This course does teach one useful tools in R. However, I feel like this course needs some serious improvements especially when it comes to the disconnect between the lecture videos and the actual programming assignments. To me it felt like during the lecture videos I was being shown pictures of a car and told what each part does, then in the programming assignment it was like I was being asked to build a fully functional car from scratch. It just wasn't practical. In the future I think to make the Programming assignments more realistic to complete on their own the lecture videos show go more in depth on the tools and functions it's teach. Not only that but i feel like I would have greatly benefitted from watch the Instructor go through sample problems and situations where I could see the tools and functions in practice. Especially for me who is a beginner in R to be able to create some of these fully functional R functions from just the lecture notes would have been extremely difficult. Also, a little note in the future you should adjust the approximate times it would take to complete the programming assignments because i don't feel like they are realistic for new programmers. For example, the approximated time to complete the week 4 programming assignment said 10 minutes and that was just flat out wrong. If you do make some of these changes I feel like this course will be significantly more enjoyable and that the students will learn significantly more.

Be prepared. The lecture portions of the course are quite short and basic. They don't provide much in the way of practical examples.

The assignments often have little to do with the lecture topics (week 3 introduces a whole new concept that you have to learn all about outside of the course to complete). They're decent assignments, and you'll learn from them, but if you don't already have a lot of experience, expect them to take a long time to complete.

I guess my main problem besides this is that there is not enough emphasis on learning the foundations. They introduce subsetting and give you some simple assignments which give you a basic superficial understanding of the concepts and syntax. Unfortunately, that stuff takes some time to really sink in, and you need to sit and play around for hours to get a good feel for it. If they had some optional assignments where you could have some guided exploration of the topic that would be super helpful.

The one saving grace is that there is a lot of help in the forums for each week. The mentors are doing their best to help students out and are generally pretty quick to get back when you ask questions.

My DEAR COURSERA TEAM thanks for providing this course.But I am sorry to say this course is not for the beginners who was not professional coder. I am from background of applied electronics and instrumentation working in Level 4 automation at Oil and Gas domain as subject matter expert. I wanted to use this language for analyzing the plant's day to day critical alarms data. During the phases of the course I faced lots of trouble when performing the assignments. I have to approach some expert coder in order to understand the question and answers. I would feel that team has to modify the course. One more feed back reading method is not at all helpful where as practical was helpful.

Once a learner is accomplishing one course hence material of the course should be sufficient in order to finish the assignments where as this course is not meant for that, I would prefer please modify the strategy. Assignments are not really helpful for learning it simply killing someone times. The reason is during the practical or reading at least same kind of example like assignment, should be demonstrated.

This course seems to have a huge gap. The content presented does not prepare you for the final assignment. The lecture videos introduce R functions and there are interactive demos in R that show how to use them. However, the final assignment requires you to determine which functions to use and how to implement them correctly. Consequently, I had to spend >10 hours finding online examples to complete the final assignment and the code I wrote used functions that were not presented in the course!

I realize that there are multiple ways to write R functions and there are tons of existing R functions to utilize, but what should be presented is one standard way of reading data, organizing the data, sorting the data, etc.

Also, I found the lecture videos to be very uninformative. The presentations need to be more streamlined, focused, and include basic examples.

Overall, I don't feel like I have a firm understanding of R. I certainly do not feel comfortable teaching R to others. I think the course could be improved with more examples of basic R functions, such as lapply and order.

By the end of the course, you do start to develop the "hacker mentality" the course writers want you to develop. That being said, it does seem that they could have done a much better job in the video introducing core concepts that would have given non-programmers a better starting point for looking for outside researchers. A ton of time is wasted in the earliest set of videos on super basic topics, but then the course jumps into some very advanced topics with little attention paid to core concepts except by the mentors in the forums. If these really are "core concepts", as described by the mentors, one would think that the teachers would spend time on those concepts in the videos instead of on more arcane use cases (which are precisely the sorts of thing it makes sense to look for in other sources). It really does seem that the teachers have no concept of what is critical content for beginners, given this odd imbalance of tons of time on extremely basic topics, tons of time on fairly advanced topics, and almost no time on critical intermediate-level content.

