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Bayes is Bae

Richard Schneeman (@schneems (https://twitter.com/schneems))

Before programming, before formal probability there was Bayes. He introduced the notion that multiple uncertain estimates which are related could be combined to form a more certain estimate. It turns out that this extremely simple idea has a profound impact on how we write programs and how we can think about life. The applications range from machine learning and robotics to determining cancer treatments. In this talk we'll take an in depth look at Bayes' rule and how it can be applied to solve problems in programming and beyond.

Linking the world's data to solve data science's first mile problem

Jon Loyens (@jonloyens (https://twitter.com/jonloyens))

The “first mile” on the road to solving any data problem is, curiously, the longest mile. We spend far too much time and energy searching high and low for the “right” datasets, wrangling them into usability, and loading them into rigid systems held together by little more than digital duct tape and baling wire.

But what if, amidst the chaos of the first mile, we rush too quickly past two of the most important features of the data landscape, context and provenance?

While the problem is nearly universal, it is far from insurmountable. In fact, the solution relies entirely on existing technologies. The trick is to combine them in a way that allows people who work with data to cruise through the most frustrating stretches of the first mile while providing a better route to familiarity with context and provenance.

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