I talk with Sam Moss and Cameron Weinert about using data science to predict college football. We talk about feature engineering, following your passing, the role of analytics in sports and sports fandom, how to be an intelligent consumer of data science as a non-data scientist, and a lot more. 

Everyday is Saturday on Spotify. 

Everyday is Saturday on Apple. 

Sam on Twitter. 

Raw data on college football here at collegefootballdata.com

We talk with Marlene Mhangami, a director and chair of the Python Software Foundation, co-founder of coding education non-profit ZimboPy, someone that took a huge career pivot from pre-med to software engineering, and one of the folks that helped bring RAPIDS to Windows. We talk about changing careers, creativity and confidence in tech, and of course RAPIDS on Windows. 

Marlene's home page

Marlene's blog post about RAPIDS on Windows

Tutorial on using RAPIDS on Windows via WSL2

ZimboPy on github

This week we’re joined by Even Oldridge, Senior Manager, RecSys Platform Team at NVIDIA. We talk about Tabular Deep Learning, NVMerlin, how bookstores aren’t like recommender systems, his team’s recent repeat win in the ACM Recsys Challenge, the future of recommender systems and more.

NVIDIA Merlin on the NVIDIA Developer Blog

https://developer.nvidia.com/blog/tag/merlin/

NVIDIA Merlin blogs on Medium

https://medium.com/nvidia-merlin

Merlin on Github

https://github.com/NVIDIA-Merlin/Merlin

NVTabular Blogs

https://developer.nvidia.com/blog/tag/nvtabular/

NVTabular on Github

https://github.com/NVIDIA/NVTabular

REES46 data set mentioned toward the end of the podcast

https://rees46.com/en/datasets

We talk with 4-time Kaggle winner Christof Henkel about how he got started in Kaggle, important skills for Kaggle success, his most memorable contests, his most recent victory, how an alien radio signal is like a bird call, climbing at the 2021 Olympics, and much more! 

Christof's Kaggle Profile: https://www.kaggle.com/christofhenkel

Christof's Twitter: https://twitter.com/kagglingdieter

We sit down and talk with 4x Kaggle Grandmaster Chris Deotte about his career, how he got started doing Kaggle, how you can get started doing Kaggle, feature engineering, the perks of AGI, and a lot more! 

Chris on Kaggle: https://www.kaggle.com/cdeotte

I sit down and talk with the new Director of Engineering for RAPIDS at NVIDIA, John Zedlewski about what economics can learn from machine learning practitioners, engineering challenges that ended up being harder than first thought, how increased automation will change the day-to-day work of data scientists, and much more. 

I talk with Zahra Ronaghi, Engineering Manager of AI Infrastructure at NVIDIA and Christoph Keller, Atmospheric Chemist with the NASA Goddard Space Flight Center about their collaboration to bring GPU-accelerated data science to the study of air pollution.

You can find their first blog on the collaboration here and their work around the atmospheric impact of COVID here.

For more about GPU accelerated shape this blog is a good place to start.

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

This week I talk with John Murray. John has been a data scientist, a CTO, and a professor and has unique insight on the history of data science and where it is going. It’s a great episode and I hope you’ll enjoy!

Links described in the episode:

MurrayData on Github

Fusion Data Science

John’s GTC 2020 talk on flood relief

Our guest this week in the one and only Wes McKinney, creator of Pandas and Apache Arrow. We have a great conversation about his career journey, funding and maintaining open-source software projects, his new company Ursa Computing, how Pandas grew from a passion project to the lingua franca of Python data science, and a lot more.