This course is not set up right, the assignments ask you to do things that aren't explained until the next weeks content, its kind of discouraging. In the end, it does teach you the basics of R, its just too bad that the way to get there is aggravating.

The reason I still only give it 2 stars is because of the quality of the courses itself. There are many, many 1 and 2 minute videos, these could've easily been combined. The teacher seems unprepared in his lectures, he stutters and repeats a lot and makes a weird noise between slides. This shouldn't be necessary with pre-recorded lectures. The assignments and quizzes are also poorly written and contain spelling and sloppy mistakes, which doesn't make sense because the material isn't new. It all just makes it seem like the teacher doesn't really care and just wants to sell the course to a lot of people without putting in much effort.

If you are not following the specialization I would not advise this course for beginners. I'm quite surprised to see the course get such high ratings.

This is not a course for a beginner in programming. If you are interested in learning R, I would recommend going to DataCamp and starting there. The lecture videos were not engaging, and the jump from the lecture and quizzes to the programming assignments was quite significant (as others have pointed out). The lecturer for this course does have a pre-programming assignment on his GitHub repository to aid in the first programming assignment, but this was not easy to locate nor advertised well (had to scroll through discussion forums before I saw someone mention it). The swirl assignments were the only saving grace for a class that was otherwise not engaging or structured well. The class could stand to be restructured: for example, it would have been nice for the lectures to cover the str() function before our first programming assignment. Overall, this class does seem to be a good choice for a "refresher" course if you already have some experience coding in R, but be wary if you have little to no programming experience.

I would only recommend this course if someone were to audit it, and not pay for the certificate and access to the assignments. This course states that it's for beginners, but I would disagree. While the lecture material was helpful for me, someone with little background in programming, the assignments seemed poorly fit to the course. There was a very large gap in what is learned in lectures and what is meant to be applied in the assignments in order to pass. I understand the instructors want to cultivate the so-called 'hacker's mentality' within the students, but I felt I did not get what I paid for. I wasn't anticipating to pay a fee for this course only to have to go and spend a lot of time on google filling in gaps that the instructors did not cover in order to pass, and still not understanding the assignments fully. I think this isn't conducive to great learning for beginners (as this course stated it's geared towards), and for this reason I wouldn't recommend this course.

This course is very bad. I would like to say this course sucks compared with my sql course no matter how it is structured or how the instructor conducted the course. The instructor is very boring and does not explain very clearly. Even when I start, he does not clearly clarify how to start to use R. He was just talking about what the powerpoint shows. I will absolutely not recommend this course if you are beginners. (0 STAR)

I will give a little credit to swirl, a practical tool provided by this course to learn R by yourself thought the instruction of the R program itself. But for the later part of swirl, it's confusing sometimes. However, I learnt much more from swirl rather than from the instructor. I can't understand what he is talking about but you have to make it clear thru using swirl. He said the swirl is an optional part but he does not realized what he shows is much worse than swirl. (2 STARS)

Overall, I don't recommend this anyone, especially if you are beginners.

Poor quality. A disappointing experience.

The lectures are very basic, thought for people that have no experience in programming. This might be ok, but the difficulty of assignments should follow; instead, they can be relatively hard; newbie programmers will probably have an extremely hard time solving them, considering that many of the problems they will encounter are not treated at all in the course.

The course does not focus enough on what differentiates R from other languages: just a few videos on data types, *apply functions, and a tiny little bit of scoping (very unclear). All these areas should have been expanded, and the course should have had more arguments as well.

Finally, the assignments are not enough and their grading system is quite poor: instead of submitting your code and testing it automatically, you should calculate some quantities by hand and select the results out of a few possibilities.

All in all, I was disappointed with the course.

Althought there is a lot to learn here and the instructor knows their stuff, I am disappointed in this course. First, I recommend going ahead and having the computer give most of the lecture, except for maybe the introductions to each week to keep it personalized. The instructor repeats "um", "uh", and "so" far too much (2-4 times per sentence) and it's very hard to focus on the lecture.

