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Statistical inference

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Original author: 
Matthew Francis

Nobody knows what the mathematician Rev. Thomas Bayes looked like, but this is the picture everyone uses. The equation is Bayes' theorem.

Nate Silver, baseball statistician turned political analyst, gained a lot of attention during the 2012 United States elections when he successfully predicted the outcome of the presidential vote in all 50 states. The reason for his success was a statistical method called Bayesian inference, a powerful technique that builds on prior knowledge to estimate the probability of a given event happening.

Bayesian inference grew out of Bayes' theorem, a mathematical result from English clergyman Thomas Bayes, published two years after his death in 1761. In honor of the 250th anniversary of this publication, Bradley Efron examined the question of why Bayes' theorem is not more widely used—and why its use remains controversial among many scientists and statisticians. As he pointed out, the problem lies with blind use of the theorem, in cases where prior knowledge is unavailable or unreliable.

As is often the case, the theorem ascribed to Bayes predates him, and Bayesian inference is more general than what the good reverend worked out in his spare time. However, Bayes' posthumous paper was an important step in the development of probability theory, and so we'll stick with using his name.

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lurkerbelow is the only developer at his company writing unit tests. Management, developers, everyone says they want to write unit tests, but nobody does. To bring developers into line, lurkerbelow has introduced pre-commit code review (Gerrit) and continuous integration (Jenkins). Not working. "How do I motivate my fellow coworkers to write unit tests?" he asks.

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Thomas H. Davenport and D.J. Patil give the rundown on what a data scientist is, what to look for and how to hire them. It's an article in Harvard Business Review, so it's geared towards managers, and I felt like I was reading a horoscope at times, but there are some interesting tidbits in there.

Data scientists don’t do well on a short leash. They should have the freedom to experiment and explore possibilities. That said, they need close relationships with the rest of the business. The most important ties for them to forge are with executives in charge of products and services rather than with people overseeing business functions. As the story of Jonathan Goldman illustrates, their greatest opportunity to add value is not in creating reports or presentations for senior executives but in innovating with customer-facing products and processes.

I still call myself a statistician. The main difference between data scientist and statistician seems to be programming skills, but if you're doing statistics without code, I'm not sure what you're doing (other than theory).

Update: This recent panel from DataGotham also discusses the data scientist hiring process. [Thanks, Drew]

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