I think giving this course a three is generous considering how incomplete it feels at times. There is a giant gap between what you learn in the lecture videos and what you are expected to do on the quizzes and assignments. The professors also don't really know how to help students as it seems they are too detached from looking at this course from the perspective of a student who has to learn a complex language. Some of the discussion boards are very out of date and give the wrong information about what you have to do or how to install some of the features that you need (besides Rstudio & R). I have worked with R before taking this course and it was still very frustrating. You do learn a lot of material, but not everything is explained well enough to fully understand the material.

although it gives an introduction to R, however the structure of the course overall was not up to expectation or objectives of it.

the videos were very much theoretical and boring. Introducing too many concepts (although basic) was not related to the programming assignments. I had to google almost all assignments. It's arguable that this is one way of learning and more of realistic. But on the contrary, as I'm paying to get the knowledge I shouldn't be wandering around to understand something that should've been taught in the lectures.

Moreover, I think that the videos need to be updated, the mentors need to be more involved in the forums, and the way the slides are presented within the videos should be more interactive or dynamic rather than being just captured lines of codes.

The lectures give the bare minimum of information needed to complete the actual assignments. There is absolutely no way to complete this course and the assignments without prior experience in programming or R itself. The code needed to complete the assignments is not explained in the lectures, and if it is, the lectures give little to no guidance on how to manipulate the information to get your desired results. Online communities are a godsend in completing this material. I understand that learning to code in R is an independent process, mainly one of trial and error, but without the proper foundation (which is barely provided here), one finds themselves agonizing and floundering completely in a sea of information that mist be deciphered on your own.

This course is not for the faint of heart. The course materials only give you the fundamental concepts but the assignments are extremely challenging. If you have no previous experience in R or computer programming then be prepared to spend hours researching online and experimenting with different techniques in order to get the assignments done. Also, the assignments are very time consuming, so I would advise that you complete the lectures as early as possible so that you can spend most of the week working on the assignments.

Nevertheless, now that I have reached the end of this course, I must say that I learnt a lot about R over the 4 week period. If you are up for the challenge I would recommend it.

Dr. Peng and colleagues communicate well and provide a reasonable overview of the R platform. I took this class hoping to take my reasonable prowess in Stata and bring it to R. The biggest gripe I have is that the course stuck almost entirely to how to program in R rather than how to perform statistics in R. Given that the historical purpose of R as described in the course was to start using it for statistics and then learn additional programming as you go, this approach to the course seemed a little backwards. I have a reasonable working understanding of R as a programming language but now I am on the lookout for a course that can let me apply my statistical knowledge to working in R.

Swirl lectures were very innovative and helped me significantly to grasp the topic. They can perhaps be updated to get into more examples/complexity- especially *apply functions and base graphics.

The lectures were dry and difficult to understand. The instructor though experienced was adding only a little bit more to slide content. Often I was able to read the slides but instructor was still reading off that.

It would have been great to point out that more practice will be required to gain significant expertise in R and any direction to get more practice.

Overall satisfied and appreciate the efforts taken to set up this course. Only made these suggestions to improve course.

In general, the course enabled me to understand the semantic and syntax and be able to process data with it, using basic functions.

I also want to highlight that I really liked "swirl" and it is a great tool to get into the subject!

Where I would encourage you to do better are:

* the slides of the ppts are in low resolution and the fonts are two small in my opionen (there are also no colours)

* Dr. Roger D. Peng did not speak that clearly and often he go muddled.

* The assignment in week 3 is pourly explained and only some further explanation in the forum could clearify the major aspects. Also it was quite unclear for me, how it is related to the learning content of week 3.

Course lectures do not adequately prepare students for the exercises. Lectures were very basic and the programming assignments required a lot of learning beyond the lectures. By supplementing with info from other online searches i was able to learn enough to complete the assignments. I feel like I learned a lot this way-- however-- i really didn't need the course to do this. I don't feel that the course lectures added anything that i couldn't have found on the internet.

Also-- if you can't stand the sound of people swallowing and slurping when they talk-- you might want to avoid these lectures-- I couldn't stand the slurping noises this guy made when he talked!

Dr. Peng is great at teaching, and the lectures are not hard to follow. The recommended e-book is almost a transcript of the lectures' examples, and I really recommend getting it (I got it on print, as I like to make annotations while following the videos).

That being said, the Programming Assignments are extremely difficult for anyone with no programming background. The Assignments do not require just what is being taught, and demands a lot of google and stack overflow research in order to solve the problems. If you are doing other Coursera courses and/or working at the same time, I recommend you invest time in this and do not let the assignments for the weekends.

I really struggled - coming from a zero programming background. I recommend tons of reading, practical exercises, swirl is a bit of a savior and so is stackoverflow. Even with all of that, I struggled to get my mind to think like a programmer and structure the thoughts into plans for the assignments and tests. I think there needs to be a course before this that teaches one what the first step of solving a programming-related problem; eg one that focuses on flow charts, breaking down the question, etc... It literally is like learning a new language : you need to keep reading and practicing and watching/listening to others do it until you start to get it.

This is a very challenging course. The slide-based quizzes and programming assignment up till the third week are fairly easy, however, for an intermediate level course, the programming assignment on week 4 is very tough. I found much of this course more as 'R as a programming language' than as 'R for statistics and data analysis'; the latter was what I was looking for. On the positive side, the course gets into the nuts and bolts of R and gives an overview of many different aspects of R as a programming tool. All in all, I found many new things to pick up from the video lessons and programming assignments, but there are equally many rooms for improvements.