Second, the assignments do not match the material taught, and there is no scaffolding. I have a Ph.D. in quantitative sociology and eight years of statistical coding experience (not in R) and I think that is the only thing that got me through the assignments. I'm sure I would have been completely lost if I had no programming experience. It makes no sense to me why the instructor doesn't slowly build the assignments in difficulty, but rather throwing students in the deep end analyzing data from 322 different datasets.

Please improve this course for future students.

This is a challenging course because there is a marked gap between the video lectures and the weekly assignments. This course will be far more demanding of your time and willingness to fail that the Data Scientist's Toolbox. If you have the time and resilience to search out support materials and frequently peruse the Discussion threads, you will be able to find what you need. My peers with programming experience confirmed that there is too wide a gap between the content delivered in the video lectures and the demands of the coding assignments. If you decide to take this course, be sure to do the swirl exercises so you get a feel of how the R functions work. When you grade your peers' work, you might notice that more than a few decided to plagiarize code from others.

The videos need to be redone. Effective instructors know that students learn by seeing examples, not by listening to broad descriptions of what a function can do.

From my past experience, I know it’s not easy for people to take suggestions. But I still want to have a try this time.

I had high hopes for this course, but I am quite disappointed. I think Dr. Peng needs to improve his teaching skills.

1. Good teaching is clear, concise, and right to the point. So please slow down, speak. Don’t mumble.

2. Programming is a practical skill. So the best method to learn programming is to use step-by-step demos. You can talk about a concept for 5 to 10 minutes but I still can’t get it. Use a demo and I can get it right away.

3. Don’t just try to cover the materials so you think you have done the teaching, try to understand how your students learn and make sure they really get it.

4. If you haven’t covered some concept, don’t assume your students will understand it by magic.

A good teacher can make the learning experience effortless and fun, a poor teacher makes it like a torture.

This course is "1-dimensional" and not of high didactic quality. I have followed several other courses ( also Python in Coursera set up) and this one really disappointed me ( I'm in wk 2 out of 4 now). It is a bit of a pitty that the instructor speaks too rapid ( it is ment to be also for non-native US speakers?). The material is hardly explained step by step. So what I do at the moment: I watch the video and than go to Yuotube to find other videos that deal with the topics in an easier and didactic better way.

Examples of good cousres in my opinion are stats and probabilty course/ and the python course

PS "all of the data toolbox" course seem to have the same flaws.

Of course my knowledge level is low concerning programming ( I am a MD, so hardly scientific educated...). Nevertheless I hope this feedback can be of any good.

This course features great Swirl exercises and decent if bland lectures. Unfortunately the homework assignments are highly problematic. They (a) do not build enough upon what is taught, (b) all too often require skills that have not yet been taught, (c) are extremely advanced for what is supposed to be a beginner R programming course and (d) often require you to look up things in Google rather than in the class lectures or exercises. You can easily put 20 hours or more into this course per week and still struggle to complete or pass it. I would recommend beginning R programmers to steer clear until they improve the assignments.

If however you're a more experienced R programmer this might be a worthwhile challenge, but I would caution that the assignments are not well-tied to lectures and exercises.

I am very frustrated by the disconnect between the lectures and programming assignment in this course. The lectures are very basic, but that makes sense for an intro-type course. The programming assignments, however, have very little connection to the lectures. I have some experience using software with command-based interface (including R) for statistical analysis, and I am relatively good at searching and finding information independently, but this course was way too hard even for me. I might revisit programming assignments again once I have more time and more experience with the application, just for the sake of completing the course, but I kind of find it silly that I need experience with R to pass the course intended for people with little to no experience with R.

Some of the assignments in the course require coding that is quite a bit more advanced than what is taught. I felt there was insufficient direction and instruction provided in how or where to begin to complete the homework in week 4 especially. This made the homework quite frustrating for me as a true beginner in R programming, and as someone with limited programming experience. I spent a maddening amount of time wandering the stacks and wondering how to even do what was being proposed. If you are a genuine beginner in R and do not have a lot of experience programming in some other (possibly related) language I would hesitate to sign up for this course. It is my opinion that previous experience really is required for the course to be accessible.

