The course introduces many good packages and skillsets, but doesn't really instruct on their use. The work typically requires extra outside learning to complete. The R courses in the data track have typically been much better than this specific instance, but it is required to complete that track.

Not bad, but as a number of other reviewers have said, the course material is out of date. There are a couple of places where the packages discussed are no longer available, the functions taught are deprecated, or where there's additional installation steps required for the newest versions.

The course is good to learn more R commands but only in the last week there is a practical assignment. I think if all weeks could have practical assignments this course would be excellent. In this assignment we don't use all the commands that we learnt. So, this course has a lot to improve.

I had a very interesting experience in the course. Thanks to all the help from the discussion forum and data science communities such as StackOverflow . They have the best resources to learn.

The assignments were a bit difficult to understand but once understood, it was quiet easy to solve.

The course is good but should update its links and go deep into the regex syntax. Moreover, the tasks of the assignment was not difficult. However, it was not easy to understand the tasks as they were not well explained/written. Overall the course is good and I recommend it.

There is a huge gap between what is touched on in the lectures and the project. The upside is that it shines light on what the student should do further research and study on. The downside is it almost becomes unwise and a waste of time to continue with Coursera.

Learned some very useful skills, but I found that some of the weeks moved too quickly without sufficiently explaining the background information required (as someone without a data science background) with abstract concepts that were not grounded in application.

the assignment project was hard and really not enough instruction was given and it was a machine learning data set which made it very hard :) i mean we hadnt seen anythin similar to that during courses :) fix that and change project assignment for final week

The course give very broad overviews in the lectures, then drops very difficult questions in the quiz and assiagnments. It is good to push a little and make you dig for solutions on the internet, but the jump in difficulty is too far to make it worthwhile.

The course is good, but it doesn't really offer all the tools required to pass the exams. I had to take extra courses in other place in order to pass. In addition, the exams some times become a bit too subjective of what the classmates want to grade you.

Gave good lectures on the methods used to get and clean data. The main downside was that the data and methods used was often outdated, and newer methods now exist. Still gives a good understanding of what you should aim for, and how to achieve it.

This course provides a lot of methods and strategies about reading data, manipulating data. But I think some important issues in the real world are not discussed enough here, like how to treat missing values, how to deal with messy format data.

Had a lot of trouble accessing and downloading datasets from the internet despite I were using the same source codes. Beside teaching how to download data from internet, it would be great if datasets were also included in the course content.

You will learn valuable tools, techniques and concepts but be prepared to feel overwhelmed (if you have no computer science background whatsoever) by quizzes and the assignment which require you to do research stuff outside of this course.

Quite disappointed at 'Getting data' part because of lack of explanation(I only had to learn extra sources to understand) but satisfied with 'Cleaning data' part. It would have been more useful if course described how to use GitHub, at the

The content of the course is good, but it seems abandoned - some links are outdated or don't work. I think it would be a much better experience for students if these first courses in the specialization got more love from the instructors.

The lecture material was high level, and didn't seem to be a good preparation for the quizzes.

The description for the final project was not very detailed, and the grading rubric likewise was not very specific for peer review.

I thought the course project grading was supposed to focus on what we learned in class, not almost entirely on creating readme and codebook files. Also, the explanation of what was expected for the project was NOT CLEAR,

The content of the course is too general, with too brief introduction of some commands in the lecture notes(slides), I don't think it would be very helpful for the students to deal with some real complicated problems.

brought up some good concept inside, like "tidy data", but not in detail, how to grab data from different source shouldn't be difficult. should have more focus on talking about data.table, "tidy data" principles etc.

Hi all,

Course provides interesting insight to getting and cleaning data. However, the course misses practical examples (not only showing the code in the slides, but also presenting how it works in R or RStudio).

This course was too hard for me compared to the first two in the program. Not sure whether it is because of my limited background in the subject area, or because of the abrupt shift in level from course 2 to 3.

This course is of lower quality than the preceding courses. The final assignment instructions are not clear. The forums helped but I have the impression that they are not really followed by the mentors.

Sometimes difficult to follow, and the content isn't really relevant to what I want to learn, considering I am not particularly interested in professional data science. Took about 2 hours to finish.

the course is good in terms of the knowledge but it is very unstructured. A lot of topics are treted just superficialy and the activities do not address the content of que lectures completelly.