Big Data, Mining, and Analytics: Components of Strategic Decision Making
暫譯: 大數據、挖掘與分析：戰略決策的組成要素

Kudyba, Stephan

Big Data, Mining, and Analytics: Components of Strategic Decision Making

商品描述

There is an ongoing data explosion transpiring that will make previous creations, collections, and storage of data look trivial. Big Data, Mining, and Analytics: Components of Strategic Decision Making ties together big data, data mining, and analytics to explain how readers can leverage them to extract valuable insights from their data. Facilitating a clear understanding of big data, it supplies authoritative insights from expert contributors into leveraging data resources, including big data, to improve decision making.

Illustrating basic approaches of business intelligence to the more complex methods of data and text mining, the book guides readers through the process of extracting valuable knowledge from the varieties of data currently being generated in the brick and mortar and internet environments. It considers the broad spectrum of analytics approaches for decision making, including dashboards, OLAP cubes, data mining, and text mining.





  • Includes a foreword by Thomas H. Davenport, Distinguished Professor, Babson College; Fellow, MIT Center for Digital Business; and Co-Founder, International Institute for Analytics


  • Introduces text mining and the transforming of unstructured data into useful information


  • Examines real time wireless medical data acquisition for today's healthcare and data mining challenges


  • Presents the contributions of big data experts from academia and industry, including SAS


  • Highlights the most exciting emerging technologies for big data--Hadoop is just the beginning




Filled with examples that illustrate the value of analytics throughout, the book outlines a conceptual framework for data modeling that can help you immediately improve your own analytics and decision-making processes. It also provides in-depth coverage of analyzing unstructured data with text mining methods to supply you with the well-rounded understanding required to leve

商品描述(中文翻譯)

目前正在發生一場數據爆炸，這將使以往的數據創建、收集和存儲看起來微不足道。《大數據、數據挖掘與分析：戰略決策的組成部分制定》將大數據、數據挖掘和分析結合在一起，解釋讀者如何利用這些技術從數據中提取有價值的見解。該書促進了對大數據的清晰理解，並提供了來自專家貢獻者的權威見解，幫助讀者利用數據資源（包括大數據）來改善決策。

本書展示了商業智慧的基本方法以及更複雜的數據和文本挖掘方法，指導讀者從當前在實體和互聯網環境中生成的各種數據中提取有價值的知識。它考慮了決策過程中各種分析方法的廣泛範疇，包括儀表板、OLAP 立方體、數據挖掘和文本挖掘。


  • 包括由巴布森學院的傑出教授 Thomas H. Davenport 撰寫的前言；麻省理工學院數位商業中心的研究員；國際分析學院的共同創辦人

  • 介紹文本挖掘及將非結構化數據轉化為有用信息的過程

  • 檢視當今醫療保健和數據挖掘挑戰的實時無線醫療數據獲取

  • 展示來自學術界和業界的大數據專家的貢獻，包括 SAS

  • 突顯大數據中最令人興奮的新興技術——Hadoop 只是個開始

本書充滿了示例，展示了分析的價值，並概述了一個數據建模的概念框架，幫助您立即改善自己的分析和決策過程。它還深入探討了使用文本挖掘方法分析非結構化數據，為您提供所需的全面理解，以提升您的分析能力。

作者簡介

Stephan Kudyba has developed computerized models for trading financial markets in the investment banking industry and has provided Business Intelligence based solutions involving data mining applications for organizations across industry sectors. He has published numerous books and articles, has been interviewed by prominent magazines and speaks at corporate and academic events addressing data, information and knowledge management and organizational performance.

Dr. Kudyba is a professor in the school of management at New Jersey Institute of Technology where he teaches business courses addressing data, information and knowledge management, market research and internet marketing. He has held editorial positions for academic journals, is a member of a number of information management based societies, and maintains relations with organizations in a variety of industries addressing strategic initiatives.

作者簡介(中文翻譯)

Stephan Kudyba 在投資銀行業開發了用於交易金融市場的電腦化模型，並為各行各業的組織提供了基於商業智慧的解決方案，涉及數據挖掘應用。他出版了多本書籍和文章，曾接受知名雜誌的訪問，並在企業和學術活動中發表演講，主題涵蓋數據、信息和知識管理以及組織績效。

Kudyba 博士是新澤西理工學院管理學院的教授，教授有關數據、信息和知識管理、市場研究及網路行銷的商業課程。他曾擔任學術期刊的編輯職位，是多個信息管理相關學會的成員，並與多個行業的組織保持關係，針對戰略性倡議進行合作。

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