This presentation was prepared for a talk on 2014.08.06 at the NYC Algorithmic Trading meetup (http://www.meetup.com/NYC-Algorithmic-Trading/events/197749772/)
Regardless of whether you call it "data science", "business intelligence", "analytics", "statistics" or just plain old "math", we have many tried and true techniques for dealing with uncertainty (particularly in quantitative finance). But ambiguity—what problem do we need to solve in the first place?—is a separate matter and, at least in my experience, is the hardest part of creating value from data. During this talk, I'll discuss how we address ambiguity by giving a guided tour of some of our client projects, such as how to reduce legal e-discovery costs by 99% (hint: supervised binary classification of text documents) or how to assemble project teams on emerging R&D opportunities in a multinational organization (hint: unsupervised classification of employee expertise).
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