PyCon Rehearsals: Gerrymandering, and Big-O

Hosting and pizza provided by VMware (https://www.vmware.com/).

Two rehearsals tonight for talks going to PyCon US.

** Fighting Gerrymandering with PyMC3: Colin Carroll & Karin C. Knudson

At the end of 2017, there were seven states with ongoing redistricting litigation. We will discuss a statistical model that the United States Supreme Court declared to be appropriate in cases of racial gerrymandering, and show how it can be implemented and used with the library PyMC3. We will also discuss what the model tells us about racial gerrymandering in North Carolina.

** Big-O: How Code Slows as Data Grows: Ned Batchelder

Big-O is a computer science technique for analyzing how code performs as data gets larger. It's a very handy tool for the working programmer, but it's often shrouded in off-putting mathematics.

In this talk, I'll teach you what you need to know about Big-O, and how to use it to keep your programs running well. Big-O helps you choose the data structures and algorithms that will let your code work efficiently even on large data sets.

You can understand Big-O even if you aren't a theoretical computer science math nerd. Big-O isn't as mystical as it appears. It's wrapped in mathematical trappings, but doesn't have to be more than a common-sense assessment of how your code will behave.