This is really R part 2, getting into file/API handling, data frames, regular expressions etc. The specialization focuses on data frames though little coverage of data tables needed for the capstone. Some of the ordering of the materials was confusing e.g. this course revisits date/time handling which was started in the previous course. Assignments are interesting and Swirl exercises are useful. All in all, the combination of these R courses gets you up to speed.

The quality of this course is much better than the earlier two. Although this course still has the problem of feeling like a disjointed series of topics on singular functions, there is much more of a cohesive overall theme and structure so it feels a bit more like you're building towards an overarching goal. The final project directly relates to the lectures and felt like a solid way to connect most of the ideas to a project on real data.

I suggest changing the quizzes and assignment questions more often because they're all over the internet for this course and rest of the courses in the specialization. I understand that students who are cheating are mostly hurting themselves, but it also affects the value and credibility of the certificates you're giving out.

In terms of course modeling and content, it's very nice. I really enjoyed. The swirl package is genius. Thanks.

There were a lot of good nuggets in here, but overall this course felt somewhat disjointed compared to the others. It would be nice to have more practice with some of the different formats (e.g., JSON) and for exercises to loop back to some of the early content. I did like that the final exercise required a fair amount of investigation into understanding the documentation and relationship between the files before undertaking to code.

The data downloads for two quizes appear to have been updated, meaning that there is no way to come up with the right answer. The course project could use some minor clarifications. I was very difficult to determine what was wanted, and this lead to my having to re-submit twice. The deliverables seemed to be confusing to both me and the graders. Course was difficult because of this kind of confusion, not because of the material.

Difficult course, I had to complete it over two sessions. I came into the Data Science track with no knowledge of computer language, which has made learning R particularly difficult; however, after the previous classes I am finally able to search for the information I need to complete the assignment. The other reviewers are correct that the final assignment is a doozy - it took me several hours to complete.

I would have given the course 4 stars if it wasn't for the last assignment which relies on other students to review your coursework. I understand that it is difficult to find another way of grading the assignments but the results of the process here are just too subjective and people influence your grade based on their subjective view on things, which I think is just wrong. Otherwise the course was good

The content in this course is essential, but the delivery is patchy and the course project is hard to complete with just the learning materials provided. Read around the course and visit the data science specialisation wiki for extra information, and work through it at your own pace, rather than that suggested by the course. It's much easier to do this now it's on the new Coursera platform :o)

decent enough but this is a heavy subject and really it is not that interesting although clearly necessary. I feel maybe it could have been organized better to make it more interesting Also reading some of Haldey wickhams book he deliberately keeps this part of Data Science away from new learners as it can be a bit dreary, so my recommendation would be to do some of the other courses first.

In weeks two and three, the course presents a list of data format and how to read them into R. I would have preferred a better description on why tidy data sets are considered tidy that included some side-by-side comparisons and downstream effects of untidy data. This would help me evaluate the effort and risk of introducing errors from tidying the data against the benefit of tidying it.

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good. I like the videos and the assignment. There is cerain redundancy of information. Much of the "new" information was already elaborated in the previous courses of the same specialization. Additionally, the grading system is based on other students whose knolledge may be not beyond the course scope and submitting an inovative solution can mean not passing the course.

Lectures add very little to what you get simply by looking at the slides on your own. Facilitators are expert biostatisticians, not R programmers, and sometimes their explanations of R functionality is superficial and imprecise. The assignments are rigorous and challenging, however, and if you take the time to go through all of the exercises you will gain valuable knowledge.

The theory is explained well and there is not much of a problem to follow the content. But there is a huge gap between understanding the theory and applying it practically. After one finished all lessons one is just not well enough prepared to solve the assignments. The problems, which one faces, are far too difficult to address without previous knowledge / experience.

first two week need an update, because many thing on the videos dint work easy on the computer, is not bad to look for more information about the subject on the web, but at least made that the examples on the videos work fine went anybody run the scripts on theirs computers, last two week are good a brief summary of R, and how to work with data, love those 2 weeks

The course fits nicely in the specialisation, and I enjoyed the Swirl exercises, which are massively useful. The structure, though, is a bit chaotic, with loads of topics touched only briefly. Perhaps less is good here. Also, I found that the Swirl exercises were repeated across Weeks, and sometimes they didn't have codes to earn extra credits.

Peer reviewed assessment with students who are unsure of the correct answers = unsure if solution is correct. Perhaps a formal process (same as previous course where a SHA commit is submitted and source is automatically downloaded (and plagiarism detected) & run to verify the output that columns / data meet an acceptable criteria

I think some concepts could have been taught better with simple examples first, and then gradually move to more complex ones, but using noisy data blur the learning objective , and again... the instructors are just showing up a slide.. I think the power of video and illustrations could have been better utilized

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.

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 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.

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.

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