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

1,863 ratings
349 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 04, 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 19, 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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76 - 100 of 348 Reviews for Developing Data Products

By jose r

Jul 05, 2018


By Henk S

Dec 06, 2015

This is my comment as a beta tester:

1) The changes to the lessons have changed the course for the better.

2) If you want to be factually right than the statement that Bootstrap is a style should be changed on a few places. Bootstrap itself is not a style, although it is used as a style guide for the development of products. Obviously this is not a big issue and people that delve into will find the facts easily.

Bootstrap is an HTML, CSS, and JS front-end framework with a strong support for themes which people also call styles. Many themes/styles are available to build responsive, mobile-first web sites. Bootstrap was created by a designer and a developer at Twitter in mid-2010 and was released to the public in August 2011. It has become one of the most popular front-end frameworks and open source projects in the world. Bootstrap has a few easy ways to quickly get started, each one appealing to a different skill level and use case.

By Juan E F N

Sep 01, 2017

Brian Caffo is the best! No one could explain better than him. Congratulations for this excellent course, you are my inspiration to become a better data scientist.

By Jay S

Sep 03, 2016

Excellent Course!


Sep 11, 2017

Very good for anyone wanting to get into the field of Data Science using R

By Paul G

Jan 16, 2016

very interesting and useful for R programmers

By Matthew W

Mar 31, 2016

Fun making some apps and presentations in the context of Data Science.

By Chigrinov S

Feb 07, 2016

For me the course was really interesting. Yes, all topics are not covered in deep details - but lecturers show what technologies exist and for what purposes. So if you're interested in some of them - it is up to you to discover more (useful links are included). Really nice and useful overview.

By jose m

Apr 27, 2016

Nice I like a lot, shiny nice tools. thanks to the teachers.

By Arnaud L

Nov 07, 2017

I loved it, very interesting and interactive

By Praveen S

Feb 11, 2018

awesome course

By Falko K C

Apr 03, 2016

Excellent course, that helped me finish my own student project.

By Pam M

May 19, 2016

Really enjoyed learning how to build a Shiny App, and see a lot of use for this in my work environment. The Slidify product was not as useful - after 3 months of working on the project, I moved from Slidify to RPres, and was able to complete the project in very little time.

By Viditya T

Mar 31, 2018

Awesome Course and content

By Harris P

Feb 16, 2017

Was a highly pleasurable and rewarding experience for me.

By Eufrásio A C J

Aug 05, 2017

Now, i'm analytical thinking

By Sathya K

May 16, 2017

Nice course

By Daryl V D

Jun 20, 2017


By Paul B

Jan 13, 2017

Filled with useful information about practical tools.

By E. M

Aug 09, 2017

Love it!

By Jean P L

Jun 21, 2018

One of the best

By ooi s m

Jun 11, 2017

interesting course covering plotly, r markdown and shiny. love it

By David P

Dec 18, 2015

The new platform is very versatile and easy to navigate. The page layout is much more clear. It is easy to navigate from course material to discussion boards.

I like the Quiz format, including expanding the number of choices for the multiple choice selections, but the grading was confusing. For Quiz 3, some questions came back with multiple "Well Done" comments, even when I had not selected the answer for which I was being praised. I also was told I made errors on the same question.... and this was after I answered the question (Question 2, on R generic functions) the exact same as I had answered it when I took the course earlier this year.

I was not a fan of not having to take a picture to submit work, so I am pleased that is no longer a requirement. I hope the typing pattern match is sufficient to affirm identity.

I have one comment on content specific to this class. Week 3 content lacks relevancy to the project and data products in general. I agree that knowledge of R packages, classes, and methods is an important part of understanding R. I am not sure where that fits in the Data Science curriculum as a whole, though. Maybe expanding the curriculum to include a second, more advanced R class, with a project to write our own methods, build an R package, or do something with yhat. That would assign relevant work to reinforce the lectures.

I would be happy to do further beta testing.


By Stefan S

Mar 04, 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.

By Alejandro B G

Feb 09, 2016

This Course was georgeous!!, i like the way of share my analisis, shiny rules!