This course uses a software package developed by the instructors for a portion of the required tests. There are serious bugs in this package that make it impossible for students to complete the tasks assigned. Going through the discussion boards, these flaws have been present for at least 2 years -- clearly Johns Hopkins in general, and the instructors in particular, hold the students' time and learning experience in such low regard it borders on contempt.

This course should be removed for two reasons:

1. The only videos are the introduction videos. Everything else is a text-based assignment. This is just lazy. If I wanted to google how commands worked, I can do that without paying for Coursera. There is no instructional material contained in this course.

2. The student is required to submit assignments through a third-party interface that doesn't work. Completed assignments are not given credit. There are many people in the discussion forums saying the same thing.

The content is good, but many students (including me) are NOT able to submit assignments with the course "token". I've commented in the course discussion forum about this, and I have opened a support ticket with Coursera, but no one is taking serious initiative to correct the malfunction.

This course, and the Specialization as a whole, is poorly structured and a rather cursory overview that is unlikely to satisfy serious learners. If the material is completely new to you then it will likely leave you confused as it does not adequately explain the concepts. If you are already somewhat familiar and are looking for deeper dive into R and programming you will be disappointed as the courses just don't have the depth of material needed to gain mastery over the subject.

Your money and time would probably be better spent on a couple of books by Wickham.

Not possible to submit programming assignments, this course seems to have a lot of technical issues. Signing out during the first week and un-enrolling the specialization. I'll check again later to see if those technical problems are solved.

Swirl doesn't allow some codes even if its correct. '=' is not allowed for example.

When a number of students ran into an identical problem with an assignment, there were no support or help, for weeks. By the end, we never got any support. A work-around the glitch was found by a student. For a paid course that is not optimal.

this course basically just has you run through the widely available SWIRL packages and then gives you a quiz it has poorly prepared you for and charges you for the privilege.

There was no mentor to help.

The course dumps a ginormous amount of content onto learners while being extremely poorly structured. The course materials are littered with spelling and formatting errors. There are assignment segments in the course that require R functions that were not covered in the syllabus. Multiple datasets required for stepping through the course materials as well as required assignments were at best not provided by mistake or neglect (datasets.zip file only contains the "Team Standings" data), or at worst provided only with the purchase of the textbook + dataset on top of the Coursera subscription fee. Some of these issues could potentially have been solved by having more responsive instructors in the forum, but they were nowhere to be seen, and there are questions with datasets or swirl token that date back years without replies. Overall, I highly recommend spending your money somewhere else. Perhaps downloading swirl alone with all its free materials without going through this course is a good start.

If I wanted to read a book, I would have bought the book.

There are also many "right" ways to code, and the swirl tutorial, while helpful for learning specific syntax, should not grade correct answers as wrong simply because they use a different syntax. It is overly rigid.

Great gap between teaching and what is required to pass the course. Unnecessarily difficult.

The didactic material is not compelling.

Not recommended.

Not at all engaging. Its like a university lecturers notes without any personal interaction or videos.

I really enjoyed this course. I thought it was a really good introduction to R. I thought Swirl was a really good tool as well. The Week 02 Swirl assignment was more time consuming than the others and the Week 04 quiz which required hands on problem solving was very good and an excellent review of the materials. I would emphasize to future students to review the online textbook and to be prepared for Google Searching in order to answer some of the questions especially from Week 02 - 04. In other words, not everything one needs to pass this course is solely contained within the weekly chapters nor the suggested textbook. I assume however; that this is by design to stretch the student and to force them to put time into learning and understanding the materials and searching the Internet for additional help. Bravo!

Loved this course! Beware that the material is at an intermediate level, and not really appropriate for beginner R programmers. The final quiz was extremely challenging, as it should be, and very useful.

I give this course six stars and take one away for the automated upload of answers from R (the swirl package). This package /process is really buggy and causes needless grief especially in Lesson 2.

The content of this course is A+++

Excellent tour of basic R programming with many examples and well thought out assignments . The final project uses a real life data set and it was fun to use dplyr piping to find the correct answers. The Swirl program, which is downloaded inside of R as a package and is used for grading, was a little cumbersome at first, but once you get the hang of it, then it's not bad. Hang in there and don't give up.

This is a really great introductory course to R as a programming language. By the end of it, you do become proficient in reading data from different formats into R and in performing basic data manipulation routines using cutting edge packages. Fantastic if you are a beginner. If you are already fairly experienced in R, only the last week of the course may hold some interest to you.

This course was a very good and powerful intro in data wrangling and tidy data. The base of the course is the interactive swirl environment which lets you experiment and play around while guiding and teaching you. The exercises in the end of the course can be a challenge and force you to combine and use all the knowledge presented throughout the course.

Amazing course, I would say addictive. However the swirl lesson #12 on Data Manipulation and quiz of week #4 on Reading and Summarizing Data offer a huge difficulty spike in comparison with the rest of the assignments. I would say almost too high compared to the rest, I would rather increase the difficulty gradually.

It was an excellent course. Even as an R user I learned a few things. Swirl can be annoying sometimes since even if you get the same answer using a different function it counts as wrong. The quiz had some tricks that one had to be aware of but they're not discussed in the course, I looked in the Forums.

Excellent course. Very well organized with plenty of course material to access for help. One thing though, given how good a lecturer Roger Peng is, it would be nice to have a few lectures interspersed throughout the course. Not needed but it would break up the lessons a bit.

Very practical course with plenty of practices to get you started data handling in R. Course focus on Tidyverse series of packages which come in handy when dealt with data manipulation. Little is talked about on in-built functions of R and use of functions is not covered.

Except the last programming assignment in week 4, which deals with large dataset that my computer had tough time reading, the course if pretty good and had provided me a basis for specialization in Data Science. Thanks to JHU and Coursera for the wonderful concept.

I learn enough introduction of R that I can feel comfortable uploading, manipulating and reporting data in R. I also get more comfortable in getting and properly using help/information about r functions and features that are available on the Internet.

A thorough introduction to the basic syntax of R, with a strong focus on understanding how to apply the key packages in tidyverse. I now feel like I have enough knowledge to conduct moderate to advanced complexity analysis on large data sets.