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.

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.

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.

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.

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.

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/

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

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.

How Deloitte uses Python within the Enterprise Science Team.

How Datascope Analytics uses Python to improve business and society through science and design.