Conference Schedule

View past PyData event schedules here.

Tutorial Sessions — Friday June 30, 2017

  A238 A130
8:30

Registration & Breakfast
9:00 Introduction to Data-Analysis with Pandas Alexander Hendorf Introduction to Julia for Scientific Computing and Data Science David Higgins, Robert Schwarz
10:30

Break
10:45 Pandas from the Inside / "Big Pandas" Stephen Simmons Leveling up your Jupyter notebook skills Gerrit Gruben
12:15

Lunch (Mensa in D Building downstairs from Registration) Menu and Details
13:45 Introductory tutorial on data exploration and statistical models Alexandru Agachi Advanced Metaphors in Coding with Python James Powell
15:15

Coffee
15:30 Introduction to Machine Learning with H2O and Python Jo-fai Chow The path between developing and serving machine learning models. Adrin Jalali
17:00 Topic Modelling (and more) with NLP framework Gensim Bhargav Srinivasa Desikan

 
18:30

General Sessions — Saturday July 1, 2017

  D105 Audimax A238 A208 A239
8:30

Registration & Breakfast (D Building)
9:30

Opening Notes
9:45

Keynote
Barbara Plank
10:45 Blockchains for Artificial Intelligence Trent McConaghy Finding Lane Lines for Self Driving Cars Ross Kippenbrock Gold standard data: lessons from the trenches Miroslav Batchkarov

Keynote Q/A
Barbara Plank
11:30 Data Science & Data Visualization in Python. How to harness power of Python for social good? Radovan Kavicky Patsy: The Lingua Franca to and from R Max Humber A word is worth a thousand pictures: Convolutional methods for text Tal Perry

 
12:15 On Bandits, Bayes, and swipes: gamification of search Stefan Otte Kickstarting projects with Cookiecutter Raphael Pierzina Evaluating Topic Models Matti Lyra
13:00

Lunch (D Building and outside A238) Menu and Details
14:30 “Which car fits my life?” - mobile.de’s approach to recommendations Florian Wilhelm, Arnab Dutta Patterns for Collaboration between Data Scientists And Software Engineers Karolina Alexiou Developments in Test-Driven Data Analysis Nick Radcliffe

 
15:15 Towards Pythonic Innovation in Recommender Systems Carlotta Schatten Introduction to Search Sirin Odrowski Size Matters! A/B Testing When Not Knowing Your Number of Trials Alexander Weiss Best Practices for Debugging Dr. Kristian Rother
16:00 Deep Learning for detection on a phone: how to stay sane and build a pipeline you can trust Irina Vidal Migallon Building smart IoT applications with Python and Spark Rafael Schultze-Kraft Is That a Duplicate Quora Question? Abhishek Thakur
16:45

Coffee
17:00

Lightning Talks (x10)
17:45

Closing Notes
18:00

Keynote
Verónica Valeros
19:00

General Sessions — Sunday July 2, 2017

  D105 Audimax A238 A208 A239
8:45

Breakfast (D Building) Menu and Details
9:15

Opening Notes
9:30

Keynote
Toby Walsh
10:30 Fairness and transparency in machine learning: Tools and techniques Andreas Dewes TNaaS - Tech Names as a Service Vincent D. Warmerdam Semi-Supervised Bootstrapping of Relationship Extractors with Distributional Semantics David Soares Batista

Keynote Q/A
Toby Walsh
11:15 Biases are bugs: algorithm fairness and machine learning ethics Françoise Provencher Polynomial Chaos: A technique for modeling uncertainty Emily Gorcenski Conversational AI: Building clever chatbots Tom Bocklisch

Keynote Q/A
Verónica Valeros
12:00 Data Analytics and the new European Privacy Legislation Amit Steinberg Fast Multidimensional Signal Processing using Julia with Shearlab.jl Héctor Andrade Loarca What does it all mean? - Compositional distributional semantics for modelling natural language Thomas Kober

 
12:45

Lunch (D Building and outside A238) Menu and Details
14:15

Ethics in Machine Learning Panel
15:15 Spying on my Network for a Day: Data Analysis for Networks. Aisha Bello

Hedging portfolios with Reinforcement Learning (CANCELLED)

 Analysing user comments on news articels with Doc2Vec and Machine Learning classification Robert Meyer

Panel Discussion
Q&A
16:00 AI assisted creativity Roelof Pieters When the grassroots grow stronger - 2017 through the eyes of German open data activists Ulrike Thalheim Find the text similiarity you need with the next generation of word embeddings in Gensim Lev Konstantinovskiy

 
16:45

Coffee
17:00 Data Science for Digital Humanities: Extracting meaning from Images and Text Hendrik Heuer Large Scale Vandalism Detection in Knowledge Bases Alexey Grigorev Where are we looking? Prediciting human gaze using deep networks. Oliver Eberle

 
17:45 Social Networks and Protest Participation: Evidence from 130 Million Twitter Users Jonathan Ronen Machine Learning to moderate ads in real world classified's business Vaibhav Singh, Jaroslaw Szymczak Engage the Hyper-Python - a rattle-through many of the ways you can make a Python program faster Daniele Rapati

 
18:30

Closing Notes
18:45

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