I give this 4/5 because I feel it's an enormous intellectual leap for most people. I have a heavy computer science background because I minored in the subject. (I say heavy because I think many people taking this course might have little to no background in CS.)

MOOC 1 was very easy and elementary. MOOC 2 gets into programming concepts that most students wouldn't learn until a second or third basic programming course in computer science as part of their undergraduate degree. I found the projects to be quite difficult.

The course says you don't need to know Linear Algebra, and I suppose you don't, but one of the programming assignments has you performing a matrix multiplication. I took Linear Algebra thankfully, but I fear that the learning curve on this course became very steep and it happened all of a sudden for most people.

I will admit that I struggled and had to Google things frequently. I think that many students will be lost in MOOC 2 and not continue unless they have tech-heavy education prior to taking these classes.

Do not expect your time output in "R Programming" to be equal to that spent in "The Data Scientists' Toolbox." You will spend much more time re-watching the technical lectures for the quizzes and trying to find the very small tricks needed in order to implement the programming assignments.

Still a very valuable course though. I did learn a lot and was grateful for the opportunity. I will have to wait until my life is less busy in order to take MOOC 3. Thank you Dr. Peng!

I was initially going to give this 3 stars but it would have been a biased score as I found the programming assignments too hard otherwise everything else was great, so I've added a star to counter my own bias.

The programming assignments for a novice like me who has only completed the Data Scientist Toolbox course (which was the only prerequisite as far as I know but may have missed this in the opening spiel) would have significant difficulty completing and comprehensively understanding the programming assignments in any meaningful way (i.e. to reuse the knowledge in a different context or question).

I completed all of the videos, swirl programming tasks, took fairly decent notes, exams/tests and still had to look elsewhere for much more guidance than I would have thought necessary on the programming assignments as I found them too hard to do on my own. I am not that smart though and this review can only capture part of my own subjective experience.

Other than that, as I really enjoyed learning about R and analysing data in general as well as the way Robert Peng's teaching style and demeanour I really enjoyed it. Trying to work out if my failure to understand and reapply is a lack of my fully grasping the material and if so whether to continue grappling with R and programming in general. I've started the third course of the specialisation so will give you more feedback once I'm done there.

Thanks very much for the course and keep up the good work.

I really enjoyed learning R and stepping up the skills for using R. I really appreciate this great opportunity to learn exquisite classes for free at home. I do think this is as amazing as the Industrial Revolution. It is the Educational Revolution!

BUT it seems errors occur (in some assignments), and I feel the level of classes is getting higher/tougher suddenly. I feel overwhelmed and challenged from assignments in week 3.

&YOU GUYS TALK TOO FAST. (well, it surprisingly didn't affect to my understanding though). PLEASE! like myself, there are many many ESL students taking this course. I really don't think my English is poor (trust me, my undergraduate major is English lit and i wrote my final thesis for master's degree in ENGLISH as an ESL) but sometimes doing assignments and understanding lectures were challenging...

Maybe that's because it takes time to get used to all the technical terms that has nothing to do with my major or career so far. But I think, not just JHU, but all the good universities providing courses need to consider ESL students who wanna learn more and pursue their dreams.

Also it will be really great there are more ways to earn some degrees and more support for using the knowledge and skills I learned here for my career. (with reasonable price!)

Thank you for the wonderful course, JHU! Hope I can go to JHU in US someday.

Overall, this was a good course. Dr. Peng is an outstanding professor. His lectures were interesting and easy to follow. My main complaint would be that the programming assignments did not always reinforce (or emphasize) the material that was covered in the lectures for that particular week (Ex: Week 3 lectures focused on the apply family of functions, but the programming assignment was on Lexical Scoping. Week 4 lectures focused on simulation and profiling, but the programming assignment focused on writing functions to perform ranking). While the programming assignments were certainly useful, in my opinion, it would be better if they more closely covered the materials in that week's lectures. Nevertheless, I would still recommend the course to anyone wanting to learn R programming.

The course was a rapid cursory introduction to R. This being said, I recommend breaking the course into multiple separate courses and including more depth (with further practice) to guarantee that the student has truly achieved mastery. I supplemented the course with courses from DataCamp and Udemy, and only now (after completing the course here on Coursera, reading the textbook(s), and additional practice through the online training by Udemy and DataCamp) can I say that I am gaining true proficiency in R.

This being said, the course was very well organized, had great examples, and excellent lectures (although the audio could be clarified by quite a bit using a higher-end microphone, preamp, interface, and affordable audio production software such as Reaper to clarify the voice).

