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DATA SCIENCE

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Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.[1][2] Data science is the same concept as data mining and big data: "use the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems".[3]

Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data.[4] It employs techniques and theories drawn from many fields within the context of mathematicsstatisticscomputer science, and information scienceTuring award winner Jim Gray imagined data science as a "fourth paradigm" of science (empiricaltheoreticalcomputational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.[5][6] In 2015, the American Statistical Association identified database management, statistics and machine learning, and distributed and parallel systems as the three emerging foundational professional communities.[7]

In 2012, when Harvard Business Review called it "The Sexiest Job of the 21st Century",[8] the term "data science" became a buzzword. It is now often used interchangeably with earlier concepts like business analytics,[9] business intelligencepredictive modeling, and statistics. Even the suggestion that data science is sexy was paraphrasing Hans Rosling, featured in a 2011 BBC documentary with the quote, "Statistics is now the sexiest subject around."[10] Nate Silver referred to data science as a sexed up term for statistics.[11] In many cases, earlier approaches and solutions are now simply rebranded as "data science" to be more attractive, which can cause the term to become "dilute[d] beyond usefulness."[12] While many university programs now offer a data science degree, there exists no consensus on a definition or suitable curriculum contents.[9] To its discredit, however, many data-science and big-data projects fail to deliver useful results, often as a result of poor management and utilization of resources.[13][14][15][16]