Past Meetings • Recent Topics

Constructing a risk metric from google query data
(0:07:00 Minutes)
By: Michael Tamillow

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.

Using Python for Kaggle competitions
(0:05:00 Minutes)
By: Hana Lee

(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

Building a BusTracker Tracker
(0:07:00 Minutes)
By: Ellie Anderson

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.

Dustin Shapiro's Python 101 Menteeship!
(0:05:00 Minutes)
By: Dustin Shapiro

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!

Web App for Caregivers
(0:05:00 Minutes)
By: Shannon Cochran

This presentation will cover the Django project I completed with my mentor, Adam Bain. The idea for this project came from my former work as a caregiver for a child with Autism. As a caregiver, there were many times behavioral issues came up and I often wondered what other possible interventions people may have tried. The child I worked with was nonverbal which made discipline and finding out the source of a behavior much trickier. Every case of Autism is different but there are still some behaviors which are more common, especially as a result of the inability to communicate. For example, self-injurious behaviors are common and usually associated with the frustration of not being able to communicate needs. My idea is to create an app where caregivers are able to share their solutions to behavior problems and search for other caregiver’s solutions as well. The app will have a space for people to share both problem behaviors they want to decrease in their client or child and positive behaviors they want to encourage. This project allows caregivers to search for problem behaviors as well as positive behaviors and find out how other caregivers addressed the behavior and whether those interventions were successful or not.

Shuang Qiu
(0:05:00 Minutes)
By: Shuang Qiu

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

*half time special* imposter syndrome.
(0:10:00 Minutes)
By: David Beazley

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

SQLAlchemy: Beyond ORM
(0:20:00 Minutes)
By: Will Engler

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.

An Introduction to the Portable Format for Analytics (PFA) and to Python-based Titus Scoring Engine
(0:30:00 Minutes)
By: Robert Grossman

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.

Meet the micro:bit
(0:45:00 Minutes)
By: Naomi Ceder

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.

Python at Nokia (by MacGregor Felix)
(1:00:00 Minutes)
By:

Python is known to be a multi-purpose and multi-paradigm programming language. Come see how the Reality Capture & Processing (RCP) group of Nokia HERE is making use of Python’s versatility. We will show you how HERE RCP uses Python’s Object Oriented constructs to represent business models in production systems. You will see how Python’s functional lambdas are used to elegantly facilitate the handling of big data. We will discuss the use of Python not only in production code but also in test code. We not only use Python for production purposes but also to build utilities. We hope to show you how we utilize Python's versatility and closeness to the operating system to build sophisticated tools for development and operational productivity. You’ll see our Test Driven development effort while building Python products and how we use Python in Behavior Driven Development to code language-agnostic acceptance tests for the evolution of software and services. We will also give you a pick at our Python packaging and distribution.

Python-fu in the GIMP
(0:42:00 Minutes)
By: Tanya Schlusser
Slides Link
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.

Fancy genetics and simple scripts: Manipulating DNA data and becoming more proficient with Python
(0:20:00 Minutes)
By: Mark Mandel

Our ability to read the genetic code of organisms and to use DNA sequencing to learn new biology has benefited tremendously from technological advances in the past ten years. My lab looks at how animals get colonized with specific bacteria. As we have been generating more data it has become clear that we are underutilizing the information. We are beginning to build resources to be more efficient and clever at data processing and data mining from biological samples. I'll talk a little about the science in the lab and show one of our Python projects that is functional but in its early stages. I am eager for feedback, and I think the talk will have resonance for a new motivated Python user in any field.

Factor analysis: simplifying high dimensional data sets for visualization and machine learning
(0:25:00 Minutes)
By: Mark Albert

For many machine learning problems, there are far more dimensions to our data than there need to be for efficient learning. Often a first step is dimensionality reduction to remove both redundancy and noise. In addition to more efficient automated learning, factor analysis allows us to visualize high dimensional data sets in our standard human-limited 2 or 3 dimensions. For demonstration, we will apply PCA on a set of questions asked of the audience to map everyone onto a 2D "personality" map - allowing us to visualize the underlying personality factors of those present. Beyond fun visualizations, these techniques are the basis of more efficient generalization in many machine learning problems.

Python-fu in the GIMP
(0:25:00 Minutes)
By: Tanya Schlusser

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.

ChiPy Mentorship Oct-Dec 2015
(0:07:00 Minutes)
By: Tathagata
Slides Link
The wait is over! ChiPy's Mentorship program returns for the third time. We learned a lot from the previous two mentorship program and will do things a bit differently this time. This will be a quick overview how we are going to conduct the ChiPy's Python mentorship program.

Why You Can't Sit With Us - Understanding Network Analysis in Python With Mean Girls
(0:40:00 Minutes)
By: Richard Harris

Network analysis is a handy tool used to understand group dynamics, provide product recommendations, and prevent homicides (and other things). This talk will introduce the theory behind network analysis and showcase the flexibility of Python's NetworkX library. No knowledge of network analysis (or Mean Girls) is needed, but basic knowledge of Python and the iPython Notebook, will be helpful. I gave this talk last month in Columbus OH at PyOhio 2015.

Exploring uWSGI
By: Chris Sinchok

uWSGI is a very popular software package, but most Python programmers just connect it to nginx, and leave it at that. I'll be exploring some of the more advanced features of uWSGI, and how they can make your life easier.

Setting Up Machine learning with anaconda
(0:20:00 Minutes)
By: Joshua Herman

5 min What is anaconda and how do i use it 5 min What is ipython 10 min Why machine learning is fun and how to do easy classification tasks