Inspired in part by Byron Smith's post about trimming our standard Python lesson, Christina Koch has written a post of her own about preparing to teach that lesson. She organizes her discussion around the motivating question that opens the lesson: "We have to accomplish a task (reading in data, analyzing and plotting it) by writing a program. How can we be smart about it?" It's a good read, as it shows how an experienced instructor thinks about (re-)designing teaching material. I hope her conclusions will feed into our discussion of how to revise the lesson (which we're going to decide in September).

Meanwhile, Pauline Barmby has also written a post comparing Software Carpentry to The Data Scientist's Toolbox, which is the first of Johns Hopkins University's data science courses. There are some interesting observations on both the content and the format (the JHU course is run online through Coursera). As with Byron and Christina's posts, these reflections are essential steps toward improving what and how we teach, and we're grateful for them. If you have thoughts of your own, please either write them up and send us links or send them something to post here.

This post originally appeared in the Software Carpentry blog.