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Bringing Big Data to the Big Tent

By Gregory Goth

Communications of the ACM, Vol. 58 No. 7, Pages 17-19

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Some experienced hands in the field of data analysis feel the differences between investigational data scientists, who work on the leading edge of concepts using statistical tools such as the R programming environment, and operational data scientists, who have traditionally used general-purpose programming languages like C++ and Java to scale analytics to real-time enterprise-level computational assets, need to become less relevant.

That sentiment is growing, especially as tools emerge that enable individual scientists to analyze ever-larger amounts of data. Though many, if not most, of these data practitioners are not considered software developers in the traditional sense, the data their analysis creates becomes itself an increasingly valuable resource for many others.


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