For my level of expertise in programming, there were huge jumps between what was explained about the R language in the lessons and what was expected from the programming exercises. So I needed to spend A LOT of time Googling and studying various examples on the web, and then, just trying again and again and again. No way could I have passed the course in a month if I hadn't been retired and been able to drop everything else to put in the time I needed. I did learn a lot, but, would have preferred to do it with less wear and tear on my stomach lining.

Also RStudio is not quite as stable or unbreakable as I'd like, nor is there any real way to get feedback on programming style when just getting the answer took 98% of my available time.

There are huge gaps between learning materials (videos) and assignments. Assignments are very difficult and definitely not for R beginners. Also assignments' instruction are a little difficult to understand. I wonder if the purpose of assignments is to exam our reading and understanding abilities or the skills of R!!!

I also took courses <Getting and Cleaning Data> and <Exploratory Data Analysis> at the same time and these two courses' assignments are much easier than the course <R Programming>. Writing function in R really isn't that easy especially for an R beginner who only has little knowledge of R packages and functions. I really don't think this course should arrange as the second course to take in this Data Science course series.

The lectures that the professor provides are quite clear, and the practice exercises created for this course do well to reinforce the lectures. However, the assignments are impossible and have little to do with the material. It is as if one is taught introductory algebra and then given a calculus problem to solve.

The professors who designed this course need to go back and review the way they design their assignments because they ruin this course. Even submitting the assignments is difficult as they somehow expect you to know how Github works. Unless you have taken an R course before or you are an experienced programmer, I would avoid this class. It will be a waste of your time.

Well, I learned the basic of R programing , I very impressed with the assignment questions; they are so challenging and I got a lot out of it. At the same time the swirl package come along with course is so helpful. Having say it, I was very dissatisfied with the way instructor present this course; it so embracing to teach programing course by reading slides. How so ?????

This is not history or music/Art course; you have to show each and every example on IDE.

I should have give this course a lone star rating , but for all resources come with it made to be nice to add one more. otherwise 1 star would have been fair review, for there is no rating option below 1 star.

The assignments for this section were difficult to follow and did not necessarily align with the presentations. There was less instruction on R than I expected and it made the programming assignments more difficult. I suggest you have programming experience and it would be even better if you have exposure to R.

A few mentors on the DB did provide some advice on how to think about and write the programs, so find those before you start your assignments. You will likely need to scan the discussion board for advice provided to other students rather than wait on a response from a mentor. They don't have dedicated staff to answer questions.

Love Coursera but was disappointed in this course. Being a true beginner w/no background in programming or data science, I felt the programming assignments were way too far a stretch from the Swirl exercises (which I felt were super helpful and beginner appropriate) and lectures. I spent 8-12 hrs on each one just googling and trying to decipher the programming jargon that most responses were written in. I was able to figure out workable code for most problems but I feel like I am still lacking the basic fundamentals underlying the solutions to these assignments and would have a hard time reproducing solutions to similar problems.

Unfortunately, lessons and practice exercises do not prepare students for the assignments beyond the first week. Concepts are brushed over lightly with chances for basic application, then assignments are given that seem years beyond the material taught. I'm not sure how such a disconnect was created. More thought and time should have been dedicated to building students' confidence levels. More frequent assignments building on lesson topics and engaging discussion could have helped. Discussion is more of a library of past problems, not a platform to engage students. Overall, disappointing.

The first 2 weeks have too many videos, which make it difficult to follow. This breaks the flow of the lectures and makes a lot of unuseful repetitions.

The slides only have words and are hard to follow, no graphs or illustrations, this means that you rely on the voice of the lecturer to guide you, which is difficult sometimes.

Furthermore, the code presented is not run 'live', and results are often lacking, which makes comparison with 'my own results' hard to do.

Summary: Not that its a bad course (SWIRL is a great tool), just look for another course that's easier to follow.