Conference Schedule

View past PyData event schedules here.

General Sessions — Monday Nov. 27, 2017

  Music Box 5411/Winter Garden 5412 (5th fl) Central Park West 6501 (6th fl) Central Park East 6501a (6th fl) Radio City 6604 (6th fl) Breakout Track Belasco 6203 / Ambassador 6202
8:00 AM

Breakfast
9:00 AM

Opening Notes - Central Park E/W 6501 (6th fl)
9:15 AM Keynote - Enabling Real Time Analysis & Decision Making - A Paradigm Shift for Experimental Science Kerstin Kleese van Dam
10:00 AM Understanding NBA Foul Calls with Python Austin Rochford JuliaDB: A data system for Julia Shashi Gowda, Jeff Bezanson A Worked Example of Using Neural Networks for Time Series Prediction Joe Jevnik Diamond: mixed-effects models in Python Timothy Sweetser

 
10:40 AM

Break
10:50 AM A Laud to Being Reactive Michael Walters Well-Aged Bacon: predicting Kevin Bacon's age with the Python Cognitive Services API Paige Bailey R for Pythonistas Christopher Roach Neural Networks for Segmentation of Vocalizations David Nicholson, Yarden Cohen

 
11:30 AM sklearn-Compatible Model Stacking Keith Myers-Crum Diving into the deep end of clothing styles Ethan Rosenthal Git Risky: Using git metadata to predict code bug risk J. Henry Hinnefeld Why Two Sigma Contributes to Open Source Julia Meinwald
12:10 PM

Lunch
1:10 PM What am I even looking at? Nick Acosta Dash Chris Parmer PyData Pop Quiz James Powell (Sponsored talk) Python on Windows & Azure: Ask Us Anything! Heather Shapiro, Steve Dower
1:55 PM Recipe2Vec: How word2vec helped us discover related Tasty recipes Meghan Heintz Party Planning with Python Jessica Forde Addressing Prejudice in Text Data Mike Cunha It's About Time ! Alex Samuel
2:45 PM Streaming Processing with Dask Matthew Rocklin Managing Python at scale without breaking the bank Misha Tselman Bookworm: Social Networks From Novels Harrison Pim The wondrous world of data science MOOCs Ashwin Menon

 
3:25 PM

Break
3:40 PM Replacing Hadoop with Your Laptop: The Case for Multiprocessing Vicki Boykis Find the Farm (Data Science Insights into Real Estate Pricing) en zyme What looks good with my sofa ? Ivona Tautkute Why your relationship is likely to last, or not: Local Interpretable Model-Agnostic Explanations Friederike Schuur

 
4:20 PM Improving Pandas and PySpark performance and interoperability with Apache Arrow Li Jin Bayesian inference in computational chemistry. Chaya D. Stern Random Forests: Best Practices for the Business World Gabby Shklovsky How to Train Your Classifier: Create a Serverless Machine Learning System with AWS and scikit-learn Stuart Myles, David Fox, Veronika Zielinska

 
5:00 PM

Lightning Talks
6:00 PM

General Sessions — Tuesday Nov. 28, 2017

  Music Box 5411/Winter Garden 5412 (5th fl) Central Park West 6501 (6th fl) Central Park East 6501a (6th fl) Radio City 6604 (6th fl) Breakout Track Belasco 6203 / Ambassador 6202
8:00 AM

Breakfast
9:00 AM Keynote Presentation Thomas Sargent
9:45 AM Jupyter, R Shiny, and the Data Science Web App Landscape Keith Ingersoll The law and ethics of data-driven artificial intelligence Aileen Nielsen Workflow Systems: Simple & Complex Titus von Koeller Analyzing Petabytes of Earth Science Data with Jupyter and Earth Engine Tyler A. Erickson

