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

4.6
1,868 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

SS

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.

RS

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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251 - 275 of 348 Reviews for Developing Data Products

By Federico G

Sep 09, 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 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 Robert W

Dec 16, 2015

In general, an excellent course, taught by competent professors. I believe that in the main this course does very well in achieving its objective of knowledge transfer. However, having experienced it, there were parts where the professor was demonstrating a topic using a video presentation showing him operate a process or screen sequence on his computer. These aspects, like virtually all the material on this course, are of a technical nature and contain many important details. As such, to help complete and re-enforce their learning, students require something like a sequence of slides that they can print out and retain for revision and future reference. In certain parts, the provision of the printable screenshots in the form of slides was absent.

An important theme of the course and Data Science in general is "Reproducible Research". What I'm arguing for here is, "Reproducible Learning Materials" covering a complete course, not only parts of it. Admittedly, it was only a very small proportion of the course that suffers from this defect. But I would not like it to become the norm in the future. As a suggestion, it could be possible to author a lecture using HTML so as to combine the verbatum lecture text with every slide/screenshot image embedded in its right position within the lecture.

I notice Coursera courses have also moved away from the weekly lists of individual lectures together with their links to .txt, .mp4, etc. files. The new presentation keeps you submerged within the flow within each week's series of lectures. One has to 'click out' in order to watch your progress and then re-enter the lectures at a resumption point. I prefer the previous navigation structure in order to access lectures and materials. Printed learning materials are also important for me, in addition to the video lectures. The latter are of course vital as the medium for the initial exposure of the material.

By Alessio B

Dec 07, 2015

Taken this course in its old fashion style. Now reviewing the new design was a little bit displacing, but I ascribe this to the fact I've done all the specialization courses in the old design.

However the structure of the course is quite good. Some typos were reported, as well as a bug on the unanswered questions in quiz 3. Main worst point was the missing format of several text boxes. I would have appreciated paragraphs, bold and italic, some links, picture, not only raw plain text.

Overall review is nonetheless over the average.

By Geovanni H M

Oct 13, 2016

Good course but I think it can be better.

By Stefan K

Mar 21, 2017

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.

By Lucas

Jun 22, 2016

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

By VenusW

Jan 18, 2017

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

By Kristian G W

Feb 02, 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 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 David E L B

Jun 05, 2017

Really useful and practical curse.

By Erwin V

Apr 15, 2017

Very interesting, lots of useful structure

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 Bill K

Feb 10, 2016

Covered really useful tools like shiny and slidify.

By Marcus S

Feb 11, 2016

Interesting course showing some of the possibilities for visualising and publishing data.

By Yew C C

Apr 01, 2016

Good and useful module.

By Craig S

Mar 08, 2018

Good intro to Shiny and Plotly

By MD A

Jun 05, 2017

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

By 朱荣荣

May 08, 2016

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

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 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 PowLook

Dec 03, 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 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 Helmut D

Nov 24, 2016

great course

By Johan J

Dec 17, 2016

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