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pries kim h.; dunnigan robert - big data analytics

Big Data Analytics A Practical Guide for Managers


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Lingua: Inglese
Pubblicazione: 06/2022
Edizione: 1° edizione

Note Editore

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 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.


IntroductionSo What Is Big Data?Growing Interest in Decision MakingWhat This Book AddressesThe Conversation about Big DataTechnological Change as a Driver of Big DataThe Central Question: So What?Our Goals as AuthorsReferencesThe Mother of Invention’s Triplets: Moore’s Law, the Proliferation of Data, and Data Storage TechnologyMoore’s LawParallel Computing, Between and Within MachinesQuantum ComputingRecap of Growth in Computing PowerStorage, Storage EverywhereGrist for the Mill: Data Used and UnusedAgricultureAutomotiveMarketing in the Physical WorldOnline MarketingAsset Reliability and EfficiencyProcess Tracking and AutomationToward a Definition of Big DataPutting Big Data in ContextKey Concepts of Big Data and Their ConsequencesSummaryReferences.HadoopPower through Distribution Cost Effectiveness of HadoopNot Every Problem Is a Nail Some Technical AspectsTroubleshooting HadoopRunning HadoopHadoop File System MapReducePig and HiveInstallationCurrent Hadoop EcosystemHadoop Vendors ClouderaAmazon Web Services (AWS)HortonworksIBMIntelMapRMicrosoft To Run Pig Latin Using PowershellPivotalReferencesHBase and Other Big Data DatabasesEvolution from Flat File to the Three V’s Flat File Hierarchical Database Network Database Relational Database Object-Oriented Databases Relational-Object DatabasesTransition 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 DatabasesKey-Value DatabasesColumn-Oriented Databases HBase Apache AccumuloReferencesMachine LearningMachine Learning BasicsClassifying with Nearest NeighborsNaive BayesSupport Vector MachinesImproving Classification with Adaptive BoostingRegressionLogistic RegressionTree-Based RegressionK-Means ClusteringApriori AlgorithmFrequent Pattern-GrowthPrincipal Component Analysis (PCA)Singular Value DecompositionNeural NetworksBig Data and MapReduceData ExplorationSpam FilteringRankingPredictive RegressionText RegressionMultidimensional ScalingSocial GraphingReferencesStatisticsStatistics, Statistics EverywhereDigging into the DataStandard Deviation: The Standard Measure of DispersionThe Power of Shapes: DistributionsDistributions: Gaussian CurveDistributions: Why Be Normal?Distributions: The Long Arm of the Power LawThe Upshot? Statistics Are not BloodlessFooling Ourselves: Seeing What We Want to See in the DataWe Can Learn Much from an OctopusHypothesis Testing: Seeking a Verdict Two-Tailed TestingHypothesis Testing: A Broad FieldMoving on to Specific Hypothesis TestsRegression and Correlationp Value in Hypothesis Testing: A Successful Gatekeeper?Specious Correlations and Overfitting the DataA Sample of Common Statistical Software Packages Minitab SPSS R SAS Big Data Analytics Hadoop Integration Angoss Statistica CapabilitiesSummaryReferencesGoogleBig Data GiantsGoogle Go Android Google Product Offerings Google Analytics Advertising and Campaign Performance Analysis and TestingFacebookNingNon-United States Social Media Tencent Line Sina Weibo Odnoklassniki Vkontakte NimbuzzRanking Network SitesNegative Issues with Social NetworksAmazonSome Final WordsReferencesGeographic Information Systems (GIS)GIS ImplementationsA GIS ExampleGIS ToolsGIS DatabasesReferencesDiscoveryFaceted Search versus Strict TaxonomyFirst Key Ability: Breaking Down BarriersSecond Key Ability: Flexible Search and NavigationUnderlying TechnologyThe UpshotSummaryReferencesData QualityKnow Thy Data and ThyselfStructured, Unstructured, and Semistructured DataData Inconsistency: An Example from This BookThe Black Swan and Incomplete DataHow Data Can Fool Us Ambiguous Data Aging of Data or Variables Missing Variables May Change the Meaning Inconsistent Use of Units and TerminologyBiases Sampling Bias Publication Bias Survivorship BiasData as a Video, Not a Snapshot: Different Viewpoints as a Noise FilterWhat Is My Toolkit for Improving My Data? Ishikawa Diagram Interrelationship Digraph Force Field AnalysisData-Centric Methods Troubleshooting Queries from Source Data Troubleshooting Data Quality beyond the Source System Using Our Hidden ResourcesSummaryReferencesBenefitsData SerendipityConverting Data Dreck to UsefulnessSalesReturned MerchandiseSecurityMedicalTravel Lodging Vehicle MealsGeographical Information Systems New York City Chicago CLEARMAP Baltimore San Francisco Los Angeles Tucson, Arizona, University of Arizona, and COPLINKSocial NetworkingEducation General Educational Data Legacy Data Grades and other Indicators Testing Results Addresses, Phone Numbers, and MoreConcluding CommentsReferencesConcernsPart Two: Basic Principles of National Application Collection Limitation Principle Data Quality Principle Purpose Specification Principle Use Limitation Principle Security Safeguards Principle Openness Principle Individual Participation Principle Accountability PrincipleLogical Fallacies Affirming the Consequent Denying the Antecedent Ludic FallacyCognitive Biases Confirmation Bias Notational Bias Selection/Sample Bias Halo Effect Consistency and Hindsight Biases Congruence Bias Von Restorff EffectData Serendipity Converting Data Dreck to Usefulness SalesMerchandise ReturnsSecurity CompStat MedicalTravel Lodging Vehicle MealsSocial NetworkingEducationMaking Yourself Harder to Track Misinformation Disinformation Reducing/Eliminating Profiles Social Media Self Redefinition Identity Theft FacebookConcluding CommentsReferencesEpilogue Michael Porter’s Five Forces Model Bargaining Power of Customers Bargaining Power of Suppliers Threat of New Entrants OthersThe OODA LoopImplementing Big DataNonlinear, Qualitative ThinkingClosingReferences


