Python-based data science to understand knowledge discovery and expertise: A science perspective
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.
The wonder and the horror of the mock module
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.
Let's see what's happening in the Python Job market in Chicago!
ChiPy Python Mentorship
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.
158 Python enthusiasts attended this meeting.
How SpotHero uses Python
SpotHero is one of the leading online parking reservation companies in the country. Come see how were using Python to make that happen.
Python at OptionsAway
How OptionsAway, an airfare lock startup, uses Python.
Python at Cofactor
We are going to talk about our history -- a high level why we switched from .net to python.
Topics include .NET pain points and the reason why we chose Python. We looked at Ruby, and chose against Ruby.
We will also talk about how we use Python, which include Bottle + MongoDB to build our APIs.
Python at ShiftGig
How ShiftGig, which connects the right people with the right job, uses Python.
Python at Modest
We will describe how Modest, recently acquired by Braintree (PayPal) uses Python.
Python at Vokal
How Vokal uses Python with Adam Bain and Chris Foresman
Python at Deloitte
How Deloitte uses Python within the Enterprise Science Team.
Python at Datascope Analytics
How Datascope Analytics uses Python to improve business and society through science and design.
Python-Powered Data Science at Civis
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 Credit Suisse
How Credit Suisse uses Python.
193 Python enthusiasts attended this meeting.
*half time special* imposter syndrome.
Seeing as this winter marks 20 years of my using Python, I might be inclined to say a few short words about imposter syndrome.
Create an interactive dashboard using Django, featuring data table and chart which take customized user filtering and sorting and return the filtered result.
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
Web App for Caregivers
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.
Dustin Shapiro's Python 101 Menteeship!
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!
Building a BusTracker Tracker
Using Python for Kaggle competitions
(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
Constructing a risk metric from google query data
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.
108 Python enthusiasts attended this meeting.
Meet the micro:bit
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.
An Introduction to the Portable Format for Analytics (PFA) and to Python-based Titus Scoring Engine
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.
SQLAlchemy: Beyond ORM
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.
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.
114 Python enthusiasts attended this meeting.
Python-fu in the GIMP
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.
Python at Nokia (by MacGregor Felix)
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.
132 Python enthusiasts attended this meeting.