Course content was good, but assignments were too hard and not well linked to the rest of the content. I spent hours online trying to work out the assessments which then leads to the question of why the course

I have a rudimentary background in R programming and a lot of experience with computers. I really doubt that someone with no programming experience would be able to complete this course. In many instances, the tools and functions needed to compete programming assignments were not taught. In addition, functions that are taught in the class are not used at all in the homework. The final assignment was unncessarily complicated and time consuming. I would not recommend this class to someone who is new to programming.

The gap between assignments and course content/exercise is too big. Assignments are very difficult to complete without programming knowledge.

This course lacked organizational structure. The lessons were not in a logical progression and the material jumped around a lot. Furthermore, once a topic was introduced in a video lesson, the practice did not correspond to the lesson just learned. This made it hard to reinforce what was just learned.

I would not recommend this course to someone with no experience with R.

I have programmed in SAS before (15 years ago), so I thought that experience would be sufficient for me to take this class. I have to say it is not. This class is definitely not for beginners - you must have programming experience, R experience and/or both. The programming exercises do not follow the lectures and it does not build upon concepts. You will be required to seek help elsewhere (i.e., Stack Overflow) and answers to your questions will take days, if not longer. So, the fact that the classes are organized by week means that you will not conclude in the time you believe you will. I feel that this class is more self-taught than anything else. I have decided to quit this class as it has become extremely frustrating.

I learned a lot of the R basics, but I was completely lost for the week 2 + 4 'write a function' assignments - they strike at a level too high for a complete beginner for me (and that's after religiously taking notes and completing all swirl exercises.)

Very steep learning curve, especially in the beginning. the professor does not have a very engaging way of presenting his material. Also unfortunately he always requires to first think theoretical and then apply some of the learned content this for yourself. However teaching works the other way around:

1) what is the problem we are facing?

2) how can we solve that problem (practically)?

3) how and why does this solution work (theoretically)?

this is proven to be much easier to learn new things and get started. Instead i think this course is set-up for people to struggle by themselves, which i find very frustrating and its not teaching! I ended up teaching myself through trial and error and lots of google search. So what am i paying you for then?

Disappointing

The assignments are too hard. Wish there were more similar example walk-throughs provided.

I learned a lot, but 1 star because most of the HMW are extremely hard compared to the lessons, and has nothing to do with the lesson, they need more smaller and easier HMWs . The class need to be structured better.

The material poorly prepares you for the assignment and expects a deep commitment to a single part just to solve it. I was unable to find help in the forums or online despite doing well on the quizzes and other work. Terrible course.

Damn near worthless. The course material has significant gaps of information. The programming assignments require knowledge that isn't presented in the course. I had to Google my way through this entire course. I would not recommend it.

I would be cautious to recommend this course to a complete beginner. I had been using R for about 6 months before starting this course and found the content challenging. For instance, topics like lexical scoping covered in this course are usually tackled in a more Advanced R course. For a complete beginner into R, I would recommend a book like "R in Action" by Robert Kabacoff or the course on Udemy by Jose Portilla.

However, if you have had some exposure to R and would like it to take it to the next level, this is an excellent course. It will force you to learn things on your own. Read the documentation of R functions, peruse through stack overflow and really step up your game.

Let me start by saying that I did learn some basics of R programming during this course with a lot of help from friends and fellow classmates.

I like to believe that my 20+ professional years in education and training design and delivery have left me with a pretty good understanding of adult learners, learning theory, and putting all of that into practice. With that in mind, here are a couple of points.

First, there is no prerequisite knowledge, skill, or course listed as required for this specialization. Here's what Coursera says about background knowledge, "Some programming experience (in any language) is recommended. We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required)." Great! My limited working knowledge of BASIC from the 1980s and novice ability with MS VB fit the bill. Math? No problem, got it covered with Algebra 2 30 years ago. But wait, it turns out neither of those are the case because there are pinned posts in the forum that say the author doesn't understand why an understanding of linear algebra isn't required because it would be really helpful, and you yourself make the point that, "Yes, the mathematics in Statistical Inference and Regression Models are tough for students who haven't previously studied statistics." Knowledge of statistics isn't required or even recommended for this specialization Mr. Greski. That's poor curriculum design and setting students up to fail because there is no realistic expectation set as to what they face in this.

Second, if the materials do not provide any framework or context to tie the assignments to previously taught content either in "lecture", swirl, or assignments, then the course designers and instructors did an incredibly poor job with the design. How can students, even those who have a background in statistics, be reasonably expected to know when an assignment makes use of information or learning that must be found outside of the course itself? This can easily be fixed by including a statement or section with each assignment that says something like, "This assignment covers material found in Lessons, x, y, and z. You will also need information found in sources outside this course such as datacamp.com, etc." The italicized sentence can be the same in every assignment. The paragraph could even say that the assignment is not connected to the current lesson in any way as the intent is for the student to make use of outside resources (or whatever the approriate intent is). Regardless, every graded assignment should have a purpose stating what the student should get out of it, and they could all benefit from a context statement.

