My experience is at the intersection between academic innovation and it's
application in a commercial setting.
After active academic research in Neural Networks in the mid 90's, and teaching
Machine Learning (ML) and Information Theory in UK universities, I have
successfully founded three commercial concerns in the advanced computing
Thoughtful Technology, a data-science and ML consultancy with over twenty
successful projects in the last 10 years LifeQueue, a medical ML business which
generated a state of the art automated head and neck cancer diagnosis system
and Temporal Computing , an advanced hardware business using time-delays as
storage. Throughout this I have remained active in
research, becoming an international lead on temporal computing and an early
contributor to advanced explainable-AI methods. Recently, I was involved in
the COVID response, firstly, providing load prediction for the NHS-login
service, a single sign-in for many NHS services,secondly creating a graph
based search engine for a leading COVID academic corpus, and finally as a lead data scientist in the central NHS data team.
Polars is a next generation data-frame library which aims to be fast, efficient, composable and lazy! This introductory tutorial will take you through the basics of getting started with polars in Python. We will demonstrate the out the box multi-core efficiencies, by composing advanced filters and joins, before comparing with the traditional pandas workflows. As a finale we will look at some lazy processing when applying polars to large scale data-sets.