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
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.
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.
Jun 22, 2016
A very straightforward course on how to build fast and useful applications fora broader audience.
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.
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!
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.
Nov 20, 2017
The course is simple yet useful. With very little knowledge about web development you learn to do some cool stuff.
By Paul A•
Nov 13, 2016
Great course, just like the others in the certification.
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 Angel S•
Jan 07, 2016
By Jason M C•
May 05, 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 Robert W S•
Dec 16, 2016
Great introduction to interactive plotting, mapping, and shiny. Deeper examples would be helpful.
By Fernando S e S•
Aug 22, 2016
The skills taught in this course are fantastic and I'm sure using them will blow my colleagues' minds away. However, I must say that the lectures on Rcharts and other interactive plot builders sound kinda sloppy, poorly prepared. I know the documentation for those packages is bad and it takes effort to figure out what they do, but that is precisely why a well-prepared lecture would be so useful. I would also talk about license, since we have been dealing with packages that are completely open for use, but these have some restrictions.
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.
By David L•
Apr 12, 2016