Data Science Salon Podcast

Probabilistic Thinking with James "JD" Long

Episode Summary

Our show host and Senior Content Advisor, Q McCallum, has been thinking a lot about what he calls "moving beyond the point estimate" in ML modeling. That usually starts with seeing the world in terms of statistical distributions, and running simulations to get a more robust picture of a model's results. When he had questions, he reached out to his old friend James "JD" Long for answers. James is a self-described "agricultural economist, quant, stochastic modeler, and cocktail party host" who does a lot of work in R, Python, and AWS. Through his work in the reinsurance field he has developed deep knowledge of simulations and probabilistic thinking, as well as an ability to explain these topics in plain language.

Episode Notes

In this episode, James and Q explore:

• The ideas of risk and uncertainty.
• What is "probabilistic thinking" and why is it important for data scientists?
• The career progression of a data analyst, and what it means to develop statistical acumen.
• Thinking in terms of distributions, and thinking in different moments of a distribution.
• Seeing BI, AI, and simulation in terms of punctuation.  (No, seriously.)
• How to bridge the gap into thinking probabilistically

And, just a reminder: James only speaks for himself in this episode and he does not represent his employer.Links mentioned during our discussion:

• You can find James on Twitter at @cmastication and on LinkedIn at https://www.linkedin.com/in/jamesdlong/
• R Cookbook, 2nd Edition (which James co-authored)
• RenRe's open roles: https://bit.ly/renrejobs
• Q's blog posts on "punctuation in data":  Periods and Question Marks (BI and AI), and  then Ellipses (simulation)

The list of books James mentioned:

• Thinking in Bets (Annie Duke)
• Fortune's Formula (Poundstone)
• The Lady Tasting Tea (Salsburg)
• Fooled by Randomness (Taleb)