**Talks**A lot of the public stuff I do is at conferences where it doesn't get recorded. However, some of my work has found its way online.

- Python for Data Science: Python is, IMHO, the best general-purpose programming language for data science. This talk gives some tips for how to get the most out of it.
- Relational Algebra and the Pig Language: This talk gives an overview of relational algebra, which is the theoretical underpinning for most modern databases and most Hadoop wrapper languages. It's cool stuff, and worth being familiar with if you want a deeper understanding of these tools. Wow, I can't believe that I used to work with Pig - I feel like a dinosaur!

**Articles**- A Stochastic Analysis of Hard Disks: I wrote this with people at CMU, and it calculates that average wait time for hard disks under certain assumptions. It turns out to be a very subtle problem; many previously published papers botched the math.
- An Elementary Derivation of Mean Wait Time in Polling Systems: This paper, which I only put on ArXiv, generalizes the previous one to general polling systems.
- Open-system thermodynamic analysis of DNA polymerase fidelity: Blast from the past! This was written back when I was at UW. I show the critical and under-appreciated role that thermodynamics plays in the low mutation rate of DNA when cells divide.

Finally, here is an article that I wrote about Big Data for IDG. A couple other people added to it, but I was the main author.

**Books****The Data Science Handbook**

I am currently in the final editing stages of The Data Science Handbook, to be published by Wiley & Sons. The book in a self-contained course in data science, unifying the required math, computer science, and business concepts in a single coherent discipline. Example code is all in Python, which I believe is the single best free data science tool around today.

**What is Math?**

My long-in-the-works book What is Math is now available on Kindle! It contains prettymuch everything I have to say about math, cognition and language, as well as awesome historical context and personal anecdotes. If you've enjoyed this blog or are interested in the human side of math, then I encourage you to check it out.

Having spent most of my life working with math in one form or another, I am convinced that curious people of all backgrounds could benefit from a novel take on the subject. There are a lot of mis-conceptions out there, in everybody from math-phobes to professional researchers. Even if you don't end up agreeing with my thesis, the book covers a fascinating range of topics, and I think there will be something new and exciting for everyone.