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How to nab a data scientist job

It's a whole new ballgame for traditional data analysts, as training focuses on deep knowledge of statistics and computer science.

By Julia King, Computerworld
May 06, 2013 06:43 AM ET

Computerworld - Ask a dozen CIOs what tops their list of strategic priorities and odds are exceedingly good that "big data" ranks either first or second. One of the greatest challenges, they'll tell you, is finding the talent they need to analyze and wring business value from the ever-increasing volume of complex data flooding their enterprises. What they need, they say, are good data scientists -- and lots of them.

In one of the most frequently cited reports on the topic, the McKinsey Global Institute estimates that there will be a shortfall of 190,000 data scientists in the IT job market by 2018.

But how exactly do you become one of these in-demand big data specialists? Is it a matter of training, certification or both? Is it simply the next logical career step for a traditional business intelligence expert? Is a computer science degree required?

As it turns out, there is no one right answer, at least not at the moment. Instead, it's largely a scramble out there on the big data field.

"Big data is like a kids' soccer game. Everyone is running to the ball, but no one knows exactly what to do with it. It has created a huge competition for people," says Greg Meyers, CIO at Biogen Idec in Weston, Mass.

"It's a very fluid area," agrees Michael Rappa, executive director of the Institute for Advanced Analytics at North Carolina State University. "Depending on what industry you're in or what company you talk to, it's a different reality when you talk about big data."

While a single definition might be elusive, academic, career and business experts agree that there are certain fundamental tasks that all data scientists need to perform and certain skills that are required to perform them well. The main pillars of the discipline are data clustering, data correlation, data classification and anomaly detection.

Or, as Rob Bird, a data scientist and CTO at Red Lambda, a provider of predictive security analytics, puts it, "You make data simpler, find relationships, find the weird stuff, and then make predictions."

Tech Glossary

Data Science vs. Business Intelligence: What's the Difference?

The terms "data science" and "business intelligence" seem to be used a lot in connection with big data, but they're really very different disciplines. Experts say data science is all about predicting the future, while BI involves producing static reports.

"Traditional BI engineers are effectively reporting information as is, even if they're reporting trends and standard deviations away from the norm," says Andrew Dempsey, director of DVD BI and analytics at Netflix. "They aren't really discovering new nuggets of information. The data is what it is."

But with data science, there's an element of mystery. For example, Netflix looks at historical data "to identify why someone is more or less likely to churn because of their behavior," Dempsey explains. "There's more uncertainty there because on an aggregate level, a lot of people may have similar viewing habits, but on an individual level, everyone is different."

Originally published on www.computerworld.com. Click here to read the original story.

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