Big data, big paycheck

If I had a 10 year old today (which to my knowledge I don’t) I’d encourage her to start learning statistics. Looking around, a good data scientist nowadays can make a fortune while solving fascinating problems. Some of our generation’s fastest growing technology companies – Google, Facebook, Twitter – are data companies at heart. Google with PageRank, Facebook with Social Graph, Twitter with Firehose, and so on. Everywhere you see a successful company, you see data.

But it’s not the data that’s valuable because most of it can, ultimately, be bought. The biggest challenge is creating the right algorithms to make sense of your data. Google created AdSense. Facebook created Facebook Ads. Twitter hasn’t figured it out yet, but I’m sure they will, provided that they find the right – you guessed it – data scientists.

The thing is that big data, as they call it these days, is such a new field that nobody really understands it. Ten years ago the only organisations who had access to big amounts of data were universities. These days, startups go from zero to millions of users and billions of data points in a matter of months. And most startups make very poor use of that data, if at all. Not because they don’t want to use it, but because there’s so much scarcity when it comes to people who actually know what they’re talking about.

So if you’re a techie, mathematician or geek who’s not sure which way to go, grab a statistics book and get going. Oh, and drop me an email as well because Brainient is hiring – you guessed it – a data scientist to work on some really interesting problems in the video advertising world.

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