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

2,208 ratings
410 reviews

About the Course

A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience....

Top reviews

Mar 3, 2016

This is a great introduction to some of the many ways to present your data. It's probably the easiest course in the specialisation but shows off an impressive array of widgets and gadgets.

Nov 18, 2018

This course was amazing, it could definetly be more deep in each of the subjects, but gives you so much practice in tools that are very useful in the day by day of a data scientist

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301 - 325 of 410 Reviews for Developing Data Products

By Winnie M

Dec 17, 2020

The course content was great, but from the peer assignments I reviewed, hate to say that the percentage of plagiarism in other's assignment is unbelievably high, some didn't even check the code they copied and included link to source code in their submitted work. This will unavoidably degrade and depreciate certificates issued by Coursera and Johns Hopkins University. Please review the existing evaluation machanism so it is fair to the students who put time and effort in completing their assignments.

By Gerrit V

Jul 31, 2017

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.

By Ariel M

Oct 30, 2016

This is an excellent course that will teach you plethora of new things! The only gotcha is that things move too fast in the world of Data Science. Some of the topics and code might not work exactly as shown when you take the course, and many things will change. It's up to the student to make-up for the missing pieces, but I guess that is the only caveat when you work in a still-evolving field.


Dec 3, 2015

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.

By Greg A

Feb 22, 2018

Analysis is useless if done for its own sake. Once you have found something interesting the challenge is finding engaging ways to share your insights. This course is a bit scattered since it covers so many different ways of publishing and presenting data, but it is a really nice survey of what is out there.

By Joana P

May 10, 2018

I lost myself a little bit, because the materials were a little over the place, we did shiny in the first week lectures and only in the last week the project was about it.

Same with leaflet, I think ti could be structured differently.

But i find them very relevant so that is the reason why I rated it with 4.

By Kim K

Aug 8, 2018

You will need to be familiar with the subject in order to keep up with the assignments or put in a large amount of time to learn. The forums are helpful and other students are great. Visit the website for Shiny Apps and slidify research. Rigorous and rewarding when you put the work in.

By Justas M

Sep 2, 2021

T​he course is on the easy side of the DS specialization. But I believe its very usefull especially in terms of communication of research results. The Rmarkdown part could have been more thorough as well as included how to work with LaTeX, non the less, a great course overall.

By Raul M

Feb 14, 2019

It is a nice class but many of the topic were already covered on previous class of the specialization. If this is the only class you will take, it is okay but it you are taking the whole specialization this is like doing few (not all) things again

By Matthew C

Jan 5, 2018

Good course, but it was a little light on content. Probably the easiest month of the specialization aside from the first one. The assignments were too easy too pass. Good intro to some very interesting packages (leaflet, shiny, google vis) though.

By Erika G

Aug 22, 2016

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.

By Yuriy V

Mar 10, 2016

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.

By Rishabh J

Aug 23, 2017

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.

By Daniel J R

Feb 12, 2019

This class includes a lot of introductory content an a good measure of hands-on practice. It also provides a great amount of resources and reference material for the learners to expand their knowledge further.


Apr 10, 2017

Nice course and good classmates. It's very focus oriented and gives very good idea of Shiny, Rmarkdown, plotly and how to publish documents on github, Rpubs and other online sites. I learnt power of R

By Jason M C

May 4, 2016

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.

By Sreeja R

Feb 14, 2018

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!

By Ramy H

Jan 28, 2018

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.

By Jeffrey E T

Mar 28, 2016

Good overview of available tools. Lack of practice exercises makes preparing for quizzes difficult. However, the course project does a good job to get your feet wet with Shiny Apps.

By Andrew V

May 31, 2017

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.

By Iain L

Dec 16, 2020

Interesting, practical course. Gives a broad overview of R Markdown, Shiny, interactive plots (Gvis/Plotly), incorporating maps via Leaflet and creating your own training courses.

By Luiz E B J

Mar 7, 2020

The content is very interesting, brings a lot of knowledge to develop good presentation and analysis. Could me more focused in just one option to go further an deep. Recomend!

By Johnny C

Nov 21, 2018

I have learned a lot. The course is simple and very useful. Maybe the assignments should improve because the directions are a bit vague, but in general I liked it.

By Mohammed H

Sep 9, 2021

I have learned a lot of things after completing this course, like how to build a shiny app by myself and how to use leaflets and plot a graph. Thanks, coursera

By Julian J

Feb 13, 2016

Although all coursera courses are same, the peer assessment is very useful for this course. It's very helpful to see products made by other people.