R Programming

I want to like this course, because I think R is a neat language with a vast potential for practical application. However, the course itself is poorly designed and implemented, and I feel like I am learning R in spite of, rather than because of, the course.

The lecture component of the course has little value. The lecturer tends to take a depth-first approach to presenting concepts, taking one concept and developing it out to its most minute and esoteric details before moving on to another. The result is that only a small fraction of what's presented could be considered appropriate for beginners and an even smaller fraction is useful for completing assignments. I've started ignoring the videos, skimming the slides and Googling everything. It works, but what's the value of the course?

Also, others have noted the large gap between the content of the lectures and the quizzes (very basic) and the programming assignments. The latter are challenging, which is good, but there's no intermediate work that we do to bridge the basic stuff to the advanced stuff. You're thrown directly into the deep end of the pool. I don't mind hitting up Google, Stack Overflow, etc. to learn things, but again, what's the purpose of the course if this is what we have to do? Why not make the assignments progressively more advanced, rather than very advanced from the get-go?

In short, this is a course designed by people who don't teach beginners very often and who have not carefully thought through what it takes to learn R from a truly entry-level position.

There's almost no need to add to what has already been said by the vast number of reviewers here - and yet the course is so bad that I feel compelled to anyways. For what it's worth, I had very minimal exposure to programming before this course, as is the case for most taking this I would imagine.

Plain and simple: do not take this course, whether you pay for it or not. I paid for it, foolishly, and felt compelled to finish it for that reason and that reason alone. It is not worth your time.

The lectures for this course are horrible to sit through. They are just a series of static slides that somehow provide simultaneously too much information and yet not enough. Very little of what you're shown will actually help you use or program with R. The structure of the lectures is also very scattered. First, they are divided into weirdly short videos that make it generally frustrating to go through. Next, the material presented isn't very logical, and he will constantly introduce functions that we are not familiar with little explanation. For example, throughout the entire course from the very beginning he uses the rnorm() function to generate some arbitrary set of numbers to work with, and then for some reason the last week is dedicated entirely to the random number simulation functions he has been using the whole time. Also, for some reason in Week 3 there are three lectures devoted to debugging. While knowing how to debug is clearly an important and useful skill, it's almost comical to think by this point we would be even remotely close to writing our own functions that are complicated enough to debug.

The instructor himself, Roger Peng, is very hard to listen to. I'm sure he is an esteemed and intelligent researcher in his field, but is a very very poor lecturer, at least for an introductory course. These are pre-recorded lectures and yet somehow he is CONSTANTLY saying "umm" and "uhh", backtracking on what he just said, and just generally not speaking clearly or at an appropriate pace. Staring at a static screen with some code written on it that you may not be familiar with listening to someone fumble over the words that they themself wrote and recorded is unbelievably frustrating. There was very, very little effort put into designing this course. He does not know how to teach R programming to a beginner, plain and simple.

The assignments. Now, everyone else has said this but if anyone in 2020+ needs confirmation that this is still the case - here it is. Just ridiculous. The quizzes are often trivial and feel unnecessary, but not completely unreasonable. When you get to the assignment, all your fears that have been sitting in the back of your mind throughout this course come to light -- you don't know how to *actually* code anything. I truly don't understand how whoever thought up these assignments could look at the course material so far, then look at the assignment, and think it would be even remotely possible to complete this assignment without ENTIRELY relying on google searches / stackoverflow, etc. It's simply not possible for a beginner. It's a very poor way to learn, because while there is merit to "finding out the hard way", when the task is so difficult that you feel like there's no way for you to have been able to do this on your own, you end up feeling defeated and like you've been wasting your time. They have almost nothing to do with the course material itself and will introduce new concepts and functions completely out of nowhere. The swirl problems were the only fun and useful aspect of the course and I felt like I learned way more from doing those than anything from the course itself, and again, makes you realize how pointless sitting through the lectures is.

