R Programming is the second course in the Data Science Specialization offered by the Johns Hopkins University on Coursera.
At the end of the Data Visualization Course, discussing with other fellow MOOCers on the “Your next MOOC” thread, I was expressing the wish to take this course. Many people who had done it stated that it was a tough one, so as I was totally “bare” in R, I started looking around for some preparation. I completed all three excellent small R and Ggplot courses from Prof. Charles Redmond on Udemy, to get familiar with the RStudio environment, to understand the potential of R and to start getting my hands dirty on it. I also had an email exchange with Prof. Redmond, who suggested to me to take also his other three free courses on the same platform because they contain a lot of “R”. I am going to do it some time in the future. Some other excellent comments on the same thread mentioned the MIT course “The Analytics Edge” on the edx.org platform, I am currently taking this one as well. You do not only learn R, you also learn invaluable use cases and a lot of real life examples. Not happy with this one, I have also started the “Data Analysis with R” free course from the Udacity platform. This is part of their Data Analyst nanodegree program. In the last two courses I am not beyond module three, but I thought I had enough to tackle the “difficult” R Programming (and I am going to write an article on those in the next future).
Well, actually I am almost done with it, I just have the second part of Assignment 2 to do (this is the peer evaluation), I have also done the optional assignments and I have done it in 2 weeks, so this is half of the requested time. But I confirm that this is not easy. The amount of work that goes in the assignments is HUGE. That is actually real work, and most of the time it is as such because not all the info needed to complete the assignments are given in the lessons videos. I am not really sure if this is a defect, as you really learn a lot by searching on stackoverflow.com or googling around. However there are also configuration management issues in the course. In some cases the lecture slides are not in line with what professor R. Peng tells, in many cases this is enough to confuse people. If you are watching a video and somebody tells you 3 and you see 10 on the screen you tend to believe that either you missed a part of the video or that you fell asleep for a short time. These are little details, they do not remove value from the course but they lower the overall experience.
In the end, the course is worth the pain, I feel I have learnt quite a bit but I also feel like there is still a lot more to learn and to practice.