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 of science in metallurgical engineering and materials science from Carnegie-Mellon University.Pries worked as a computer systems manager, a software engineer for an electrical utility, and a scientific programmer under a defense contract for Stoneridge, Incorporated (SRI). He has worked as software manager, engineering services manager, reliability section manager, and product integrity and reliability director.In addition to his other responsibilities, Pries has provided Six Sigma training for both UTEP and SRI and cost reduction initiatives for SRI. Pries is also a founding faculty member of Practical Project Management. Additionally, in concert with Jon Quigley, Pries was a cofounder and principal with Value Transformation, LLC, a training, testing, cost improvement, and product development consultancy.He trained for Introduction to Engineering Design and Computer Science and Software Engineering with Project Lead the Way. He currently teaches biotechnology, computer science and software engineering, and introduction to engineering design at the beautiful Parkland High School in the Ysleta Independent School District of El Paso, Texas.Robert Dunnigan is a manager with Janus Consulting Partners and is based in Dallas, Texas. He holds a bachelor of science in psychology and in sociology with an anthropology emphasis from North Dakota State University. He also holds a master of business administration from INSEAD, "the business school for the world," where he attended the Singapore campus.As a Peace Corps volunteer, Robert served over 3 years in Honduras developing agribusiness opportunities. As a consultant, he later worked on the Afghanistan Small and Medium Enterprise Development project in Afghanistan, where he traveled the country with his Afghan colleagues and friends seeking opportunities to develop a manufacturing sector in the country.Robert is an American Society for Quality–certified Six Sigma Black Belt and a Scrum Alliance–certified Scrum Master.

Altre Informazioni



Condizione: Nuovo
Dimensioni: 9.25 x 6.25 in Ø 1.75 lb
Formato: Brossura
Illustration Notes:58 b/w images
Pagine Arabe: 576

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