Follow the advice of others in here and find other resources to learn R. Don't waste your time.

tl;dr this course taught by Roger Peng is so bad that it made me take the time to write this essay.

Just completed this course, passed with 100% score—but only because I have extensive previous programming experience.

Contrary to what is implied in the course descriptions for this sequence, you should probably not attempt this course (or at least not pay for the signature track) unless you have previous coding experience.

This course goes over R concepts well and concisely, but it does NOT go over fundamental programming concepts, it jumps right in with the assumption that you already understand and are familiar with classic data structures, interfaces, flow control, machine states, the characteristic rigidity of computing language syntax, and so on.

If you have no previous exposure to programming or Computer Science 101 concepts, this course will probably leave you absolutely bewildered from the first assignment.

If you do have previous programming experience, it's a great way to get up and running with R. The assignments are more or less perfect for this target audience—enough to force you to confront R on its own terms if you are going to find elegant and non-sloggy/kludgy solutions, but not so much that you're going to spend a lot of time getting bogged down in peripheral particulars.

I dock the course a star because the lecture materials at times fail in being technical enough—they tend at times to "teach by example" rather than "teaching by specification." That is to say that there are times when they tell you to use an R feature or syntactical phrasing *for* a particular situation without telling you *why* it works or what the rules are in general so that you can apply the same tools for other, similar-but-slightly-different situations.

I found that the R language specification itself is a great way to bridge the gap. The instructors/lecture point you to the right part of R to look at for given circumstances, and knowing this, you can refer to that component of the R language specification and get the complete details on how that tool/function/syntactical component operates at the specification level.

All in all, a valuable course for me, and enabled me to put R to work for real-world projects right after completing the course. But if I hadn't previously programmed in C, C++, Pascal, Python, PHP, etc., I would have probably been lost.

Like many of the previous reviews have stated, this course is extremely difficult to a beginner programmer. In fact, I would recommend that anyone who hopes to have any amount of success in this course first starts with an intro programming course such as Learn Python The Hard Way: http://learnpythonthehardway.org/book/

Additionally, I would recommend a more basic introduction to R before attempting this course, such as Data Camp's introduction to R course: https://www.datacamp.com/

Another good introduction to R course is Codeschool's Try R series: http://tryr.codeschool.com/levels/1/challenges/1

After completing those, the coursera programming is much more manageable.

My recommendation for completing the course would be to not stress out about the programming assignments, which, as previously stated, are incredibly challenging. Remind yourself that this course is free and take it a few times if you have to, focussing on really learning the basics and establishing a fundamental understanding of everything you learn each time you go through. With that mentality, the course changes from being an insurmountable challenge to a fantastic resource for getting a deep understanding of the R programming language.

Best of luck.

The course is helpful only to the point that it pushes you to look all over the internet to figure out how to understand/complete assignments . The downside is that if you're going everywhere else to learn, why are you enrolled in a class?? Typically, pushing students beyond course material is a good thing as it challenges them. That said, this is beyond pushing - it's akin to showing you what a few tools do, what a few types or material are and then telling you to build an addition on your house. If I hired the teacher to train employees at the company I work at and he took a similar approach to R Programming, I'd fire him.

Data Science has a great place in the future of Business, Science etc. We should be encouraging more and more people to pick it up and learn, not discouraging them. This course, from what I've experienced and read, is doing far more to discourage people from entering the field and more to validate those who are already familiar with R & programming and want to complete the course for a certificate.

Coursera is a great platform to do help encourage/educate others and I urge the company to review this class and it's approach and to work with John's Hopkins to come up with a better course or courses on R programming....

My recommendations:

1.) Don't pay for this course unless you've already completed it and just want the certificate.

2.) If you're new to programming, don't try and complete the course in a single month - you'll only get frustrated. You'll tear through the estimated 7-9 hours a week very quickly (especially, if like me, you have a full-time job). Take a month just to get through videos/quizzes and read his book.... I also recommend googling other R books that are out there and reading them. Then in the second or third month, run through the quizzes and then tackle the assignments.

