Eyal Kazin
👋 Hi I'm Eyal. My superpower is simplifying the complex and turning data to ta-da!
I'm an Ex-cosmologist turned data scientist with over 15 years experience in solving challenging problems. I am motivated by intellectual challenges, highly detail oriented and love visualising data results to communicate insights for better decisions within organisations.
My main drive as a data scientist is applying scientific approaches that result in practical and clear solutions. To accomplish these, I use whatever works, be it statistical/causal inference, machine/deep learning or optimisation algorithms. Being result driven I have a passion for facilitating stakeholders to make data driven decisions by quantifying and communicating the impact of interventions to non-specialist audiences in an accessible manner.
My claim for fame is that between 2004-2014 I lived in four different continents within a span of a decade, including three tennis Grand Slam cities (NYC, Melbourne, London).
Sessions
To apply or not to apply, that is the question.
Causal reasoning elevates predictive outcomes by shifting from “what happened” to “what would happen if”. Yet, implementing causality can be challenging or even infeasible in some contexts. This talk explores how the very act of assessing its applicability can add value to your projects. Through a gentle introduction to causal inference tools and practical use cases, you will learn how to bring greater scientific rigour to real-world problems.
Target audience: Practicing and aspiring data scientists, machine learning engineers, and analysts looking to improve their decision-making with causal inference.
No prior knowledge is assumed.
For the seasoned practitioners I hope to shine light on aspects that may not have been considered. 💡
Can't make the talk? Read all about it in my new TDS article: 🧠🧹 Causality — Mental Hygiene for Data Science