good tutorial over R base plotting system and ggplot2.

The final project did not require use of the material in the course beyond the first week and a half. I did not take any quizzes or otherwise have my knowledge tested on the material in the second half of the course.

Course content and assignments were difficult to follow. Loads of statistical content along with high-level R content means it was probably the toughest of the 4 I have taken so far in the Coursetrack.

some swirl tests (4,5) don't work because of parameter method in qplot function. This parameter is not realy existed in this function now

S​VD should be better explained. I found diffucilut to understand. Some backgroeund matrices and it's operations should be explained.

Quite repetitious in covering basic graphing, and very shallow in regards of clustering, SVD and PCA.

Very unprofessional, compared to other courses. It wasn't well organized.

About plotting in R. Not about generating real insights from EDA.

Too weak regarding the clustering methods, very disappointed.

didnt get much useful-- a whole "course" on plotting? meh.

Not upto expectations

too superficial