Nate Silver Predicts That You, Too, Can Be A Data Master

The Web's greatest prognosticator believes that you--yes, you--can learn to be data literate, if you're willing to get your hands a little dirty.

Everybody has a crush on Nate Silver.

The statistician-author-blogger calls elections and awards with titillating accuracy--and from what he's said before, it's mostly gut feeling.

Thankfully HBR.org had the guts to ask him why. So let's learn how to have the stomach ourselves.

To get data literate quick, first get dirty.

Silver's thoughts on getting good with data echo back to earlier findings on unstoppability: It's about getting as experienced, rather than knowledgeable, as quickly as possible.

The best training will almost always be hands-on, he says:

My experience is all working with baseball data, or learning game theory because you want to be better at poker, right? Or [you] want to build better election models because you’re curious and you think the current products out there aren’t as strong as they could be. So getting your hands dirty with the data set is, I think, far and away better than spending too much time doing reading and so forth.

The education doesn't need to be formal.

You don't need to go back to school, Silver says. But the path of the autodidact isn't purely alone, he says: If you want to learn analytics well, you need to find a statistical spirit guide, "some type of mentor who you talk to now and then."

Why this versus the institutional route? Because, Silver says, the toughest part of growing your data capabilities is honing in on the intuition about what the big questions to ask are--which, to borrow a poker metaphor, requires playing as many hands as possible.

Which is what some companies, in fact, are doing. As Silver told us last year:

I talked at an investment fund recently. Since they know there's a lot of noise in stock market data, they actually have their employees play a lot of poker. There's also a lot of randomness and luck in poker, but at least it gets to the long run a little bit faster. So developing an intuitive sense--one that is honed and refined through experience--for what's meaningful and when you've gotten enough data to say, This represents a change in my business environment, or equally important, to say, This doesn't.

Getting the opportunity to gain that experience.

The question for us skill-hungry careerists, then, is how to get a seat at the poker table. If we're at a company that has analytics nestled somewhere within it, we could opt to cross-pollinate with them--which is a big predictor of success. If not, we can find the data wherever it may lie--for instance, on your company site or even on your personal blog--and work with it every day.

Hat tip: HBR.org

[Image: Flickr user Thomas Leuthard]

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