Note: In this post, I assume some familiarity with PyMC. If you need to get up to speed in a hurry and you're familiar with linear regression, go here for a tutorial. Alternatively, you can read for the methodological intuition,... Continue Reading →
Contextualizing My Vendetta I've been on a streak of bad flights lately. The last two, in particular, were horrible -- and not horrible in the standard "cramped seats/rubbery food/my-God-that-smell" way. Horrible due to (A) an unexplained cancellation, which turned my 12-hour... Continue Reading →
I. I recently wandered down a Google Black Hole on The Skills Gap, hoping to see if economists had come to a consensus on whether it was a real thing or Convenient-sounding Story. While there, I found an interview that... Continue Reading →
I. Upon completing college, I joined the Treasury Department. (No, I didn’t.) But I did help them bolster the once-proud, then-crumbling pillars of the American Economy: too-big-to-fail banks and car companies. Concretely, I bought a Ford Focus. I’d read Consumer... Continue Reading →
And now for something completely different This is (nominally) a Data Science blog, but I do have other interests. One of those other interests is music, and now that I have a platform for forcing my opinion onto others, I... Continue Reading →
Warning: This is a love story between a man and his Python module As I mentioned previously, one of the most powerful concepts I've really learned at Zipfian has been Bayesian inference using PyMC. PyMC is currently my favorite library... Continue Reading →
The Point of this Post: To Document an Example In this update, we'll cover reading data into a pandas DataFrame, Seaborn, creating multi-plot figures with matplotlib.pyplot.subplots(), LaTeX labeling, and parameterizing Gamma distributions using SciPy. I've been sitting on this example... Continue Reading →
Dan Saber 