It's not really acceptable to make students google new things in order to pass the quizzes. Quizzes should asses knowledge gained through the reading and lectures, not our ability to learn via Google.

The course is good but the only problem is there is no explanation on how to solve different problems. there should be a live example of problems so people who have some trouble can get through

Swirl practice in for Getting and Cleaning Data in this class is terrible. Most of my code working fine in R and R studio but Swirl would tell me "That's not the answer I'm looking for, try again" Then I type "skip()" Swirl will give me the exact answers that I just typed earlier.

There is a huge disconnect with the material and the HAR dataset exercise. I would suggest that there is some help with smaller exercises to help explain how to complete it. Yes, I know you're supposed to do research to help figure out problems, and I have. As a matter of fact, I have taken other courses on data wrangling to be able to figure out this problem. Merging two datasets makes this problem very confusing. Why can't you help guide students through a similar problem, instead of throwing to the fire?

The material in this course is very condensed. Data Table lecture was very much a copy of someone else' information on the web and was so terse, I would imagine even people from programming backgrounds had had to listen to it many times just to understand what was going . Expect to put in good 8-10 hours a week into this course if you want to become proficient in course' material.

There's too much of a jump from the theory to the practice. I had a difficult time understanding what was being asked of me.

Prepare to not actually learn anything, rather you're going to go on a journey through google to try and find obscure ways to install packages onto your Windows computer. Whether it be packages to read Excel files, SQL files, API's and more, you'll rarely have the time or patience to put any of this to practice because you'll struggle to just get packages installed.

For the record, I gave the previous two courses in the specialty a good rating, but this is clearly a low effort showing. It's a shame because I really think this might be the most applicable and useful content in the course.

The contents of the course are extremely useful. BUT if your programming experience is the two previous courses I think it's a very difficult course, since there are some issues that are outdated or not explained in detail or not explained at all.To do most of the quizzes it's not enough to repeat and listen to the videos. In many cases it's necessary to read a lot of documentation, search and apply new functions that are not explained in the videos, search forums and realize that the packages not work in the same way for the new versions of R, that some functions don't work correctly with RStudio but they do with RGUI, in other cases must be added a certain argument that was not explained in the videos (eg: for windows "binary" mode in the function download.file, which I still have no idea what it means).In short, a lot of things that make certain parts of the evaluations do not measure if you really learned what was taught in the course, but what has been your ability to handle yourself in a self-taught way. Which is a necessary skill in general (not only in R and Data Science) but that isn't what I expect this course teaches me.All this search is more difficult especially for Spanish-speaking people because it isn't enough to have a level in the language between intermediate and advanced, rely on Google Translator and rewind the video many times; to really understand, you have to have some technical language management.

Week 1 can be more detailed as per what you expect in the quiz. The main idea of following a course is that we get all material about that topic together at one place. But here we are given just names of topics and told to research & read about them ourselves.

Modules 1 and 2 are horrible, so much to cover (several types of files) and so little actual information from the course. Yet, quizzes demand one knows every detail of each file type. Scripts and links are not available from the slides, although I did manage to find a repository with all scripts of the course (after much trouble). Why not make it available from the main page of the course? Anyways, some links were broken and could not be used to follow classes. Classes themselves are very dull, no interaction whatsoever.

Horrible Assignment. So vague. So much puzzling to do. Students cannot waste their time in attempting to understand the loose vague assignment that was made. ASsignment took me 4-5 hrs of pondering and referrinf to online material just to freaking understand partially what the hell is expected of me to do. I hate this part of CoursERA IT is ugly!

Loved the structure of the course. Learned a lot. The course project seemed a little funky , especially creating the codebook for an already existing set of data but was a useful teaching aid.

This course is amazing! I have spent the majority of my time in R merely doing analytics. This course taught me the tools needed to go out and grab the data that I need for those analytics.

More challenging than the "R Programming" course. The instructions for the final project were a little vague, but I think maybe this was intentional to promote discussion. Definitely give yourself plenty of time to complete the final project if you take this course. The principles of a tidy data set might seem like common sense, but in practice it's more challenging than you might think. I highly recommend taking this course even if you think you know what a tidy data set is.

this course is a hands of this specialization in R.

this hands use to play with a raw and tidy data in R.

Useful, the course is as much an introduction to R- part 2 as about cleaning data

EXCELENTE!!!

Just Amazing, one should take this course for betterment

Love quizzes and a course project. Working on them develops you as a data scientist

Fantastic course!

Better than R Programming. Still very hard, but worthwhile.

This course is great. The tactical skills in R you really need on the job.

The course project was really helpfull in understanding how the data is presented to datascientists. Now to get the jist of the data we have to go through assembling, cleaning and cutting the data.. It was a challenged to understand the data.. assembling the data was a lot of fun in R..

It was pretty hard for someone like me who has a weakness in programming but it provided sufficient exposure and tasks for me to learn within my capabilities. I did enjoy its challenges.

Great course with detailed exercise about what's most important task in Data Science, "Cleaning Data"