Python at ShiftGig
By: Brian Eagan
By: Tyler Hendrickson
Date: Feb. 11, 2016, 7 p.m.
How ShiftGig, which connects the right people with the right job, uses Python.
Python at Modest
By: Emily Anderson
By: Mark Ashton
Date: Feb. 11, 2016, 7 p.m.
We will describe how Modest, recently acquired by Braintree (PayPal) uses Python.
Python at Vokal
By: Chris Foresman
By: Adam Bain
Date: Feb. 11, 2016, 7 p.m.
How Vokal uses Python with Adam Bain and Chris Foresman
Python at Credit Suisse
By: David Matsumura
Date: Feb. 11, 2016, 7 p.m.
How Credit Suisse uses Python.
Python-Powered Data Science at Civis
By: Stephen Hoover
Date: Feb. 11, 2016, 7 p.m.
Civis Analytics uses Python to develop the machine learning software which powers our products, and we run our Python software in production. I'll give a (very) brief overview of what Civis Analytics does and where Python and the open source community fit into the picture.
Python at Datascope Analytics
By: Brian Lange
Date: Feb. 11, 2016, 7 p.m.
How Datascope Analytics uses Python to improve business and society through science and design.
Using Python for Kaggle competitions
By: Hana Lee
Date: Jan. 14, 2016, 7 p.m.
(Lightning talk as part of ChiPy mentorship)
I'll be talking about using Python to develop a classifier for a Kaggle competition looking at crime data in San Francisco
Dustin Shapiro's Python 101 Menteeship!
By: Dustin Shapiro
Date: Jan. 14, 2016, 7 p.m.
This is a brief overview depicting where I started before this mentorship, through the various projects me and Ray worked on, and where I plan to take it moving forward!
Constructing a risk metric from google query data
By: Michael Tamillow
Date: Jan. 14, 2016, 7 p.m.
We have created a dataset from the search queries provided by Google and matched it up with some market data. We will attempt to produce a some metric or predictive model given the limited, slightly correlated data.
Building a BusTracker Tracker
By: Ellie Anderson
Date: Jan. 14, 2016, 7 p.m.
First, I’ll discuss a data-gathering pipeline that uses AWS Lambda functions written in Python to scrape CTA’s BusTracker prediction service and interpolate actual arrival times. Then I’ll detail an API written in Django REST Framework to select and analyze a range of data. Finally, a simple JavaScript-based front-end visualizes the data provided by the API.