3.) Git and Git Hub add an additional level of frustration to the course... get familiar with both during your first attempt so you can easily submit assignments when you're actually attempting them.

4.) Don't stress over the course. If it takes you a few attempts, that's what it takes. You're not the only one who thinks there's a huge gap between the lectures/videos and assignments.

Finished this. Got a distinction. Hated it. One reason was it's simply badly designed: going from lecture, via (frankly perfunctory, "oh, we need to give them a quiz on something, so let's ask anything vaguely relevant") quiz, to quite complicated programming assignments. If this is indicative of the state of pedagogy at JHU, any reputation JHU students might have stems ENTIRELY from a highly competitive selection/entrance procedure.

But maybe I just wasn't the target audience. The course is for 'experienced programmers'. My smattering of Python was probably not enough. R is a stats language with more fundamental data types than say Python or Java. Confusion over which types were returned or required by which functions was a major headache for me. But this was magnified by the failure of the materials to point out telltale identifiers, and on-forum chats with a few more experienced programmers suggested this was a pitfall for them too, until they worked through supplementary material they dug up for themselves. So... maybe not: even for members of the target audience the course per se was deficient.

In short: however talented the JHU team may be as data scientists, researchers, and on-campus teachers, like many of their MOOCs this is a half-hearted, pegagogically-incompetent, cynical attempt to cash in on the data science boom.

Which sadly, appears to have paid off.

I have spent entire days and nights working on the assignments. The course assumes that you know how to program in R - why, I don't know. Actually, let me go as far as to say that the course assumes that you are an outstanding R programmer. And if so you can find some entertainment in the video clips discussing topics such as debugging.
The discussion forums say it all... You can find people saying that after trying the assignments they are going back to Excel; and also experienced programmers in R unhappy about the difficulty of the exercises.
So if you wanted to learn R - forget about this uninspired course. If you are good at R, sign up and have some fun.
As for me, I'm going back to the last assignment, because I started this, and will finish it.

I am a 20+ year data analytics professional. My objective is to learn R in order to supplement my SAS, SPSS, SQL, C skills. Also - R has emerged as the tool du jour and I need to speak the same language as the people on my teams.

This is NOT a course to take if you want to learn R - end of story. If you want to learn R, go elsewhere. If you feel the need to achieve the JHU Specialization, fine - Learn R elsewhere and return to this course to check the box.

The best explanation (rationalization?) for the poor program design that I have come across comes from postings by some of the TAs on the internal Coursera blog pages - essentially the comment is that one of the program design objectives is to provide the student with a "real world" experience. That is, force students to experience an ill-defined problem and "teach" them the process of searching for alternate information among various on-line community resources.

I have worked in data analytics for my entire career and am currently an engagement manager for a well known analytic vendor at a Fortune 50 company - this is not how we do things in the "real world". The real world values and rewards efficiency. When I send my people for training, I expect an effective and efficient learning environment. After this experience I will search for an alternative. I'm reading good things about "Bootcamp"...

I've had years of experience as a PL/SQL developer and am familiar with packages, functions, loops, conditional statements, etc. That part of the course is easy for me. Where I'm struggling is in the programming assignments, which as others have said aren't well-related to the lectures (those are useful for overview/background). I've spent hours googling, watching other tutorials, etc., in order to figure out what R-steps I have to take in order to be able to get to the looping/conditional statements steps. I've paid for this class, but right now it looks as if I'll drop it, take the rest of the specialization for free (downloading absolutely everything as I go, including datasets), work through at my own pace, and officially re-enroll and complete it when I'm much more familiar with R. Then I won't be spending 15+ hours/week figuring out the basics.
One tip: purchase the author's book. It's quite helpful for the quizzes, though not so much for the assignments.