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

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?

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.

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.

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.

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.

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experience is the two previous courses I think it's a very difficult course, since there are some issues that are outdated or not explained in detail or not explained at all.To do most of the quizzes it's not enough to repeat and listen to the videos.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.

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!

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.

Easy, mostly instructive Course. The Assignments and quizzes are quite good, and illustrates the lessons very well.

See the videos for general presentation, but use the energy on the excersizes.

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.

This course is a nice introduction to the complex process of getting and cleaning data in R. It introduces you to some fundamental tools in the area, such as the dplyr and tidyr packages, and touches upon the most important aspects of data gathering and transforming. The final project is an interesting mix of technical challenges with a touch of intelligent practices in data handling and sharing. Whatever your level in R programming and data science, this course is an enjoyable hands-on experience.

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.

The course is very helpful when it comes to exploring commonly used R packages and learning certain best practices involved in data cleaning. I'd definitely recommend it to any data science enthusiasts. One area with slight scope for improvement could be the final project. The instructions are quite open to interpretation, which means that the final grade which you get via peer review is always going to be debatable. Other than that, I have no complaints whatsoever :)

This course is really a challenging and compulsory for any one who wants to be a data scientist or working in any sort of data. It teaches you how to make very palatable data-set fro ma messy data.

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.

Pros: I learned a ton about cleaning data, the challenges involved, and how to tackle new problems. The quizzes and projects throw you into the deep end, asking you to import some data set and report some features of it, and you often need to figure out what package to use and how to work with it on your own.

Cons: The videos in this course are basically useless. You get a superficial coverage of how to use some package without a lot of explanation on what each part does, and basically all of the examples are broken, because the data have been updated, the site has changed/no longer exists. The instructors very annoyingly bat away any responsibility in the forum by saying it would be too expensive to fix anything. Too expensive? This isn't a Micheal Bay movie, this is a guy talking over a powerpoint.

Thanks for this wonderful session on Getting and Cleaning Data. I would like to convey my sincere thanks to Professors Roger D. Pend, Brian D. Caffo and Jeff Leek and my fellow learners for their excellent help in completing the projet to generate Tidy dataset. I would like to name Mr. Luis Sandino for his help and effort in putting a help Guide for this assignment. I follwed it and got the assignment completed. The step by step procedure helped me and other fellow learnerrs to complete the assignment on time.

Though this course is over, still we have the doubt on the dimension of the tidy dataset, whether it is 180 by 68 or 180 by 88 as the total number of "mean' variables considered are varying. Request mentors or TAs to help us arrive at the correct dimension and help us understand the reason behind the same.

This course has witnessed the need for support from TAs and mentors. Their help and support was very valuable in understanding the subject.

Thanks to Coursera, my Professors, mentors and TAs of this course for their insight, guidance, support and effort.

Wishing Coursera and Professors all the best and Success.

The SWIRL component for learning the subject is the best and wish SWIRL support for all the heavy courses. Special thanks to those who made SWIRL course material possible for Data Scientisit's toolbox.

With Best Wishes,

S. Ramalakshmanan

This is my third course completed in the Data Science Specialization offered by Johns Hopkins. In all three, I feel the lectures, quizzes, and swirl exercises are easily accessible, and then the final project makes me feel like I am seeing R for the first time. One review of the course made a brilliant suggestions: go through the videos as quickly as you can, and then look at what will be asked of you in the final project. Then, go back through the videos and quizzes with a different set of eyes.

I feel like there is just so much to learn with R that sometimes you need a lens to help you focus on a subset of things that you absolutely will need, while getting a "taste" for all that R has to offer.

Overall, I am enjoying the courses, but the final projects are indeed a different kind of challenge.

Thank you for giving me opportunity to learn. These material (or this class) would have been super difficult, if it was taught through the same traditional channels based on my academical experiences. Yet, the materials were presented in such an amazing way that I wasn't taken over by the difficulty of the presented subjects, rather I was getting more focused to learn more and to be challenged. Thank you for letting me get 3 free online certificates. It means a lot to me and it has given me hope through this difficult time. I feel accomplished. It's a great feeling and it the best and the only gift that I have received and would probably receive this holiday.

I learned so much of R with this course. Thanks Johns Hopkins. Thanks Coursera.

The course final project was so challenging that made research R tools I did know they existed. Such as generating MD files from RMD markdown notebooks, so I could mix live code with text. That's how I produced my CodeBook.md. Then I learned that there are a bunch of libraries for pretty-printing tables. I discovered even more about dplyr. And also learned how to return multiple objects from a function.

You can really write papers with all these tools in R and getting expertise about knitr and pandoc.

Thank you Jeff and team for putting together such a quality course.

Before taking "Getting and Cleaning Data", I had no prior R programming experience aside from completing the R programming course in the data science specialization on Coursera. I found this course to be challenging and that it covered quite a bit of ground in terms of the "getting data" more so than the cleaning data. After completing this course, I feel like I learned quite a bit more R programming and the basic knowledge for obtaining data from a variety of sources/formats and cleaning it up to make it look nice and tidy. Overall, I rate this course very positively!

The course is great and useful. In my personal experience, this course were so important as R programming course, since on this course one get the essence of R and the hardest process when deal whith real cases. I could see that the videos has ensured about velocity and audibility; when I took it, it was difficult to heard and has a so high velocity.

I want contribute as beta tester, and will try to follow all the course, at my own pace giving feedback in thankful to you for the opportunity you gave me to learn free.