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Doug Henschen
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Big Data Talent War: 7 Ways To Win

53% of big data-focused companies say analytics experts will be tough to find for the next two years. Here's how IT leaders plan to train, borrow, or steal talent--and what job seekers should know.
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Tip 5: Plug Into College And University Programs
When big technology waves come along, as we've seen in the last five years with big data analytics, it takes about 10 years to train the next generation on the new skills that are needed, according to Jim Spohrer, IBM's director of global university programs. "We're in one of those 10-year cycles right now where we're shifting to create the next generation of graduates," he says.

Thus a new wave of graduates is just starting to emerge and will reach a steady stream by 2018. Spohrer tracks IBM's work with various colleges and universities, including financial support, research grants, software and technology donations, and recruiting. Among the institutions on Spohrer's short list of top analytics schools are North Carolina State University (NCSU), the University of Ottawa, Northwestern University, DePaul University and the University of Connecticut. At SAS, Jerry Oglesby, senior director of global academic programs, also cites NCSU and Northwestern as leaders, but he also lists Louisiana State University, Oklahoma State, Texas A&M, Texas Tech, California State University at Long Beach and the University of Alabama.

Genealogy website needs big data scientists who can work with Hadoop and write their own algorithms. To find employees who are hip to R statistical programming and MapReduce, the company is working with schools that have introduced course work or degree programs in machine learning. Scott Sorensen, senior VP of engineering, says institutions including Carnegie Mellon, California Polytechnic State University in San Luis Obispo and the University of California at Berkeley are among many that have stepped up their machine-learning programs.

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Tony Kontzer
Tony Kontzer,
User Rank: Apprentice
1/23/2013 | 8:26:14 PM
re: Big Data Talent War: 7 Ways To Win
I'm shocked to come to this story more than 2 months after it posted and see no comments here. This seems to me a valuable and insightful set of tips that can serve as a launch point for a discussion on how to best fill the data science talent gap. And I'm sure readers would be able to add more tips of their own--and maybe even offer counter-evidence questioning the effectiveness of one of the tips listed here. What say you, readers?

Tony Kontzer
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