Peter Norvig discusses mistakes people make when they perform A/B tests. Plenty of useful insights, I have made (or seen people make) at least a half of these mistakes. The best observation, I think, was about people confusing uniformity with randomness.
A terrific illustration of formatting tables with data for readability that argues the “less is more” principle:
Surprisingly few people I have met were familiar with the shrinkage idea, even the statistically literate ones. If you routinely estimate means of several groups of objects, this will likely come to be a surprising result for you. Here is a paper that describes how and why this estimate works, and when it may not the best available option to use.
A long article about contemporary software industry: What Is Code? It breaks down a lot of high-level concepts into easily digestable pieces, and should be required reading for anyone who wants to understand how the modern software industry works.
John Rauser, a fellow ex-Amazonian, shared a story from his Amazon years. It’s a pretty short read, and the critical piece of it is the attached paper by the member of the U.S. Atomic Energy Commission in 1953 that can be summarized with two sentences: In theory, there is no difference between theory and practice. But, in practice, there is.
A fantastic piece from the Google data science team concerning analysis of new data sets. I particularly like the advice of being both the skeptic and the champion of one’s analysis at the same time.
Two insightful posts titled “Machine Learning Meets Economics”: Part 1 and Part 2. They illustrate how one can use a good classifier for making better business decisions. In fact, one of the points these posts make is that sometimes you don’t even need a good classifier to be able to make it work for you.
An interactive SQL quiz with focus on analytical functions and other advanced features. Can be helpful for interview study.