The assignment was tough.

A little hard to follow

Too much repetittion.

Too tough

Professors did not do a lot beyond rehearsing what the commands did. More important, there were a lot of small things that would stop progress on the course unless you went deep into the forums - for instance, one of the files in the final project was illegible unless you used the right text editor. Final project was poorly designed in that the data were untidy but intended to stay that way (See "Should I decompose the variable names?" in Thoughtful Bloke's post at https://thoughtfulbloke.wordpress.com/2015/09/09/getting-and-cleaning-the-assignment/ - he is right about jerk and mag but wrong about time/freq, gravity/body, acc/gyro, and x/y/z, which are mutually exclusive members of the same set and thus values that appear in column names). I appreciate that this course, unlike other online courses, actually makes you think, but students should only have to think about topics germane to the course. Overall much more frustrating and time-consuming than it should have been.

This course is by far the least polished and engaging course I've taken on Data Science in Coursera. It just feels like it's 'phoned in'. Like the lecturer isn't really bothered with making it interesting, and just wants to reel off all this stuff as quickly as possible.

Then there are things that are just sloppy - like the section on Regular Expressions - they cover them, but then don't demonstrate their usage in R even once. Like - what function(s) can I use this stuff in? Then, at the end of Regular Expressions, he talks about turning off the 'greediness' of the * operator, but then doesn't even bother to show what that means via an example. It's like he just couldn't wait to finish the video.

I'm doing this course as part of the 10-course specialisation, but if the next courses are this flat and boring I'll probably pull out.

This course is where the material starts to get difficult, and the learning materials fail to provide the structure needed. There absolutely HAS to be a better teaching method than "reading the slides of bullet-ed text that I'm also showing". No functional examples are provided in the lectures and the real learning content is linked out to web resources. You will have to Google your way through this class because the provided instruction will not contain answers to the quiz or exam questions. A real disappointment.

I also think that Coursera knows this, because this was the first course where they ramped up the e-mail encouragement campaign. Their data must tell them this is where people fall off the specialization. Rather than addressing with marketing and messaging, they should encourage the instructors to improve the course.

There is a lot of room for improvement. In an ironic twist, since the course is about "cleaning data," we are left to our own devices figuring out a lot of this very outdated material, broken links, codes that don't work, etc, so we have to google and search StackOverflow and forums to fill in the gaps and create a better course. I was subsequently asked to be a Mentor in the course, but I would rather the author of the course revise it, instead of having us work for free trying to help people get through outdated material. All the help is in the discussion forums already anyway, so I'm not sure why they need more Mentors. The saving grace of this course is that you will learn, if you are desperate to learn, and it is part of a greater Specialization that is worth your time.

I believe this course suffers from neglect. Rarely did I see any of the mentors participating in the group discussions even though there were plenty of questions. Furthermore, some of the quiz questons seemed incomplete or confusing. The project was no better. I feel like the course was recorded a few years ago, and not much done after that to fix flaws, even though they are probably well known. The material is useful, but it would be nice to have a set of notes or a text to go with the lectures. You will spend a lot of time searching the internet to compelte the assignments. Sometimes that is good, but other times a guide geared to the course would have been better.

I am done with this course. every week is the same thing. the lectures are a long list of references to other references. The quiz questions can not be answered without spending hours troubleshooting RStudio or searching the forum for help and hints to find out why the loaded packages or functions are not found. The quiz recommends to load packages that don't work or have dependencies that are no longer valid. I wanted to take this specialization to learn new data analysis techniques. if I wanted to spend my time searching the internet for answers I can do that without paying monthly fees. Good luck everyone. I am done. I will try a different course or field of interest.

The methodology of getting and cleaning data was good but the course materials were lacking and really outdated. Some of the material is 5+ years old and reference deprecated packages and functions or includes links to sites that have been long updated or no longer exist. I found myself spending a lot of time doing my own research on what packages to use. There is value in that.

The quizzes and assignments cover good topics but the instructions are pretty unclear as to what the ask actually is. It takes a lot of independent research and combing through the forums to gain clarity. It is very time consuming.

Not a great course. The lecture videos were dull and not very informative, and did not do a good job of preparing you for the quizzes at the end of each week. The lecture videos mentioned and linked to a number of external resources, but you couldn't click on the links through the videos, so that wasn't useful. The forums were much more helpful than the lecture videos when it came to teaching you what you needed to know. I understand why a course like this is essential to the Data Science specialization, but I feel like this content could've been covered in a much more engaging and instructive manner.

Have to say, very disappointed when comparing this to the first course. The first course teaches you the concepts and the quizzes/projects give you a great environment to learn new concepts while proving knowledge of the previous ones. This course so far has 20is minutes of videos per week that teach you 60% of what you need for the quizzes, especially true for the second week. Save time and use another resource for learning about APIs and other data resources.

Not bad, but certainly not good. I cannot believe there is a style of teaching where you never get to see the best way to do something. I can slog thru the programming, but I doubt it's the best way to do something, but I never get to see how something should have been done. It's odd we have no feedback from prof and just 'grading' from other students who also are slogging thru without ever seeing the best or even some good ways to have done something.

The instructor cares very little about the ability of his students to keep up with his explanations. The pace at which the material is presented is horrible, the amount of details is just the bare minimum. I do not think it would be too much work for the instructor to double or maybe even triple the length of the course videos. But he just does not seem to care.

Instructor reads lectures in monotonic voice. The lectures themselves are just a series of cases of some R functions usage with no basics of Why you need to clean the data or real cases with complete examples how and where to get your data and what steps you can do to make it useful.

The course has a lot of links for tutorials in R. That's a plus.

The project at the end requires actions that data scientists should know but does not actually talk about the items. For example the project "book". You hear about it but are not actually taught the right way to make one. At best case you are taking a guess and at worst you are learning bad habits or missing out on what should be in it.

Didn't really cover how to deal with messy data, e.g. if you need to join to datasets and have orphans, or you have no foreign keys between two datasets and you need to use fuzzy matching.

Basic validation was also not covered (i.e. making sure that your data covers all that you expect).

The explication in every lesson is really bad, and the exercise need more thigs that they explain, you must search the most of the tools in the course, if they make some videos or examples with all tools in the program, maybe can be better but in this moment is not good course

I saw two main issues with this course: 1) dated lecture videos, oftentimes with R code that can't be replicated using up-to-date packages, and 2) lack of thoughtful design: example after example after example after example doesn't really teach you anything.

The class is getting stale. The instructors didn't respond to questions on the discussion forums about quiz items, the majority of assessment items seem to be available on Google and 50% of the peer reviewed assessment I checked used plagiarized solutions.

The code for the final assignment is peer reviewed which doesn't make sense. It should be reviewed by either a TA or some kind of application than can verify what you've done. Also, the assignments were a bit of a leap from the video tutorials at times.

Although this course is on a very interesting topic, it is quite outdated. Its lectures and examples are quite outdated; some web scraping examples are incompatible or don't exist anymore, and the described techniques are mostly (outdated) R libraries.

The videos did not teach anything that was going to be on the quiz so it was like answering 5 questions at random using google. The lesson plans and project were very vague and too much time was spent trying to figure out what was even being asked.

This is a very crucial part of the data science specialisation and I feel more hands-on exercises and quizzes should have been there. Small practice quizzes for testing incremental learning within a week should be there.