Kursteilnehmer-Bewertung und -Feedback für Developing Data Products von Johns Hopkins University

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.

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.

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.

Good course but I think it can be better.

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.

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

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

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

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.

Really useful and practical curse.

Very interesting, lots of useful structure

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.

Covered really useful tools like shiny and slidify.

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

Good and useful module.

Good intro to Shiny and Plotly

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

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

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.

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!

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.

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

Well done!

great course

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

should have been more indepth