Intro on Using Python for Big Data
An overview of the Python scientific computing stack, with a focus on working with very large datasets. I'll try to introduce the most widely used libraries for scientific computing, Numpy and Scipy, discuss techniques for using them with big data, and touch on some of the machine learning techniques (and possibly libraries) for reducing large data into something manageable. Then I'll go over how to make some of the most common data visualization tasks (plotting a time-series or mapped data using matplotlib) dynamic and web-based.
About Hannah Aizenman:
Hannah Aizenman is working on a PhD in Computer Science at The Graduate Center, CUNY. Her research is in using machine learning to make sense of and visualize large, mostly climate, datasets, and she's spent the past two summers writing related web apps. Hannah's also a sometime NYLUG workshop/hacking society coordinator.