Using Python in a Data Hackathon
So, you've seen a lot of buzz around hackathons and datathons. Machine learning and data science are all the rage, and the photos of the events on twitter just look amazing. Not to mention free coffee and pastries, if you're lucky. Getting to grips with one of these events can be stressful, particularly when you slam into the reality of trying to get something done in practise.
This talk provides a basic set of techniques to make sure you're set up ahead of time so you don't lose precious hours downloading packages over an overloaded and shaky WiFi connection, and gets you straight into the fastest way to start working with whatever unwashed dataset the sponsors have decided to throw your way.
Topics include: setting up your tools, loading large data, working with data subsets, convenience functions for data processing and making pretty pictures
Tennessee started coding when the internet did not exist. He is very glad the internet now exists and has been fascinated watching the growth in IT and related areas over the past 30 years. He can remember the first bug he ever had to fix - how to make his program save a copy of it's state, rather than a copy of it's source code.
Tennessee has moved from help desk operator (have you tried turning it off and on again?), through the developer ranks (is it definitely plugged in?) and now manages a team of around twenty (what exactly does IT stand for?).
He really likes Python. He also really likes attending and giving presentations at PyCon AU.