Python's success as a data analysis language is due in large part to the strength of it's ecosystem for scientific computing, with Pandas (http://pandas.pydata.org/) and the IPython Notebook (http://ipython.org/notebook.html) project being chief among the tools that have driven the language's widespread adoption in the fields of data science and quantitative finance. This talk will focus on getting the most out of these tools, with a particular eye toward using them (in conjunction with Zipline (https://github.com/quantopian/zipline)) for quantitative financial analysis. The talk will conclude with a sneak peak of a new IPython research environment under development at Quantopian.
Scott Sanderson is a software engineer at Quantopian where he has tackled everything from API features to engineering better backtest performance. Before joining Quantopian Scott worked as a software engineer for Demiurge Studios, a Cambridge-based video game studio. Scott was also an early Zipline contributor working as an intern for Quantopian while also completing a BA in Mathematics from Williams College.