Amid all the talk of big-data, no doubt you come across the newfangled job title "data scientist." Various definitions, purposes, and forecasts of this line of work blanket headlines daily. I found this in-depth interview with data scientist consultant John Hooks helpful to identify what the role actually entails: big-data predictive analytics.
In other news, Information Management's trend no. 7 in "Big Data Talent War: 10 Analytics Job Trends" revealed that Scott Sorensen, Ancestry.com's Senior VP of Engineering, is beefing up his team with data scientists. Sorensen offers his definition of a data scientist as someone "skilled in taking a statistical approach to algorithm development." He says: "Many times they're statisticians, and they understand how to create statistical models that allow you to use massive amounts of data to develop algorithms."
Of course, as with anything new, there are obstacles. "One of the challenges our industry faces is we presume access to large amounts of data also comes with a button that says 'Insights,' " says Alexandra Drane in this AllAnalytics.com slideshow featuring nine data scientists. Will the data scientist ensure insights are found through appropriate technologies and (sometimes controversial) organizational change?
I asked the following six industry experts whether they thought organizations were moving toward emerging roles like the data scientist:
This blog is an excellent resource for those looking to get more depth around a hot topic. I hope my "day in the life" of a data scientist Job Interview on JobShadow.com and the other expert feedback here will help clarify the role and how it benefits clients and practitioners of predictive analytics alike. - @BigDataGuru on Twitter
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