Third, if people like me (those with a non-statistics/mathematics background) are not part of the target audience, then please define the target audience better. Currently, Coursera says this, "Beginner Specialization. No prior experience required." That makes it sound like it is appropriate for anyone with no background knowledge or experience because the course will provide all the background knowledge and skills needed along the way. I'm willing to bet that the full program for $3,310 at JHU has prerequisites other than "Beginner Specialization. No prior experience required," and "Some programming experience (in any language) is recommended. We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required)."

Finally, portions of this specialization that I have completed so far are out of date. There was one quiz question that involves a specific package in R that is not compatible with the latest version of R. That forces the student to guess. Of course, we can take the quizzes over and over again if we are patient enough so it doesn't really matter if we are guessing on the answers or actually learning and getting the correct answers. Does it? While $49/month may not seem like a lot of money to some people, for others it could be quite a bit if they are having difficulty finding a job and working to improve their skill sets and qualifications. Even though it is "just" $49/month, we are paying for what we presume to be a quality product from a top-notch university. I don't think it's too much to ask that someone correct all the various errors, keep the materials up-to-date with the current version of R (once a year at least), and review feedback such as this (and others) in the forums and Coursera comments. Yes, this means someone has to put the time in on that type of work. I can honestly say that the current state of the materials and quality of design and delivery are well below what I expected for a JHU associated product. I'm not sure I can recommend this to someone as an introduction to data science as it currently exists. I wouldn't be surprised if JHU as an Instructional Design program that could use something like this as a capstone project or similar effort.

I sincerely appreciate the time that the JHU staff, the folks volunteering as mentors, my fellow classmates, and my neighbor give to help me and other students understand the concepts and skills in this course. Our expressed frustration about not seeing a connection between assignments and lectures is not an expressed desire to have our hands held and be spoon fed. It's a frustration at wanting to understand the materials, how they fit together, and how we can use them, which I believe is the intent of education and training in general. Hopefully, Dr. Peng or someone from JHU will see this feedback and be interested in making improvements to the curriculum.

First off, I would like to thank you Dr. Peng for making this course. I am really happy to be one of his students and got the chance to learn so much about R programming. However, I am giving this course a 3 out 5 after completing it. The reason is that the assignments and final is extremely difficult and they do not connect so much to lecture videos. This course is 100 percent rewarding if someone can put all of their time and energy into it. But the difficulty is that if you are a starting out your programming journey, then this course can be "VERY DIFFICULT." The workload must be taken serious at all time!!

I am 100% new to programming and though the videos and Swirl exercises were both very helpful, they were not sufficient preparation for the programming assignments. It's the equivalent of giving two or three easy practice problems and then a high-level math test. How is a newcomer supposed to make that leap? That's why I feel this course is really written for working professionals who already know how to use the R language and who are just looking for a credential. I am disappointed.

Very challenging, but good course. I've been programming in R for over a year, but there were still some things for me to pick up in this class. Assignments were a challenge, but satisfying to tackle.

I tried to take this course 3 or 4 times and each time it turned me off of R. The course assignments do not match the course content and you are expected to really learn on your own by googling and what not. I finally was able to finish the course but it was hard to do it with out googling and finding other peoples answers posted on GitHub or other locations, because I did not want to cheat I did not use these other people answers although maybe that is the Hacker Mentality the professor was talking about. The professor would do well to watch this https://resources.rstudio.com/rstudio-conf-2019/opening-keynote-day2 and maybe read more about how people learn programming.

I am glad I learned R using other courses and came back and finally finished this course but if you are looking to learn R through this course good luck.

Videos are difficult to follow along due to speaker.

It was always too hard to understand this course because of gaps between theory and practice. It will be much easier if you have a lot of exercises step by step mooving us to the final assignment. Like swirl but more sequential.

The assignments were way beyond the scope of what was taught in the lectures.

Honestly, I'm very disappointed with this course. The content taught in this course is not in accordance with the assignments. It is like someone taught to be a builder and asked to build the Brooklyn Bridge ... Not fair at all.

If you are going to ask to build a bridge, teach me how. If you teach me how to be a builder ask me for a wall, not a bridge.

If I wanted to learn from outside source and "hack" my way through understanding, I would not be paying money for this course. I am paying money as I expect a smooth introduction and summary to the topic which it is not. I have taken classes in SQL and python on courser. Those were of better quality. I think this R class is one of the worst.

Really interesting course. The interactive coding sessions with swirl are especially useful. Would be great, if you provided sample solutions for the programming assignments, in particular for week 4.

This seemed unnecessarily difficult because I feel there was a huge gap between learning the concepts and the homework. I understand the "developing the hacker mentality" is needed but some things should not take so long. Maybe I'm just an idiot, but learning curve is huge. My recommendation is to be patient and google is your friend,.

Questions asked in the assignments were on a much higher level than what was taught in the lectures. I get it that I should do my homework and refer other sources, but that defeats the purpose of watching the videos on Coursera itself.

Lectures could be more interactive, instead of simply having a running commentary on a ppt slides.