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The content of the course is good, but it seems abandoned - some links are outdated or don't work.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.

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.

The professor Leak is not as gifted (in terms of teaching skills) as R. Peng. In some of the lectures he just reads what it's in the presentations but he does not go very deep into them.

Contents in first half weeks are very superficial, have low depth so that do not help me do some meaningful studies. But later ones are good for understanding the structure of data.

less hands-on exercises and this course covers too much topics without details. More like general intro to each tool and data sources. Swirl is still a great package for practice.

I liked it, but I had the impression it wasn't as prepared as the other courses. Especially with the course assignment I had a bit trouble to understand, what was wanted.

This is not a beginner's course, so a decent grasp of the R language is necessary. It is best to take this course after some stints with Data Camp, Swirl, or Code School.

Some lessons are pretty good, but there is a gap between the lessons and the assigments. You're evaluated in some aspects that are not very explained in the lessons.

Assiignments are out of date, some commands are deprecated. Also, the quizzes and projects require more in depth lessons or practice problems to complete. Thank you

Some of the lectures on loading data were very dry, but I guess that can't really be helped. Also the final course project's requirements were on the vague side.

I think this one is easier than the R programming but still, more exercises to practice would be good. Also, the assignment's explanation should be more simple.

It would be better if we had practice problems along the way. This course seemed to have a lot of commands that didn't stick until I put them into practice.

This is an important course, but many updates will be needed. There are only a few exercise task, there could be more. The swirl part of the course is good.

The best course so far (though that's not saying much). This course would be better as a follow up to an example workflow showing an end to end analysis.

Not on par with the course sets before it, could have been a bit more explaining as cleaning data is one of the most important parts of data science.

The content of the course is very interesting and useful. However, I found very challenging the difference between lectures and quizzes/assignements

The selection of topics is great. However, the course is too abstract. I think some of the materials deserve to be discussed more comprehensively.