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

4.6
stars
2,144 ratings
400 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

SS
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.

RS
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 399 Reviews for Developing Data Products

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 shinyapps.io website for Shiny Apps and slidify research. Rigorous and rewarding when you put the work in.

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 21, 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.

By VIPULKUMAR P

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 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 Julian J

Feb 12, 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.

By MD A

Jun 5, 2017

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

By Rafael M

Jul 24, 2019

Excelente curso, 4 estrellas debido a que las instrucciones para revisión no son tan claras y los compañeros revisan de forma subjetiva.

By Johan J

Dec 16, 2016

Awesome course. Overview of all the things you need to become part of the data science community in terms of contributing and sharing.

By Jorge L

Apr 25, 2018

Excellent content. I was expecting more examples related to the industry, such as data products for data journals, telcos, etc.

By Federico G

Sep 9, 2018

Really good course, but for the time invested I think they could have gone a bit deeper in some areas. I enjoyed it though :)

By Simon

Nov 20, 2017

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

Well done!

By Erik P

Jun 26, 2017

Great overview of possibilities to interactively present your data!

The provided written material could be more in depth.

By Kristian G W

Feb 2, 2017

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

By Manuel E

Aug 29, 2019

Some parts could be dropped and devote that time to develop the content of others beyond scratching the surface.