the instructor of this course, unlike the other 1, is quite unclear about what needed to be done. a lots of the packages of the course are not up to date.

more quiz and exercise would be highly beneficial

Mediocre presentation of tidy data, which is probably the most critical topic. Otherwise, its mostly just walking through what commands to use in R to load in various file formats.

This class needs more content, more explanation. It is clearly a very important aspect of Data Science, but the assignments were more complex than the given course content.

I learned a lot but my usually happy & grateful attitude was sorely challenged by the fact that so many facts in the videos and obvious course material was, well, wrong.

Out of date material. Many links broken. Some of the functions taught are sunset. Week 2 was too surface level to do anything useful. Weeks 3 and 4 better than week 2.

This course has a very interesting subject and a concise syllabus, but is very poorly prepared. I hope coursera will pass on the message to Johns Hopkins University!

Too difficult practical exercises with the theorical background given. I know that hackers skill should be used but it is too much assumption in the projects!!

It is somehow just an extension on R Programming course, with many unnecessary details that will be forgotten in a few days after the course.

The video is simple and good.

But the final project and some test are too hard based on material presented.

Also staff's support are not good.

The only interesting part was dplyr. The rest was too confusing, with lots of lists and no explanations.

The information in the lectures is very stale, which makes it extremely frustrating to learn from.

The course material needs update. There are code snippets that do not work.

This course could have been better. It was all textual and it got boring.

Final assignment is not well detailed, and may cause confusion.

This course lacks projects to implement the skills we learn.

The course was a bit mixed in terms of its quality.

Very poor instructions for assignments.

waste of time for software engineers

Too many things in one place

Worst class in this series.

too superficial

Lacking focus.

Boring.

2

R is really just the worst, and the instructors do not make it better. The code in this class is unreadable:

- too many one liners, because "it's faster to write", though harder for other people to read

- variables are named cryptic things like spIns or x, rather than names with meaning (eg, sprays.by.insect), again "because it's faster to type"

- way too many cases of "there is more than one way to do it", which just makes things confusing because the other ways tend not to be equivalent

What I'm most concerned about is that I've seen lots of poorly written code in many different languages: Java, C++, C, Python, Perl, and now R. But I've also seen really well-written code in all the languages *but* R, I have yet to see any code in R that is flexible, maintainable, and clear. Which leads me to think that no such code exists, or it's so rare that it doesn't matter. It is clear to me that if I am to do data analysis, then I will need a different set of tools; but because this specialization is taught entirely around R (the lectures are about R, not about higher-level concepts), then this specialization is not useful to me.