Conference Schedule

View past PyData event schedules here.

Tutorial Sessions — Tuesday Dec. 3, 2019

	Tutorial Track 1	Tutorial Track 2	Tutorial Track 3
8:00 AM	Breakfast & Registration
9:00 AM	Computer Vision with PyTorch Daniel J. Brooks	Introduction to H2O AutoML with Python Franklin Velasquez	Introduction to Data Analysis with Python datatable Ana Castro Salazar, Pasha Stetsenko
10:30 AM	Break
11:00 AM	Kedro + MLflow – Reproducible and versioned data pipelines at scale Tom Goldenberg	Web Scraping w BeautifulSoup & Yelp's API Juan S Vasquez	Git-ting along with others Paul Anzel
12:30 PM	Lunch
1:30 PM	Reinforcement Learning: Pac-Man Manu Gopinathan, Malte Loller-Andersen	Experimental Machine Learning with HoloViz and PyTorch in Jupyterlab Hayley Song	Code Sprints
3:00 PM	Coffee Break
3:30 PM	Analyzing genetic networks using neural networks Manu Flores	Turn Python Scripts into Beautiful ML Tools Adrien Treuille	Code Sprints
5:00 PM

General Sessions — Wednesday Dec. 4, 2019

	Plenary Room	Track 2 Room	Track 3 Room
8:00 AM	Breakfast & Registration
9:00 AM	Opening Notes
9:15 AM	Keynote Milana Lewis
10:00 AM	Break
10:15 AM	Building a Data Driven Organization Franklin Sarkett	Ahead in the Clouds: How to get started with serverless on Google, Amazon & Microsoft Michelle Brenner	Bokeh Maps: Making an interactive map for your next web application Kevin Chrzanowski
11:00 AM	Break
11:15 AM	Making data relevant to business. Its harder than you think! Ravin Kumar	A "Supremely" Light introduction to Quantum Computing Rodolfo Bonnin	Gradient Boosting for data with both numerical and text features Anna Veronika Dorogush
12:00 PM	Lunch
1:00 PM	Write the Docs! Hareem Naveed	Introducing Autoimpute: a Python Package for Grappling with Missing Data Joseph Kearney, Shahid Barkat	Learning Topology: Topological Techniques for Unsupervised Learning Leland McInnes
1:45 PM	Break
2:00 PM	A/B Testing in Python Raul Maldonado	Data and ETL with Notebooks in Papermill Matthew Seal	A Guide to Modern Hyperparameter Tuning Algorithms Richard Liaw
2:45 PM	Coffee & Sponsor Break
3:15 PM	What's Data Science Reporting? Amy Tzu-Yu Chen	To Production and Beyond: How to Manage the Machine Learning Lifecycle with MLflow Amanda Moran	Evaluation of Traditional and Novel Feature Selection Approaches Ben Fowler
4:00 PM	Break
4:15 PM	Lightning Talks (Sign-ups)
5:00 PM	Keynote Kyle Polich
6:00 PM	Social Event
8:00 PM

General Sessions — Thursday Dec. 5, 2019

	Plenary Room	Track 2 Room	Track 3 Room
8:00 AM	Breakfast & Registration / Speed Mentoring
9:00 AM	Opening Notes
9:05 AM	Keynote Carol Willing
9:50 AM	Break
10:00 AM		Tackling Homelessness with Open Data Eric Busboom	Building Named Entity Recognition Models Efficiently using NERDS Sujit Pal
10:45 AM	Break
10:50 AM	Dynamic programming for machine learning: Hidden Markov Models Avik Das	Preparing messy data for supervised learning with vtreat John Mount, Nina Zumel	Build an AI-powered Pet Detector in Visual Studio Code Jeffrey Mew
11:35 AM	Break
11:40 AM	RAPIDS: Open Source GPU Data Science Brad Rees	Accelerate your NumPy Data Science Workloads and Deep Learning Applications Hao Jin	What You Got Is What You Got James Powell
12:25 PM	Lunch
1:30 PM	Machine Learning Model Evaluation Metrics Maria Khalusova	Fitting Many Dimensions into One: The Promise of Hierarchical Indices for Data Beyond Two Dimensions Christopher Ariza	MAP all the things John Healy
2:15 PM	Break
2:20 PM	Simplicity For Scale: Analyzing 15 Million DNA Samples With Python Tim Orme	Datasets and machine learning models versioning using open source tools Dmitry Petrov	AI meets Fashion for product retrieval with multi-modally generated data Ivona Tautkute
3:05 PM	Coffee & Sponsor Break
3:20 PM	IBM Code and Response: Open Sourcing Natural Disaster Preparedness and Relief Nick Acosta	Open Source is Better Together: GPU Python Libraries Unite Dante Gama Dessavre	Modeling Search Term Revenue: Using Embedding Layers to Manage High Cardinality Categorical Data Fletcher Riehl
4:05 PM	Break
4:15 PM	Keynote Sameer Singh
5:00 PM	Closing Notes
5:45 PM

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