Eyal Kazin
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 quantifying and communicating the impact of interventions to non-specialist audiences in an accessible manner.
My claim for fame is between 2004-2014 living in four different continents within a span of a decade, including three tennis Grand Slam cities (NYC, Melbourne, London).
Sessions
Correlation does not imply causation. It turns out, however, that with some simple ingenious tricks one can unveil causal relationships within standard observational data, without having to resort to expensive randomised control trials. Learn how to make the most out of your data, avoid misinterpretation pitfalls and draw more meaningful conclusions by adding causal inference to your toolbox.
Lightning Talks are short 5-10 minute sessions presented by community members on a variety of interesting topics.
In hypothesis testing the stopping criterion for data collection is a non-trivial question that puzzles many analysts. This is especially true with sequential testing where demands for quick results may lead to biassed ones.
I show how the belief that Bayesian approaches magically resolve this issue is misleading and how to obtain reliable outcomes by focusing on sample precision as a goal.
P-values are the most (mis)used and abused tool for quantifying statistical significance in hypothesis testing. In this lightening talk I highlight the virtues and vices of this Frequentist metric and suggest improved Bayesian alternatives.
Raising a child, and especially your first, means dealing with many unknowns. I explore usage of data collected in an app to make life a bit more predictable. Especially sleep!