Eyal Kazin
I'm an Ex-cosmologist turned data scientist with 20 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 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.
In my free time I craft engaging articles on applied stats in data science and machine learning: https://medium.com/@eyal-kazin
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).
Session
Switch or stay, what do you say? And more importantly, why?
The Monty Hall Problem is a well-known brain teaser from which we can learn important lessons in decision making that are useful in general and in particular for data scientists.
If you are not familiar with this problem, prepare to be perplexed 🤯. If you are, I hope to shine light on aspects that you might not have considered 💡.
I introduce the problem and solve with three types of intuitions: Common, Bayesian and Causal. I summarise with a discussion on lessons learnt for better data decision making.