Chevron Left
Back to Developing Data Products

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

4.6
1,823 ratings
344 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

Filter by:

226 - 250 of 343 Reviews for Developing Data Products

By Jeffrey M H

Jun 16, 2019

This course provided a great number of methods used to deliver a data project.

By jessica c f

Jun 16, 2019

This is the ninth course of a series of nine courses. The creation of the apps and the didactics is very good, I just needed to do the first course of the series to get to work better the fundamentals, since this course is a bit advanced.

I loved the experience and everything I learned, I would say it is well worth it!

By Ingrid T

Jul 11, 2019

Best class of the specialization. The assignments were actually instructive.

By Nino P

May 24, 2019

In terms of practical data science in R, this is the most usefull course in the specialization. Here you can learn a lot about very important R packages such as shiny, MarkDown, leaflet,... Highly recommendable and very, very important.

By Subramanyan K P

May 27, 2019

good course

By Luis M M R

Jun 05, 2019

very good

By Brayan A A X

Jul 17, 2019

Extraordinario Curso

By Diego C

Jul 16, 2019

Excellent course!

By Don M

Jul 17, 2019

This is an excellent course. It's not as hard as the last three in the sequence but there is plenty to experiment with, and I was very pleased to see that we learned how to build packages, methods, and classes along the way, created an app, and even delved into building our own Swirl tutorials. While not strictly part of creating a data product, those are great things to have on the resume. I was pleased to see the capabilities of Plotly and will certainly use that. As with all of these courses, you must pay close attention to the marking rubric to get full marks. Onward to the Capstone!

By Shubham K

Aug 01, 2019

It is a good course provide neccessary stuffs for data science.

You can skip week 4 as it is done previously in other courses . Small and good course.

By Rodrigo F L L

Aug 07, 2019

Excelent introduction to the topic and really fun to use!

By Khalid S A

Aug 18, 2019

excellent Course

By Andreas P

Aug 27, 2019

Thank you very much.

By Rok B

Aug 28, 2019

It is a very good course. There's quite some work, but the content isn't hard. Make sure you update your RStudio for all the features to work correctly

By Muhammad Z H

Sep 15, 2019

learning alot

By Charbel L

Sep 14, 2019

Very good. Basic but very diverse

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 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 Alfredo A N

Apr 08, 2019

Interesting course but some topics don't have motivation

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.

By Nikhil T

Feb 19, 2018

should have been more indepth

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 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.