Computer Vision in Python: How to build a basic face detection system
(25 Minutes)
By: Jeremy Watt

In this short talk Jeremy will describe the universal pipeline for performing object detection (that is, the automatic detection of objects in digital images) in Python. This will include a discussion of various classification schemes, feature extraction methods, and their fusion in the form of deep neural networks. Demo code illustrating these concepts will be shown using the IPython notebook environment.

Getting meaningful results from unit tests
(10 Minutes)
By: Manu Phatak
Slides Link
We count on unittests failure messages to give us reasonable feedback on how to proceed with a failing test. When you're working with builtin data structures and objects, unit test feedback is usually pretty good. It helps you identify and solve the problem. In contrast, the default results you get from custom objects can be practically useless. https://gist.github.com/bionikspoon/2e434a2c193a06b0996cc98c6a604de9

Intro to Deploying Django with Ansible
(15 Minutes)
By: Joe Jasinski

Deploying Django is a breeze when using Ansible. Learn a bit about the power that Ansible provides and how easy it is to get started using it!

Python Hype?
(30 Minutes)
By:

Brian will reveal the survey results that will help shed some light on the current and future projectile of the Python programming language. Has Python reached a peak? Will it popularity continues to rise? What are the users of Python at different levels saying about the state of Python Programming Language?

An overview of python projects of OS X administration
(20 Minutes)
By: Ryan Manly

In this talk Ryan will give a brief overview of several python projects used by many OS X admins to provide cached update services, imaging, software deployment, and configuration management. Looking at the code in these tools can provide insight into Apple's preferences frameworks and if you are in a "DevOps" type role some of the projects discussed may help you immensely.

PyCon 2016 recap
(15 Minutes)
By: Jerry Dumblauskas

Let's give an overview of Pycon

JIRA + Python
(15 Minutes)
By: Jonathan Pietkiewicz

JIRA is a popular issue tracking and project management software. In this talk you will learn about JIRA and how to interact with the tool using the jira-python library. Primary topics covered will include an overview of the API, creating and modifying issues, linking issues, and searching from Python.

The 'collections' module
(10 Minutes)
By: Phil Robare

A quick overview of the collections module and its five data structures. The talk will be aimed at the intermediate level python user who has the basic syntax down but has not yet delved into the wealth of programming tools in the standard library.

pyStan: Bayesian Inference for Fun and Profit
(30 Minutes)
By: Stephen Hoover
Slides Link
Probabilistic programming languages offer a flexible and expressive way to model data by treating random variables as first-class objects. Stan is a popular and well-supported library which allows users to write models in the Stan programming language and use MCMC methods to perform Bayesian inference. Stan itself is written in C++, and has a Python interface through the PyStan package. In this talk, I'll show off some of the capabilities of PyStan and go through a simple practical example of Bayesian inference in Python.

Python, Startups, Tech Debt, and You
(20 Minutes)
By: Matt Erickson

There's a lot of people newish to Python and either interested or already in a startup environment (either within a larger corporation or an actual startup). Python makes a *great* tool for that, however while there’s ways to use it to carry your work along to great success, there’s ways to wind up with such spaghetti you’re tempted to throw your hands in the air and go back to Java. The focus on the talk is how to use Python and the tools it provides to avoid the unmaintainable mess while still being able to “cut corners” to get something out the door to make your boss/investors/customers happy.

Multiple System Failure: A case study in debugging
(30 Minutes)
By: Adam Forsyth

Recently, the Braintree Python library wasn't working on Google App Engine. Braintree, GAE, requests, and urllib3 all had problems and I tracked down each one. I'll walk you through debugging with only basic tools -- editing the code to observe state and using git to find the responsible commit. This talk expects a basic understanding of web programming, git, and Python.

module of the month - usaddress
(10 Minutes)
By: Cathy Deng

usaddress is a python library that uses NLP methods to parse address strings into structured components (e.g. street name, city, zip). it is trained on real-world addresses with real-world data quirks - as a result, it's robust in handling messy data. usaddress was built by DataMade, a local civic technology company. TL;DR usaddress helps you avoid regex for address data, which is a terrible rabbit hole.

Hacking Bokeh
(20 Minutes)
By:

I brief introduction into Bokeh http://bokeh.pydata.org/en/latest/ And a bit on how to build interactive graphs in jupyter notebooks or stand alone.

ChiPy Python Mentorship
(7 Minutes)
By: Tathagata
Slides Link
This April we will the start the fourth round of ChiPy's mentorship program. We have worked with more than 70 developers till now, and some of them have landed exciting jobs by showcasing their mentorship projects. I'll give a quick view of the program and what are we looking for in a mentor and a mentee. FAQ: http://www.chipy.org/pages/sigs/mentorship/

Job Market
(15 Minutes)
By: Jerry Dumblauskas

Let's see what's happening in the Python Job market in Chicago!

The wonder and the horror of the mock module
(5 Minutes)
By: Stephen Hoover

The "mock" module is a powerful (and fun!) tool for unit testing, and it comes built in to the the Python standard library. I'll give an overview of some of the more useful features of the module, and finish with a warning about the dangers of too much mockery.

Python-based data science to understand knowledge discovery and expertise: A science perspective
(45 Minutes)
By: Daniel E. Acuna

All kinds of businesses are using data science and machine learning to understand themselves, lowering costs, engineering better products, and improving customer experiences. Similarly, we use data science to improve science itself, understanding how scientific topics are discovered and modeling institutional expertise. In our work, we use a combination of Python-powered big data analytics and web-based tools to achieve this goal, including pyspark (http://spark.apache.org), scikit-learn (http://http://scikit-learn.org), Django (https://www.djangoproject.com/), Celery (http://www.celeryproject.org/), and or-tools (https://developers.google.com/optimization). First, we will present the infrastructure behind Scholarfy, a recommender system for massive scientific conferences (http://www.scholarfy.net). We will also present a machine learning approach to automatically match expert scientific reviewers to research proposals (http://pr.scienceofscience.org). Finally, we will present the work behind our award-winning visualization, World’s Science Map (http://map.scienceofscience.org), where we modeled the institutional expertise, collaboration network, and funding of all institutions in the world. At the end of our talk, we will argue that Python-powered data science can improve not only businesses but also science, making it more agile and accurate.