Containerization technologies such as Docker enable software to run across various computing environments. Data Science requires auditable workflows where we can easily share and reproduce results. Docker is a useful tool that we can use to package libraries, code, and data into a single image. This talk will cover the basics of Docker; discuss how containers fit into Data Science workflows; and provide a quick-start guide that can be used as a template to create a shareable Docker image! Learn how to leverage the power of Docker without having to worry about the underlying details of the technology. Although this session is geared towards data scientists, the underlying concepts have many use cases (come find me after to discuss).
In an extended version of the lightning talk I gave for the spring ChiPy mentorship final presentations, I will go into more depth about how I collected and processed bus location data from the CTA's bus tracker API. I will also discuss interesting discoveries I made once I plotted the data, work I have done on the project since completing the mentorship (collecting data from 30 additional bus routes, converting visualizations from Bokeh/Python to D3.js, analyzing and visualizing bus bunching, etc), as well as future plans for the project.
A continued narrative of the tale of two snakes. In this talk, we will discuss some of the most impressive features of Anaconda, including built in binaries, command line interface, the history of the distribution, and why it is the right choice for just about every Python stack. This talk does not assume audience familiarity with the distribution. We will take advantage of the *better* batteries included nature of this distribution to step through beginner and intermediate concepts. I intend for the audience to feel comfortable and excited to give this a try on their own.
The 2017 hurricane season is proving to be one of the strongest in history, and predictive modeling plays an important role in evacuation and mitigation planning. Coastal communities in the path of hurricanes face several major hazards - strong winds, heavy rainfall, relentless waves, and storm surge. Storm surge is a type of transient sea level rise where water is forced towards the shore by winds, and the right conditions can produce very high levels - Hurricane Harvey raised Galveston Bay by upwards of ten feet, and in 2012 Hurricane Sandy produced 12-foot surge in Lower Manhattan. I'll discuss the current state of storm surge modeling with focus on an open-source package called GeoClaw, developed by academic researchers across the U.S. GeoClaw uses Python and Fortran to run a dynamic simulation of coastal flooding using storm and topography datasets, and thanks to some novel dimensionality reduction it can be run on a laptop.
I've been doing a bunch of analysis on the recent FCC public comments in Python (https://medium.com/@csinchok/an-analysis-of-the-anti-title-ii-bots-463f184829bc). Due to this work, I was quoted in Gizmodo, Ars Technica, and the BBC. I'd like to talk about how I approached this problem, how Python helped make sense of my findings, and what my conclusions are.
Description: a pythonic tour of time series methodologies and packages, including ARIMA, seasonal models, and Markov approaches. Intermediate level with basic statistics and time data familiarity required. Bio: Jonathan Balaban is a senior data scientist, strategy consultant, and entrepreneur with ten years of private, public, and philanthropic experience. He currently teaches business professionals and leaders the art of impact-focused, practical data science at Metis.
We've all used context managers provided by the Python Standard Library to read from/write to a file. Have you ever wondered what was happening underneath the hood when you used a with statement? This talk will explore context managers, discuss various use cases, and show you how to implement a context manager to manage MongoDB connections.
Have you ever tried to make something with scrap wood, and wondered how to use it optimally? Do have a bunch of pickles and jams you made, and you want to eat them in an order that maximizes variety? These are real problems a co-worker of mine had, and we used Python to solve them. I'll show the data we started with, the solutions we came up with, and a bit of the computer science behind them. See some examples of how to think through problems and design your own algorithms to solve them.