The course was great. Having no background in programming languages is surely a set back to complete this course. The first time I enrolled for this course, I cant go any further than a week. But in my second attempt I was determined to complete it and I did it. What I understand is, if you have passion to learn R , this is a nice course but you cant rely on the class teaching alone, there are quite a number of blogs from where you can get help to complete this course. I felt a huge gap between assignments and the teaching; means, one has to struggle a bit to complete the assignments. But may be thats how one can get better with the nuts and bolts of R. Again, the course discussion forum and a few blogs can guide you through to understand and complete the assignments.

This is a good overview to R Programming, though the lectures leave much to be desired, at least for a programming beginner. Watching the videos left me confused about key concepts, which I absorbed much better through the Swirl interactive exercises, the discussion forums and other examples online. Of course we are supposed to be "hackers," but I enjoyed this course much more once I just turned off the video, read through the slides and focused on other areas where I could learn the concepts.

Also, the discussion forums were great for helping with key issues, and the homework assignments were also outstanding for making sure you absorb and apply concepts. So it's worth taking - just keep in mind the videos may not be for everyone...

Learning R in 4 weeks was quite tough. I haven't done serious programming for the last 25 years but the way in which the course was structured, assisted me in getting through the difficult parts. I battled the most with the notations, ie. when to use <- or = or ==? Also things like when to use ( or [. Might sound trivial to you, but for a first timer it can be quite confusing.

I have come across a few "cheat sheets" which has helped me a lot. I am still using it and I'm busy with course 9 now. I don't think one should call it "cheat sheets", rather call it "summary sheets". If I can make a suggestion, I would like to see some of these "summary sheets" included in your courses as part of the supporting material.

Thanks a lot.

A very hands-on course, you get you hands dirty, so to speak, since the first hour. A few important notions are given and then it paces up, introducing you to several concepts required to handle data using R language. The Swirl exercices are greately educational but even then, I saw myself feeling a little overwhelmed at times, not being able to cope with the flush of useful functions and different ways to subset, create and transform data. This being said, I would say that despite the fact that the concepts are transmitted in an easy-going way, there is a lot to master here and the practice exercises sure are welcome.

If you are doing the Data Science specialization, you are just getting warmed up... :)

Although this course covers quite a bit of the fundamentals of R programming, you will need to use the web quite a bit in order to complete the assignments if you are not familiar with R. I could complete the assignments, but it took me a very long time, as some of the required R commands were not taught in this course (granted, if you are willing to fill in the gaps through google and the wonderful answers from all the guys and gals at StackOverflow, you can complete them, as I did). If you have no previous R experience, like me, but you can hold your own at programming,(me again) you can pass the course. If you have never coded, I think the course would be next to impossible; bear that in mind.

Good for a beginner. Practical exercizes are really helpful, assignments remain challenging and require a lot of extra learning.

However, it could be beneficial to review the quality of videos and assignment descriptions. For videos: presented code lines are in a tiny font, which becomes illegible if one tries to work with more than one window (on a 15'' laptop). Sometimes watching videos and testing things in R studio simultaneously could be much more productive.

Assignment descriptions could be considered incomplete, and there are few errors. I recommend to keep this in mind and read forum even before starting the script writing, as it provides way more better explainations in many cases.

I entered this course with no coding experience, and I can definitely say I am disadvantaged because of it. The lectures are great explanations of the different commands in R, and the projects are a great playground of R's different capabilities, but the lectures and projects are too difficult to approach using just the lectures. That being said, this course's difficulty provides students like myself the opportunity to learn how to ask questions and seek resources for the programming.

Overall, I do not enjoy this course - mainly because it is very difficult. However, the course is structured logically and provides (what I assume the majority of students) a comprehensive introduction to R.

The Course is a great and strong introduction into R. It was very challenging but rewarding when I got through it. It could however have more examples/videos/reading particularly in week 2 where the assignment was beyond the concepts contained within the videos/reading. I understand the benefit of online communities and primary resources but more care could be given to help new learners understand how programming logic works.

Unfortunately due to time constraints, I could not do the swirl exercises. These may have been very helpful for me and would encourage others to look into them.

Overall, I would recommend this course to others as it was originally recommended to me by a friend.

In terms of content, it's a great course, it's really challenging, sometimes I felt really overwellmed because I didn't know exactly what to do. The debate forum is really useful, but I think the real problem is that the course doesn't make you integrate everything step by step, that's the intention of the programming assignments, but I think it doesn't achieve it al all because they are really difficult for some beginners like me. If you have some background in programming it'd be kind of easy, but if you don't have any experience in programming it's really challenging, not imposible, but the homeworks take you a lot of time doing research to solve it.

