Big Data Analytics Practical by Pries Kim (17 results)
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Condition: New. 2022. 1st Edition. Paperback. . . . . .
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Condition: New. Kim H. Pries has four college degrees: a bachelor of arts in history from the University of Texas at El Paso (UTEP), a bachelor of science in metallurgical engineering from UTEP, a master of science in engineering from UTEP, and a master.
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Taschenbuch. Condition: Neu. Big Data Analytics | A Practical Guide for Managers | Kim H. Pries (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2022 | Taylor & Francis Ltd | EAN 9781032340197 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preig…u.
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - With this book, managers and decision makers are given the tools to make more informed decisions about big data purchasing initiatives. Big Data Analytics: A Practical Guide for Managers not only supplies descriptions of common tools, bu…t also surveys the various products and vendors that supply the big data market.Comparing and contrasting the different types of analysis commonly conducted with big data, this accessible reference presents clear-cut explanations of the general workings of big data tools. Instead of spending time on HOW to install specific packages, it focuses on the reasons WHY readers would install a given package.The book provides authoritative guidance on a range of tools, including open source and proprietary systems. It details the strengths and weaknesses of incorporating big data analysis into decision-making and explains how to leverage the strengths while mitigating the weaknesses.Describes the benefits of distributed computing in simple termsIncludes substantial vendor/tool material, especially for open source decisionsCovers prominent software packages, including Hadoop andOracle EndecaExamines GIS and machine learning applicationsConsiders privacy and surveillance issues The book further explores basic statistical concepts that, when misapplied, can be the source of errors. Time and again, big data is treated as an oracle that discovers results nobody would have imagined. While big data can serve this valuable function, all too often these results are incorrect, yet are still reported unquestioningly. The probability of having erroneous results increases as a larger number of variables are compared unless preventative measures are taken.The approach taken by the authors is to explain these concepts so managers can ask better questions of their analysts and vendors as to the appropriateness of the methods used to arrive at a conclusion. Because the world of science and medicine has been grappling with similar issues in the publication of studies, the authors draw on their efforts and apply them to big data.
