Lots of material in a short amount of time.

The course is OK, but I'd rather learned app development with Python or Java.

Updated and entertaining!!

Good range of topics - updates to content would be useful --> eg http://jupyter.org

The class was OK. This was the topic I was most excited to get to in the specialization, so I started playing around with Shiny, Slidify, and other tools ahead of time. Because I had gained some basic familiarity with those packages, this class was a bit of a letdown. I was happy with the week on making R packages. That was all new and fascinating. If you're looking for something more advanced than a very basic overview, you may want to look elsewhere.

This module is not the same level as the previous ones, giving a superficial view of several package but not on any deepens. The application development was the positive part of the module.

This course is clearly not the most interesting of the specialization regarding math/theoretical knowledge, but it helps you get to know a nice way of presenting your results.

Not bad. But I feel that this topic is more for those who likes to develop web products.

From this course I took away how to make shiny apps.

This is very useful and fun to do.

There was much more material covered in the course, but that was not tested via the assignments.

That means that passing the course does not ensure that you actually master these subjects as well (one example is creating and publishing a new R package).

This course can be updated:

1) add swirl lessons.

2) add more video materials

A bit fast paced as they teach so many things in a single week. But the stuff they teach is surely great and makes you appreciate the beauty of R

The course is a great survey of tools for sharing data analyses with R. Unfortunately, the content is already a little out-of-date because the technology is always developing.

Interesting but I had to learn most of the things here by my own

A disappointing end to the pre-capstone lectures, taking the foot off the machine learning gas from course 8 with a detour back to tools and yet another Rmarkdown lecture. This basically covers building shiny apps (needed for the capstone), leaflet (maps), making presentations in RStudio - then gets lost in R Packages and Swirlify which are not very useful here. Some of this is needed in the capstone, but this course can be compressed and combined with earlier courses and make room here for something more substantial at this late stage in the specialization.

The syllabus is very unbalanced through the weeks. For example the week 01 could be split into two weeks . The week 04, in my view, is unnecessary.

seemed like a filler, nit are why this was included

Would like to use the time to learn more machine learning/predictive technique, etc.

I am a developer and know how to create advanced websites. This course has less use for me as in my day job i create sophisticated websites

This was a fun course, but not particularly useful.

peer reviewers are spastic mouth breathing idiots.

I didn't learn many data science concepts in this class

To be honest I don't think this is worth the money.

This review does not reflect the course content. The new Coursera UI makes it impossible to download transcripts or slides of the videos. Without these features, following the lectures is significantly more difficult, and I can't rate this course any higher than 1 star. I would rate it zero stars if that was possible.