PyData

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PyData is a gathering of users and developers of data analysis tools in Python. PyData aims to provide Python enthusiasts a place to share ideas and learn from each other about how best to apply our language and tools to ever-evolving challenges in the vast realm of data management, processing, analytics, and visualization. We aim to be an accessible, community-driven conference, with tutorials for novices, advanced topical workshops for practitioners, and opportunities for package developers and users to meet in person.

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PyData

Michal Mucha: Build and Deploy an End-to-end Streaming NLP Insight System | PyData London 2019

Unit testing data with marbles - Jane Stewart Adams, Leif Walsh

The TileDB Array Data Storage Manager - Stavros Papadopoulos, Jake Bolewski

Using Embeddings to Understand the Variance and Evolution of Data Science... - Maryam Jahanshahi

Deploying Data Science for Distribution of The New York Times - Anne Bauer

Graph Analytics - From the Whiteboard to Your Toolbox - Sam Lerma

Do Your Homework! Writing tests for Data Science and Stochastic Code - David Waterman

RESTful Machine Learning with Flask and TensorFlow Serving - Carlo Mazzaferro

Mining dockless bikeshare and dockless scootershare trip data - Stefanie Brodie, Kiana Roshan Zamir

Avoiding Bad Database Surprises: Simulation and Scalability - Steven Lott

Words in Space - Rebecca Bilbro

End-to-End Machine learning pipelines for Python driven organizations - Nick Harvey

Pydata beautiful soup - Monica Puerto

1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jeff Roach

Extending Pandas with Custom Types - Will Ayd

Measuring Model Fairness - Stephen Hoover

What's the Science in Data Science? - Skipper Seabold

Applying Statistical Modeling and Machine Learning to Perform Time-Series Forecasting

Solving very simple substitution ciphers algorithmically - Stephen Enright-Ward

The Face of Nanomaterials: Insightful Classification Using Deep Learning - Angelo Ziletti