Learner Reviews & Feedback for Developing Data Products by Johns Hopkins University

Good course but I think it can be better.

I think this one is the best from the Specialization as it is the most Practical one (more than Practical Machine Learning).

Can be taken without the others if you have basic experience in R and want to learn about cool R applications.

The reason I don't give full rating is for not having practical assignment every week. So there wasn't enough effort put into the course. Of course, we can do optional homework and make more applications, but assignments like these should be mandatory. There is no package building and no swirl course building - So why do we have week 3 and 4 at all? The quizzes are also laughable - no knowledge testing at all.

So although I liked this course from the Specialization the most, I still can't give full rating because of the mentioned issues.

A very straightforward course on how to build fast and useful applications fora broader audience.

Really Interesting class, interactive app/plot is so much fun. Great to be able to make creative stuff myself.

I really like the new version of this. It mixes a lot of tools, but most are useful. It was fun doing the assignments.

Really useful and practical curse.

Great course, I just missed some material on distributing data products as files or objects. Data Science environments are getting connected to traditional BI-environments more and more, now that organisations are getting more used to DS. So it is starting to be important to also deliver data products as files to the e.g. data warehouses, ArcGIS, or open data platforms. I know this is mentioned in Getting and Cleaning Data. But some further elaboration would be nice.

The lecture is not so fluent taught than other coursers in the specialization

This course gives a good introduction on how to develop an application.

It gives all the available tools in the field out for us to try and use them.

The course is enjoyable and not stressful. I find the assignment as meant to get us to do the project and not really there to fail us. It is difficult to fail to course as only the minimum is required.

The course is simple yet useful. With very little knowledge about web development you learn to do some cool stuff.

Well done!

Interesting topic, touching on many field. I believe it was quite informative as it applies on the previous modules knowledge into this one. However it didn't touch deeper on each topic.

I liked the course and found it informative, but wish there were more stuff on Shiny Widgets and Input/Output/Render topic. R Shiny tutorial is pretty good, but I was hoping more relevant info about those topics from this course.

This course lacked required information of help to get started. Now thanks to some posts by mentors i was able to successfully complete the Capstone project. Overall a very good experience!

Interesting course

Great course, just like the others in the certification.

Good course, but I felt it was a bit easy to get good marks on the assignments with a minimum effort assignment. Some of the ones I marked were very little to do with data-science.

Quite hard for a non-specialist but very useful

good and quite interactive

Compared to the other classes in the JHU Data Science specialization, this one is pretty laid back. It's useful information, and teaches a few nice tricks on how to present data analysis results.

Great introduction to interactive plotting, mapping, and shiny. Deeper examples would be helpful.

Would be nice to add some optional references, reading materials or videos covering "Creating Data Products with Python and Python stacks"

Excellent course. Got to learn Shiny Application in this.

I enjoyed the class, but was frustrated when it came time to get my Slidify to work on GitHub (since RPubs wasn't publishing them correctly.) I had to convert it to RPres (which was easy, thankfully) in order to get my project submitted in time.

Good learning experience. I'd love to see little more detail work assignments.

This was just a quick overview of different technologies out there to help creating various types of interactive graphics in R. But I would have preferred if at least one of those technologies were explored in more detail.