Statistics with R on Coursera

18332552I held off on posting about this until we had all the courses ready, and we still have a bit more work to do on the last component, but I’m proud to announce that the specialization called Statistics with R is now on Coursera!

Some of you might know that I’ve had a course on Coursera for a while now (whatever “a while” means on MOOC-land), but it was time to refresh things a bit to align the course with other Coursera offerings — shorter, modular, etc. So I chopped up the old course into bite size chunks and made some enhancements in each component such as

  • integrating dplyr and ggplot2 syntax into the R labs,
  • restructuring the labs to be completed in R Markdown to provide better scaffolding for a data analysis project for each course,
  • adding Shiny apps to some of the labs to better demonstrate statistical concepts without burdening the learners with coding beyond the level of the course,
  • creating an R package that contains all the data, custom functions, etc. used in the course, and
  • cleaning things up a bit to make the weekly workload consistent across weeks.

The underlying code for the labs and the package can be found at Here you can also find the R code for reproducing some of the figures and analyses shown on the course slides (and we’ll keep adding to that repo in the next few weeks).

The biggest change between the old course and the new specialization though is a completely new course: Bayesian Statistics. I touched on Bayesian inference a bit in my old course, and this generated lots of discussion on the course forums from learners wanting more on this content. Being at Duke, I figured who better to offer this course but us! (If you know anything about the Statistical Science department at Duke, you probably know it’s pretty Bayesian.) Note, I didn’t say “me”, I said “us”. I was able to convince a few colleagues (David Banks, Merlise Clyde, and Colin Rundel) to join me in developing this course, and I’m glad I did! Figuring out exactly how to teach this content in an effective way without assuming too much mathematical background took lots of thinking (and re-thinking, and re-thinking). We have also managed to feature a few interviews with researchers in academia and industry, such as Jim Berger (Duke), David Dunson (Duke), Amy Herring (UNC), and Steve Scott (Google) to provide a bit more context for learners on where and why Bayesian statistics is relevant. This course launched today, and I’m looking forward to seeing the feedback from the learners.

If you’re interested in the specialization, you can find out more about it here. The courses in the specialization are:

  1. Introduction to Probability and Data
  2. Inferential Statistics
  3. Linear Regression and Modeling
  4. Bayesian Statistics
  5. Statistics Capstone Project

You can take the courses individually or sign up for the whole specialization, but to do the capstone you need to have completed the 4 courses in the specialization. The landing page for the specialization outlines in further detail how to navigate everything, and relevant dates and deadlines.

Also note that while the graded components of the course which will allow you to pursue a certificate require payment, one can audit the courses for free and watch videos, complete practices quizzes, and work on the labs.