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How to Nab a Data Scientist Job Computerworld, May 6

 As companies increase their efforts to find and hire Big Data talent, it is opening up new opportunities for IT workers with the ability to analyze the ever-increasing volume of complex data flooding the enterprise. For now, it's largely a scramble in the Big Data field, as most employers are mixed on the appropriate training, certifications or degrees required for this career path. 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, career 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 skills required to be a data scientist cut across traditional academic disciplines, including statistics, mathematics and computer science. This is why several schools, including New York University and NC State, offer specialized data scientist certification and degree programs that create graduates who are good at handling large volumes of data and have knowledge of math and statistics to analyze the data. In 2005, for example, NC State created the Institute for Advanced Analytics, which pulls together faculty members from various disciplines and teaches data science in a very integrated way. At NYU, the newly launched, two-year master of data science degree is also multidisciplinary, intersecting mathematics, computer science and statistics.

Data scientists also require domain expertise to give them the intuition about what to work on and test, especially in business. The optimal place to gain domain expertise is on the job. But for people interested in improving their technical skills, there are options beyond university programs. There are good math and statistics courses online, and many computer science courses online, too. Additionally, vendors in the Big Data market are developing extensive training programs for would-be big data professionals. The target audience they are aiming for includes the individuals who are not yet calling themselves data scientists. They may be software engineers or statisticians, but they need what it takes to operate in a new data-driven environment.

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