Expert Briefings | November 21-29th

Before the main PyData Global conference, the committee runs an Expert Briefings series- these are presented by individuals who are key contributors to the PyData community and have extensive experience in their respective domains. It is in the format of a short presentation (15 mins) on the state of the art in their area of expertise, and a discussion session afterward. Session info below:

Huda Nassar

Monday 28 November 3pm UTC

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Understanding problems in a highly connected world via graph analytics

This talk will cover the following topics: (1) graphs that appear in everyday life and why approaching them with the graph analytics toolbox is important, (2) recent trendy ideas in the graph analytics research field, and (3) tools and software often used to address these problems.

Thomas Wiecki

Tuesday 29 November 2pm UTC

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The State of the Art for Probabilistic Programming

Probabilistic Programming as a field is moving at breakneck speed, with innovations being driven on all levels: language, algorithms, compilers, computation, hardware. In this expert briefing I will give a brief overview of where the field is today and where it is headed. One big trend is what I call The Great Decoupling: rather than monolithic PPL systems, we are seeing how various layers of abstraction are introduced and separated. This allows more interoperability, as well as innovation to occur at every level of the stack. Finally, I will talk about a convergence of Bayesian modeling and Causal Inference to a new paradigm called Bayesian Causal Inference.

Ian Ozsvald

Wednesday 23 November 4pm UTC

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The State of Higher Performance Python

We’ll review the state of the art in the data science world for common number-crunching tasks on small to big data. Topics we’ll cover include profiling, compilation and data manipulation. We’ll also review the near future for Python, Numba, Pandas, Dask and Polars and I’ll help you make some pragmatic choices about tools you might invest time in. We’ll have plenty of time to discuss your use cases and problems you might have encountered.

Sole Galli

Thursday 24 November 3pm UTC

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Feature engineering for machine learning with open-source

Feature engineering is the process of transforming variables, and extracting and creating new features from data, to train machine learning models. Data in its original format is almost never used to train machine learning models right away. Instead, data scientists devote a huge part of their time to data pre-processing.

Marco Bonzanini

Thursday 24 November 5pm UTC

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Natural Language Processing: Trends, Challenges and Opportunities

A short presentation on the state of the art in Natural Language Processing, followed by a Q&A / round table discussion where you’ll have the opportunity to ask your burning questions on NLP.