Big.Data.Analytics.A.Practical.Guide.for.Manager

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文件名称：Big.Data.Analytics.A.Practical.Guide.for.Manager
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更新时间：2018-03-18 04:22:59
Big Data Title: Big Data Analytics: A Practical Guide for Managers Author: Kim H. Pries, Robert Dunnigan Length: 576 pages Edition: 1 Language: English Publisher: Auerbach Publications Publication Date: 2015-02-12 ISBN-10: 1482234513 ISBN-13: 9781482234510 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, but 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 terms Includes substantial vendor/tool material, especially for open source decisions Covers prominent software packages, including Hadoop and Oracle Endeca Examines GIS and machine learning applications Considers 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. Table of Contents Chapter 1: Introduction Chapter 2: The Mother of Invention's Triplets: Moore's Law, the Proliferation of Data, and Data Stor Chapter 3: Hadoop Chapter 4: HBase and Other Big Data Databases Chapter 5: Machine Learning Chapter 6: Statistics Chapter 7: Google Chapter 8: Geographic Information Systems (GIS) Chapter 9: Discovery Chapter 10: Data Quality Chapter 11: Benefits Chapter 12: Concerns Chapter 13: Epilogue