Topics

• The 'collections' module
  By: Phil Robare
  Length: 10 Minutes
  Description: A quick overview of the collections module and its five data structures. The talk will be aimed at the intermediate level python user who has the basic syntax down but has not yet delved into the wealth of programming tools in the standard library.
• pyStan: Bayesian Inference for Fun and Profit
  By: Stephen Hoover
  Length: 30 Minutes
  Description: Probabilistic programming languages offer a flexible and expressive way to model data by treating random variables as first-class objects. Stan is a popular and well-supported library which allows users to write models in the Stan programming language and use MCMC methods to perform Bayesian inference. Stan itself is written in C++, and has a Python interface through the PyStan package. In this talk, I'll show off some of the capabilities of PyStan and go through a simple practical example of Bayesian inference in Python.
• Python, Startups, Tech Debt, and You
  By: Matt Erickson
  Length: 20 Minutes
  Description: There's a lot of people newish to Python and either interested or already in a startup environment (either within a larger corporation or an actual startup). Python makes a *great* tool for that, however while there’s ways to use it to carry your work along to great success, there’s ways to wind up with such spaghetti you’re tempted to throw your hands in the air and go back to Java. The focus on the talk is how to use Python and the tools it provides to avoid the unmaintainable mess while still being able to “cut corners” to get something out the door to make your boss/investors/customers happy.