This course was mostly self learning. It gave me a framework to go along but what is presented in lecture and assignments are completely different. The discussion board and mentors are really good resources and can provide pointers to move along. It is definitely not a class that can provide sufficient information to learn and build applications, but the individual's effort in passing the quizzes and the projects will. It is up to the student to figure out through stack overflow, youtube, and other search result to figure out how to complete these assignments. I believe the assignments given from this class are realistic to real-world situations.

Overall, a somewhat challenging course due to the complexity of assignments not matching the lectures. The lectures were simplistic, which would be acceptable for this course level, but applying this information to the assignments went well beyond what the instruction given. I would not expect the assignments to be a recitation based on the lectures and expect their difficulty to increase. However, I spent a considerable amount of self-learning to complete the assignments. I would have spent more time, if it weren't for a couple of contributors/mentors on the discussion forums helping us along.

Assignments and swirl package were useful, but lectures were unengaging to new learners because material was taught in a very abstract way. Real, concrete examples in the lectures, maybe presented through an R programming interface (shared from instructor's screen) may have been more helpful.

Also, issues on programming assignment frequently pop up that were not covered well in lecture. For example, assignment 3 had issues with entries in the csv all being factors, so we often had to convert those to a more useful format. I didn't feel the lectures prepared me well for that challenge.

To start with the positive, the swirl exercises on the R console were very good. By far the most helpful thing about this course, and probably worth enrolling to have access on its own. Five stars

Biggest complaint: the programming assignments were significantly too advanced for the first level of this course catalog. Their lack of instructions also make it impossible to complete without some type of external guidance. Thankfully, the message board assists with some - but far from all - of these issues. One star

Videos were average, some more difficult to follow along with. Three stars

It was a decent introduction to R programming IF you already have some programming experience. I would never recommend this as a first programming experience to a novice programmer. That would be like throwing someone who has never swam in the Atlantic without a life boat! That said, if you have some programming knowledge to begin with, the Week 2 and Week 4 programming assignments were good. Week 3 was disjointed; it had NOTHING to do with the lectures from that week. I found the Week 3 programming assignment to be tor easy and, frankly, not very valuable as a learning tool.

I wanted to love this course so bad but unfortunately I couldn't. There was a great distance from what was taught in the lectures and what was asked for you to do in the assignments... and It's not a matter of knowing how to program. I know Ruby and Python but R is a very peculiar language itself. Perhaps they should invest more on gradual exercises like the one they advised to do on github or some easy statistical exercises... Now I don't know if I should take the next module or if I should look for something out of here where I can learn R. I see potential on this course.

As a scientific researcher, I find R programming a very useful tool. Therefore, I was excited when I found out about this course on Coursera.

Although I have learned a lot, I found the theoretical classes relatively hard to follow and the programming assignments really difficult to perform. I can understand this kind of method encourages the self-learning. However, I guess more theoretical support would be helpful, mainly for programming newbies. On the other hand, I really enjoy the swirl practice exercise and I really recommend them to future students.

Cheers, Jose

Being familiar with Python but not R, I didn't find this course too challenging. It is, however, rather topical and I would have liked to have spent a lot more time studying how data frames are organized and how to slice/subset them quickly - this really needs to be hammered into us as budding data scientists! Also, I felt that the lexical scoping assignment was contrived and frankly so complicated that I didn't really absorb what I was supposed to be learning. That being said, the last assignment was great!All in all I thought this was a decent intro to R.

I enjoyed watching video lectures and doing swirl exercises. They provide a good overall understanding of basic R commands and functions. The biggest weakness of this course is that weekly programming assignments are way beyond the level that you get from the videos and swirl exercises. Basically, you will need to figure out how to do them on your own through extensive googling. There is almost no guidance in the videos or other course materials. I am feeling like I wasted considerable time on those assignments because of that, hence I am giving 3 stars.

As a student of Computer Science and Engineering, I have done extensive coding. The course material is very easy to understand and is readily available in the book "R Programming for Data Science", written by the instructor himself, Robert D.Peng. However, I found the programming assignment for the 4th week quite challenging and took quite a bit of fumbling through the help manuals in R and using search engines. Students who are quite young in the art of programming will find the course assignments very difficult to understand and code the solution.

I understand that it must be a great challenge trying to teach R in four weeks. I feel the lectures were clear, the supplemental swirl assignments were beneficial, and that the discussion boards were fruitful. However, the assignments were ridiculous. Google is your friend with this course, as always, but if you're looking to gain a lot from this course, pay close attention to the lectures and the supplemental assignments in swirl. The assignments, in my opinion, were beyond the scope of the lectures and supplemental assignments.

There is a big disconnect between the lectures/learning material, and the actual programming assignments. Being new to programming, I found myself lost and lacking the "creative thinking" that seemed required to complete the assignments. The lectures taught a lot of new material, but there was no connection between weeks, and cumulative examples were lacking in order to teach newcomers how to tackle creating complex functions. Overall, I did learn, but I was left feeling disappointed once I encountered the final assignments.

The classes could and SHOULD be much better: deeper content, more references and material for reading. The video classes are really loosely tied together and aren't engaging. My perception is that I had video classes on one extremely superficial subject, while the assignments were different and considerably harder. I do not have a problem with hard and deep assignments, I quite enjoyed completing them, but I had higher expectations for the contents of the course itself. I do not believe it's worth investing in this course.

The video lectures and reading presented material well, and the programming assignments were reasonably well constructed, but there was a very limited relationship between those two elements of the course (the lectures/readings and the programming assignments). Because of this, according to the literature on teaching and learning, the course itself will have little to do with how well students retain and use the information. Perhaps the material here will be reinforced in later courses in the data science specialization.