Think Like a Data Scientist,
in early access now. The first chapter is free to download.
Tell me what you think!
There's not a huge difference between fiction and data science. They both take roundabout paths strewn with dialectic encounters and flowery—if incomprehenisble—language, but they both are capable of leading us to truths that nothing else can find.
If you stumble sideways into one of the great truths of fiction or data science, it's not because you're lucky; it's because your preparation taught you how to recognize something meaningful. That doesn't happen if you don't know the canon. I take pride in knowing my Anna Karenina and my Bayesian Data Analysis.
Motto of the moment: Lots of people are good at telling you things that you can do with data science, but I want to tell you what you should do.
I'm what you might call a "full stack" data scientist, but I don't think anyone should be employed as such. You don't want one guy doing the UI and the machine learning. Sure, we can do both, but one of them won't look good.
Data sets are rife
with errors, problems, and lies,
but don't make one up.
PhD in applied mathematics from the Technical University of Vienna. Visit my publications page to see some of my research.
I'm always trying to think of something clever. Hopefully I'll have something to show for it soon, fiction or non. For now, here's an old blog post of mine on special relativity.