I did not come up with a good worksheet last week. We did a lot of examples, but I didn’t do anything unified.

On the other hand, here are some interesting resources I drew on for examples of continuous random variables in Python.

• PMF, PDF, and CDF basics using histograms here.
• Some iPython notebooks from the Statistical Learning online course by Hastie and Tibshirani. Original code was in R.
• Very pretty visualizations and plotting of distributions using the Seaborn package. I used a lot from this page in putting together my own iPython notebook.

This week was spring break so nothing new — only relaxation! I will have to make public some of the review material I’ve been working on, though, so that students can get another view on the material before class this week. A few more discussions and then another midterm… the semester races onward!

Why Python when I’ve been concentrating on Ruby for the last year? Popularity and the way Python’s developed so robustly into a data science tool with pandas/numpy/scipy. In particular, I want to do some time series analysis I’ll write about next week for another project and Python is just worlds ahead in time series. However, Ruby is developing on the stats and data visualization fronts too, and if I can schedule my time very well maybe I can play with some of that this weekend.