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

Course gives thorough introduction to basic tools for exploratory data analysis, including visualisation, PCA and clustering. Good mix of lectures, practical in swirl and programming assignment. Swirl practice are mostly a repetition of the examples in the presentations, which is a bit of a pity...and I missed a programming assignment on cluster analysis/PCA

Very good. Great videos but perhaps the most learning was obtained through seing different apparoches taken during the peer review. The course could be even better if more smaller peer reviewed tasks where to be completed where extra points where rewarded for not just displaying correct data, but also visualising it more efficiently.

I'm glad for completing this course, it added a value for me.

I wish the videos about (SVD and PCA) in week 3 was more clear but it was difficult for understand and i feel lost , I think you need to update this videos to have more a satisfied materials.

Thanks for your effort and for what i have learned for this course

Week 3 content is difficult to understand without background knowledge in clustering and component analysis. Hope the instructor can provide some materials or web links for cluster and component analysis at the beginning of Week 3. Other weeks' contents are good and helpful!

Great practical course on exploring big datasets in R. The main part, plotting, is very clearly and thoroughly explained and framed. Only 'single value decomposition' and 'principal components analysis' was somewhat hard te grab and need a lot of extra research and study.

I really enjoyed this course. I was a good reminder of what analysts need to do when looking at a new dataset. Dr. Peng does a great job walking through the steps and there is enough information given to enable the student to effectively explore on their own.

The course was really good, thanks for that; however the part of single value decomposition and principal components analysis was not explained in a gradual fashion and even though I researched outside of the course I still have some confused concepts there.

Good, but the lack of assignment in week 3 seemed to screw up the UI, prompting me continually to do the Swirl exercises, which were non-compulsory (and, given I hadn't completed any of the other Swirl exercises, something I didn't want to take on.)

The only missing point I would say about this course is how to deal with skew data and/or outliers. Although it is not specific to "cleaning data", I think there is a good opportunity there to at least give some hints on this subject

The Swirl activities followed along with the lectures, which allowed us (as learners) to better understand core concepts. The lecture videos continue to end while the professor is still speaking, but this is not a major issue.

It was awesome to learn visualization. SVD and PCA part of the course could have been elaborated better, and a pilot project on that would have cleared the basic concept. As usual Prof. Roger is a engaging and amazing teacher.

The course was great, I'm not sure if I'd really consider using the base plotting package in reality as the plots are just too ugly, and the API is harder to learn. I think a stronger on ggplot would help to keep it relevant.

I enjoyed the course and learned important graphing concepts for R/RStudio. I just wish the assessments had been a little more rigorous, as it felt like I could have done better but still passed the projects anyway.

This is a very good course, at times it felt like the instruction was to do things mechanically without understanding the motivation. Perhaps this should come after or in conjunction with Statistical Inference

I love the course. However, the treatment of PCA, SVD, and colors seems to me very long and slow. Maybe a more direct and quick overview would be better. Even with that expection I really enjoy the course.

It was fun and interesting learning how to explore the data. For the final project I missed a assignment about clustering, PCA and SVD. It could be useful for a better understanding of the concepts.

The course on Exploratory Data Analysis was highly enjoyable. I used to do a lot of this sort of thing in my job, but now spend more of my time managing people. It is fun to get "hands-on" again.

Great in-depth content about techniques related to exploratory data analysis and implementation in R language using R Studio. Definitely recommend this course to any aspiring data scientist!

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 ....

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.

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.

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.

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

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.

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.