 
10:25 AM

Break
10:45 AM Getting Scikit-Learn To Run On Top Of Pandas Ami Tavory bqplot - Interactive Visualization in Jupyter Dhruv Madeka, Srinivas Sunkara Asynchronous Python: A Gentle Introduction James Kirk Cropcho Data Science Keys to Open Up OpenNASA Datasets Noemi Derzsy
11:20 AM Changing the World with Data, Combatting human trafficking as an example. Eric Schles BeakerX: Beaker Extensions for Jupyter Tiezheng Li Learning in Cycles: Implementing Sustainable Machine Learning Models in Production Andrew Therriault Simplifying And Accelerating Data Access for Python With Dremio and Apache Arrow Sudheesh Katkam

 
12:00 PM

Lunch
1:00 PM Keynote Andrew Gelman
1:45 PM What is the Future of Pandas Jeff Reback Analyst’s Nightmare or Laundering Massive Spreadsheets Feyzi Bagirov Time Series Forecasting using Statistical and Machine Learning Models Jeffrey Yau There's a Sign for That! Heather Shapiro
2:25 PM Turning PyMC3 into scikit-learn Nicole Carlson Wrangling and Evaluating Financial Datasets Philip Brittan Free Lunch With NYC Analytics: optimizing school lunch programs with Python Simon Rimmele Money for Nothing: Introducing Pennies, an Open-Source Pythonic Pricing Package Casey Clements

 
3:05 PM Reverse image search engines using out-of-the-box machine learning libraries Leon Yin, yvan State of NumFOCUS: A year in review and a look forward at 2018 Leah Silen, Gina Helfrich, James Powell An Attempt At Demystifying Bayesian Deep Learning Eric J. Ma Why does Python need security transparency? Steve Dower

 
4:30 PM

Capital One networking reception at the Sky Room
6:30 PM

Tutorial Sessions — Wednesday Nov. 29, 2017

  Music Box 5411/Winter Garden 5412 (5th fl) Central Park West 6501 (6th fl) Central Park East 6501a (6th fl) Radio City 6604 (6th fl)
8:00 AM

Breakfast

DISC
9:00 AM Using Python and Astropy for Astronomical Data Analysis Kelle Cruz, Adrian Price-Whelan Develop Interactive Matplotlib figures Thomas A Caswell Building Serverless Microservices On AWS Using Python Srini Karlekar
10:30 AM

Break
11:00 AM Using Python and Astropy for Astronomical Data Analysis (continued) Kelle Cruz, Adrian Price-Whelan Develop Interactive Matplotlib figures (continued) Thomas A Caswell An Introduction to F# for data Jamie Dixon
12:30 PM

Lunch
1:30 PM Pandas: .head() to .tail() Tom Augspurger Shogun.ML Viktor Gal Network Science, Game of Thrones and US Airports Mridul Seth
3:00 PM

Break
3:30 PM Pandas: .head() to .tail() (continued) Tom Augspurger Two views on regression with PyMC3 and scikit-learn Colin Carroll Bridging .NET with PyData Denis Akhiyarov
5:00 PM

Tutorial Sessions — Thursday Nov. 30, 2017

  Music Box 5411/Winter Garden 5412 (5th fl) Central Park West 6501 (6th fl) Central Park East 6501a (6th fl) Radio City 6604 (6th fl)
8:00 AM

Breakfast

DISC
9:00 AM Idiomatic Pandas Ted Petrou Text generation using Neural Networks with the Keras framework Kirit Thadaka Introduction to Julia Jeff Bezanson, Stefan Karpinski
10:30 AM

Break
11:00 AM Idiomatic Pandas (continued) Ted Petrou Text Analysis with SpaCy and Scikit-Learn Jonathan Reeve Introduction to Julia (continued) Jeff Bezanson, Stefan Karpinski
12:30 PM

Lunch
1:30 PM pomegranate: fast and flexible probabilistic modeling in python Jacob Schreiber Bayesian Statistics from Scratch: Building up to MCMC Justin Bozonier Deep Learning from Scratch using Python Seth Weidman
3:00 PM

Break
3:30 PM Stan: Bayesian Modeling and Inference Made Easy Bob Carpenter Top-To-Bottom, Line-By-Line James Powell Loan Default Modeling in Python: An Introductory Guide Lore Dirick
5:00 PM

Closing Notes
5:15 PM

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