This is a difficult course to get through without prior programming experience. In spite of that, I would have given the course a 5-star rating if there were not such a large gap between the course as presented and the programming assignments for Weeks 2-4. As I noted in the post course survey, the course would have benefited from a couple of extra weeks in which a larger set of programming examples were provided that linked the course to the programming assignments. Otherwise, I thought the videos and the "Swirl" exercises were quite good. Dr. Peng has an easy manner about him, is clear and explains the basic concepts quite well.

R Programming in Coursera is a great course. You will learn a lot about R and a lot about advanced programming. But you will suffer along the way. Every lesson brings tons of knowledge, and every quiz will leave you with the feeling you only knew half of what was needed. You will have to fill in the blanks, which is easier if you have previous knowledge of R and/or have previous coding know-how. From someone who had taken other R programming courses, I loved and hated this one at the same time. It's a great way to really learn R, but be ready to put the additional hours and reading required to successfully pass.

This course is a very nice introduction to R, I enjoyed learning with Swirl and I found the concepts and materials presented were valuable. I like the teacher Lee and his videos. I feel that I'm closer to the world of the Data Scientist after completing this course.

Cons: The theory is very far from the exercices and the assignments. I struggled several hours (and days) building the code for week 2 and 4. I ended by looking for help in forums and I was forced to sometimes look at parts of published answers. The instructors must improve the examples and the exercises of coding previous to the assigments.

Overall it's a good course and I've learned a lot from it. But I think It'd be great to have more examples and context on how and why use those functions. To me R is a really quirky language and isn't really intuitive to use and it doesn't have a lot of functions that other programming languages have such as push and pop for lists. I remember spending like 5 hours trying to get it to work in a way that I'm more used to but then realized it could be done in a completely different manner. Most of the time I spend so much time on trivial stuff which I think can be avoided if more explanation is provided.

I believe this is an "intro" to R course with great intentions. But as a starting R programmer I found myself stuck on some of the materials without knowing where to turn except for stack overflow. The course moves too fast for a pure beginner.

The videos and textbook sometimes work out of sequence and on a couple of occasions I found myself needing to move ahead in the textbook to solve problems that videos had not covered and vice-versa.

But overall, it was a very nice intro. I gained some knowledge but will definitely need more in depth courses to master these programming concepts.

Pro: The course covers a lot of information and the necessary reference material to do the assignments

Cons: 1) This is definitely NOT a beginners course with no background with R. The assignments cover/test things that are not covered in class. Please don't advertise this to be a "no experience required" class. 2) The code that is presented in the lessons is overwhelming (read: daunting) to someone like me (with no experience). So it doesn't help when the instructor talks through the code. Please look at datacamp tutorials - If it wasn't for them, I wouldn't have passed this class.

The overall topics covered appear to be in line, however the course looses some of accessibility given the lack of participation from the instructor and moderators. This may be fine for someone with a stronger background in programming and statistics, but more novice users (like myself) may find it a bit challenging. Guidance is probably available deep in the message boards and somewhere in the world of google.

Overall, great course for exposure to R, but if you don't have a strong mathematical / stats background, be warned..you will be stretched. Good for some, but not for all.

The course was OK as an introductory course. I agree with most of what's done there, but - as I mentioned on forums - I dislike some of the quiz questions which were created not to check knowledge, but instead to make students give erroneous answers. For example, quiz checks for 4 output values and each option for selection has a different first digit, except for one. And it is this one that's correct... Why? If you want to check my output, then simply let me easily choose buy fact-checking first digit only... Don't give people possibility to make mistake where not important.

The swirl exercises are very good. The assignments are quite challenging but enjoyable. The last one took me > 9 hrs. I would suggest to first give a sample assignment. And then in the theory discuss the tools for that assignment. And then give the assignment. The theory is a lot of explanation - which I am battling to "hook" onto something. Why - because I had not been exposed to an application opportunity. The text in the slides are too small to view on a phone. Overall I feel proud to have completed the course and it gives me confidence to attempt problems with R.

Pros:

- If you don't know R well, or at all, you will learn a lot about R!

- The projects were sufficiently challenging and used real data. Great experience.

Cons:

- Some knowledge required in the assignments are not covered at all in the lectures, or even the textbook.

- The week 2 assignment was a bit less clear than it could've been.

Other thoughts: I wouldn't recommend this course for someone new to programming. You should have at least SOME programming experience. Even for someone with some coding experience it can be quite challenging. But that's what makes it so FUN!

Although course is good and mentors have put a lot of effort in designing and explaining certain concepts still i would say that assignment exercises are quite much tougher as code errors come quite easily due to lacking of basic use of functions with operators and else.

My request would be to at least provide the helpful links that is sort frames in data frame, extract operator links in assignment 3 just as one begins to start assignment 3 and not somewhere hidden into the discussion forums. In that way one can save crucial time and can solve it understanding more