Let the MOOCing restart

After a break, I'm starting two new Coursera MOOCs this month:

• Computing for Data Analysis: In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, creating informative data graphics, accessing R packages, creating R packages with documentation, writing R functions, debugging, and organizing and commenting R code. Topics in statistical data analysis and optimization will provide working examples.
• Data Analysis: This course will focus on how to plan, carry out, and communicate analyses of real data sets. While we will cover the basics of how to use R to implement these analyses, the course will not cover specific programming skills.

While I may not have time to complete either of these courses once my teaching starts at the end of the month, I hope to pick up some useful skills along the way. Previously, I found it a good idea to set personal learning objectives for MOOCs as this helps with engagement and ongoing commitment. For both of these courses, my planned learning outcomes are fairy straightforward:

• To improve my statistical knowledge and ability.
• As I tend to use R in bursts rather than constantly, to revise my existing/previous R skill set.
• To improve my knowledge of R and related software such as RStudio. 

Computing for Data Analysis, Week 1:
This is coming (as advertised) from a programming perspective rather than a statistics/R angle. I'll have to see how this goes for a couple of weeks. At the moment, I suspect I won't be recommending this to my students or colleagues. Based on Week 1, I have a feeling I won't be completing this course as it doesn't mesh with my desired learning outcomes.

A.J. Cann