Title
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Diving into Open Data with IPython Notebook & pandas

Abstract
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I'll walk you through Python's best tools for getting a grip on some new
open data: IPython Notebook and pandas. I'll show you how to read in
data, clean it up, graph it, and draw some conclusions, using some open
data about the number of cyclists on Montréal's bike paths as an example.

Details
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The idea is to choose a sample data set (cyclists on Montréal's
bike paths), and

* clean up the data (fix date formatting issues, remove null values, ...)
* graph the data
* download some weather data from the weather office website and look at
  the correlation between weather and number of people biking
* do some aggregation to find out how many people bike on
  Mondays vs Wednesdays
* talk about possible directions to take the project (make a model using
  scikit-learn or PyMC!)

Additional notes
----------------

## Speaking Experience

I gave a lightning talk similar to this at Montréal Python. The video
is here: http://www.youtube.com/watch?v=2JJ102ZpoH0

I also gave a lightning talk about open data at
[ODX13](http://odx13.com/), and have given tutorials on Git and Python
at Montréal Python and to the McGill Physics grad students three times
this year.

## Why I know about this

I work as a data scientist, and I've been spending all day every day
for the last 6 months working inside IPython Notebook and typing
'import pandas as pd' as the first line :)

This is not substantially different in aim from the talk Taavi Burns
gave at the last Pycon Canada, but I keep meeting all kinds of people
who don't know about IPython Notebook, so I think it's still of
interest

## Constraints

Due to some family obligations, I can only come to the first day of
PyCon Canada (Saturday), so I can only speak on that day. 

Add talk proposal for PyCon Canada 2013
Add talk proposal for PyCon Canada 2013
