Seeing as this winter marks 20 years of my using Python, I might be inclined to say a few short words about imposter syndrome.

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!

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.

(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

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.

Project Goal: Create an interactive dashboard using Django, featuring data table and chart which take customized user filtering and sorting and return the filtered result. Progress: 1. Data normalization 2. Data Importer 3. url patterns 4. Django form - HTTP get/post request 5. Created chart view with C3.js 6. Bootstrap for error warning and numeric stepper 7. Manipulate data within shell

The Portable Format for Analytics (PFA) (www.dmg.org) is an emerging standard for predictive analytics that addresses some of the limitations of the Predictive Model Markup Language (PMML) and was designed for today’s big data environments, including Hadoop, Storm and Spark. In this talk, I give an introduction to PFA, model deployment, and Titus: Open Data's Python toolkit for building, inspecting, and modifying PFA scoring engines. Robert Grossman is the Founder and a Partner at Open Data Group, which has building predictive models over big data for its clients since 2002. He is also a Professor in the Division of Biological Sciences at the University of Chicago, where he leads a research group in bioinformatics with a focus in managing and analyzing large genomic datasets for advancing the understanding of human disease.

You may have heard of the BBC micro:bit - a tiny (2" x 2.5") ARM based single board computer that every 11 year old in Britain will be receiving in a few months. (And if you haven't, well, as for everything else, start with Wikipedia.) Even better, the micro:bit runs Python 3 (MicroPython, to be exact). The Python Software Foundation is a partner in the project. (see http://ntoll.org/article/story-micropython-on-microbit for more) The micro:bit will be released in the UK some time around February, and should be available commercially shortly after that. Even though the micro:bit has't been officially released yet, a few have made their way out the door. So I happen to have one these precious few devices in the wild. I'd be happy to give a 30-45 minute talk about the background of the micro:bit and getting Python on it, about the teaching implications, the development done so far, and what's needed for the future, as well as the world tour that several of the devices are on. There would also be a live demo of the device.

Before I started my new job, I thought of SQLAlchemy as "that ORM people use with Flask." Well, it is that - and more! With this talk, I want to give the audience a taste of SQLAlchemy's philosophy and capability. Outline: 1) Picking the right abstraction: SQLAlchemy's ORM and Core layers. 2) Transaction management: The Unit of Work pattern (SQLAlchemy) vs. the Active Record pattern (Django models, Rails ActiveRecord). 3) In the wild: code samples plus practical concerns like migrations.

GIMP (the GNU Image Manipulation Program) is great all by itself but is even better with Python-fu. This talk demonstrates a little Python-fu to manipulate images in GIMP, with a little (slightly ugly) hacking to add external libraries.