Big data analytics : a practical guide for managers /: a practical guide for managers. (2015)

• Record Type: Book
• Further Information: Note: Kim H. Pries and Robert Dunnigan.
• Authors: Pries, Kim H, 1955-; Dunnigan, Robert
• Contents: Introduction; So What Is Big Data?; Growing Interest in Decision Making; What This Book Addresses; The Conversation about Big Data; Technological Change as a Driver of Big Data; The Central Question: So What?; Our Goals as Authors; References; ; The Mother of Invention’s Triplets: Moore’s Law, the Proliferation of Data, and Data Storage Technology; Moore’s Law; Parallel Computing, Between and Within Machines; Quantum Computing; Recap of Growth in Computing Power; Storage, Storage Everywhere; Grist for the Mill: Data Used and Unused; Agriculture; Automotive; Marketing in the Physical World; Online Marketing; Asset Reliability and Efficiency; Process Tracking and Automation; Toward a Definition of Big Data; Putting Big Data in Context; Key Concepts of Big Data and Their Consequences; Summary; References; ; Hadoop; Power through Distribution; Cost Effectiveness of Hadoop; Not Every Problem Is a Nail; Some Technical Aspects; Troubleshooting Hadoop; Running Hadoop; Hadoop File System; MapReduce; Pig and Hive; Installation; Current Hadoop Ecosystem; Hadoop Vendors; Cloudera; Amazon Web Services (AWS); Hortonworks; IBM; Intel; MapR; Microsoft; To Run Pig Latin Using Powershell; Pivotal; References; ; HBase and Other Big Data Databases; Evolution from Flat File to the Three V’ s; Flat File; Hierarchical Database; Network Database; Relational Database; Object-Oriented Databases; Relational-Object Databases; Transition to Big Data Databases; What Is Different bbout HBase?; What Is Bigtable?; What Is MapReduce?; What Are the Various Modalities for Big Data Databases?; Graph Databases; How Does a Graph Database Work?; What is the Performance of a Graph Database?; Document Databases; Key-Value Databases; Column-Oriented Databases; HBase; Apache Accumulo; References; ; Machine Learning; Machine Learning Basics; Classifying with Nearest Neighbors; Naive Bayes; Support Vector Machines; Improving Classification with Adaptive Boosting; Regression; Logistic Regression; Tree-Based Regression; K-Means Clustering; Apriori Algorithm; Frequent Pattern-Growth; Principal Component Analysis (PCA); Singular Value Decomposition; Neural Networks; Big Data and MapReduce; Data Exploration; Spam Filtering; Ranking; Predictive Regression; Text Regression; Multidimensional Scaling; Social Graphing; References; ; Statistics; Statistics, Statistics Everywhere; Digging into the Data; Standard Deviation: The Standard Measure of Dispersion; The Power of Shapes: Distributions; Distributions: Gaussian Curve; Distributions: Why Be Normal?; Distributions: The Long Arm of the Power Law; The Upshot? Statistics Are not Bloodless; Fooling Ourselves: Seeing What We Want to See in the Data; We Can Learn Much from an Octopus; Hypothesis Testing: Seeking a Verdict; Two-Tailed Testing; Hypothesis Testing: A Broad Field; Moving on to Specific Hypothesis Tests; Regression and Correlation; p Value in Hypothesis Testing: A Successful Gatekeeper?; Specious Correlations and Overfitting the Data; A Sample of Common Statistical Software Packages; Minitab; SPSS; R; SAS; Big Data Analytics; Hadoop Integration; Angoss; Statistica; Capabilities; Summary; References; ; Google; Big Data Giants; Google; Go; Android; Google Product Offerings; Google Analytics; Advertising and Campaign Performance; Analysis and Testing; Facebook; Ning; Non-United States Social Media; Tencent; Line; Sina Weibo; Odnoklassniki; Vkontakte; Nimbuzz; Ranking Network Sites; Negative Issues with Social Networks; Amazon; Some Final Words; References; ; Geographic Information Systems (GIS); GIS Implementations; A GIS Example; GIS Tools; GIS Databases; References; ; Discovery; Faceted Search versus Strict Taxonomy; First Key Ability: Breaking Down Barriers; Second Key Ability: Flexible Search and Navigation; Underlying Technology; The Upshot; Summary; References; ; Data Quality; Know Thy Data and Thyself; Structured, Unstructured, and Semistructured Data; Data Inconsistency: An Example from This Book; The Black Swan and Incomplete Data; How Data Can Fool Us; Ambiguous Data; Aging of Data or Variables; Missing Variables May Change the Meaning; Inconsistent Use of Units and Terminology; Biases; Sampling Bias; Publication Bias; Survivorship Bias; Data as a Video, Not a Snapshot: Different Viewpoints as a Noise Filter; What Is My Toolkit for Improving My Data?; Ishikawa Diagram; Interrelationship Digraph; Force Field Analysis; Data-Centric Methods; Troubleshooting Queries from Source Data; Troubleshooting Data Quality beyond the Source System; Using Our Hidden Resources; Summary; References; ; Benefits; Data Serendipity; Converting Data Dreck to Usefulness; Sales; Returned Merchandise; Security; Medical; Travel; Lodging; Vehicle; Meals; Geographical Information Systems; New York City; Chicago CLEARMAP; Baltimore; San Francisco; Los Angeles; Tucson, Arizona, University of Arizona, and COPLINK; Social Networking; Education; &
• Edition: 1st
• Publisher Details: Boca Raton : Auerbach
• Publication Date: 2015
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 658.0557; Management -- Statistical methods; Management -- Data processing; Big data; Data mining; Database management
• Languages: English
• ISBNs: 9781482234527
• Related ISBNs: 9781482234510
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; item not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.