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:
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.
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.
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.
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.