Learning R: Index of Online R Courses, October 2015

October 8, 2015
By

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

by Joseph Rickert

Early October: somewhere the leaves are turning brilliant colors, temperatures are cooling down and that back to school feeling is in the air. And for more people than ever before, it is going to seem to be a good time to commit to really learning R. I have some suggestions for R courses below, but first: What does it mean to learn R anyway? My take is that the answer depends on a person's circumstances and motivation.

I find the following graphic to be helpful in sorting things out.

  Untitled

The X axis is time on Malcolm Gladwell's "Outliers" scale. His idea is that it takes 10,000 hours of real effort to master anything, R, Python or Rock and Roll Guitar. The Y axis lists increasingly difficult R tasks, and the arrows within the plot area are labels increasingly proficient types of R users.

The point I want to make here is that a significant amount of very productive R work happens in the area around the red ellipse. So, while their is no avoiding "10,000" hours of hard work to become an R Jedi knight, a curious and motivated person can master enough R to accomplish his/her programming goals with a more modest commitment. There are three main reasons for this:

  1. R's functional programming style is very well suited for statistical modeling, data visualization and data science tasks
  2. The 7,000+ packages available in the R ecosystem provide tens of thousands of functions that make it possible to accomplish quite a bit without having to write much code
  3. Numerous, high quality books and online material devoted to teaching statistical theory and data science with R

If you have some background in some area of statistics or data science a viable strategy for learning R is to identify a resource that works for you and just jump into the middle of things, picking up R as you go along.

The lists below link to courses that can either start you on a formal programming path, or help you become a productive R user in a particular application area. Some of the courses are "live events" that you take with a cohort of students, others are set up for self study.

The courses devoted to teaching R as a programming language are

The first two courses above are from Coursera's Data Science Specialization sequence. Taught by Roger Peng, Jeff Leek and Brian Caffo they are probably the gold standard for MOOC R courses. I am a little late with this post. The Data Scientists's toolbox started this past Monday but there is still time to catch up. The third course, Introduction to R Programming, is a relatively new edX course from Microsoft's online offerings that is getting great reviews. The fourth course on the list a solid introduction to R from DataCamp. R Programming – Introduction 1 is a beginner's introduction to R taught by Paul Murrell or Tal Galili.  Next listed, is a Spanish language introduction to R from Coursera and O'Reilly's interactive Code School course.

These next three lists contain courses from DataCamp and statistics.com and online resources from R Studio that introduce more advanced features of R by buildng on basic R programming skills. Note that the final course on the DataCamp list introduces Big Data features of Revolution R Enterprise which is available in the Azure Marketplace.

DataCamp

statistics.com

RStudio

This next section lists courses from the major MOOCs, and non-MOOCs DataCamp and statistics.com that use R to teach various quantitative disciplines

Coursera Courses

edX Courses

Udacity Course

DataCamp

statistics.com

Finally, here are a couple of google apps and Swirl, a new platform for teaching and learning R that may be useful for learning on the go.

It's time to "go back to school" and make some headway against those 10,000 hours.

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)