On this week’s episode, we have NVIDIA’s Head of Developer Relations, Data Science, Jim Scott. We talk about the data science of fine whiskey, data science for fitness, the “secret” of Kaggle Grand Masters (spoiler: it’s giving back to the community), learning and community resources as the future of data science, classic “paradoxes” in basic probability, and some great resources for being a better data scientist.

Kaggle Grandmaster Youtube Interviews – Here’s the most recent sit down Jim did with the Kaggle Grand Masters of NVIDIA. https://www.youtube.com/watch?v=bHuww-l_Sq0

Data Science of the Day – we talk about this toward the end of the episode, and this is a GREAT resource to keep up-to-date with everything going on in data science. https://forums.developer.nvidia.com/c/ai-data-science/data-science-of-the-day/323/none

Jim on Twitter: https://twitter.com/kingmesal

Jim and I reminisce about the Birthday Paradox – here’s a good piece on it from Scientific American. Jim and I were way off on remembering how likely birthday sharing is in a small handful of people. https://www.scientificamerican.com/article/bring-science-home-probability-birthday-paradox/

Don’t let us get your goat talking about the Monty Hall Problem. This explainer shows how an example with a larger number of doors can help give more intuition about what’s actually happening by changing your guess. https://www.statisticshowto.com/probability-and-statistics/monty-hall-problem/

Cantor’s Diagonalization Theorem mentioned in passing. Here’s a link to the wikipedia article – if you aren’t familiar with it, you should check it out. https://en.wikipedia.org/wiki/Cantor%27s_diagonal_argument