LEADER 07124oam 2200613 450 001 9910812216103321 005 20240123204004.0 010 $a1-119-69342-X 010 $a1-119-69438-8 010 $a1-119-69798-0 035 $a(CKB)4100000010871038 035 $a(OCLC) 1151198638 035 $a(MiAaPQ)EBC6173692 035 $a(CaSebORM)9781119693413 035 $a(JP-MeL)3000134331 035 $a(Au-PeEL)EBL6173692 035 $a(OCoLC)1151198638 035 $a(PPN)272718386 035 $a(EXLCZ)994100000010871038 100 $a20200803d2020 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSmarter data science $esucceeding with enterprise-grade data and AI projects /$fNeal Fishman, Cole Stryker 210 1$aIndianapolis :$cJohn Wiley and Sons,$d2020. 215 $a1 online resource (307 pages) 300 $aIncludes index 311 0 $a1-119-69341-1 327 $aCover -- Praise For This Book -- Title Page -- Copyright -- About the Authors -- Acknowledgments -- Contents at a Glance -- Contents -- Foreword for Smarter Data Science -- Epigraph -- Preamble -- Chapter 1 Climbing the AI Ladder -- Readying Data for AI -- Technology Focus Areas -- Taking the Ladder Rung by Rung -- Constantly Adapt to Retain Organizational Relevance -- Data-Based Reasoning Is Part and Parcel in the Modern Business -- Toward the AI-Centric Organization -- Summary -- Chapter 2 Framing Part I: Considerations for Organizations Using AI -- Data-Driven Decision-Making; Using Interrogatives to Gain Insight -- The Trust Matrix -- The Importance of Metrics and Human Insight -- Democratizing Data and Data Science -- Aye, a Prerequisite: Organizing Data Must Be a Forethought -- Preventing Design Pitfalls -- Facilitating the Winds of Change: How Organized Data Facilitates Reaction Time -- Quae Quaestio (Question Everything) -- Summary -- Chapter 3 Framing Part II: Considerations for Working with Data and AI -- Personalizing the Data Experience for Every User -- Context Counts: Choosing the Right Way to Display Data; Ethnography: Improving Understanding Through Specialized Data -- Data Governance and Data Quality -- The Value of Decomposing Data -- Providing Structure Through Data Governance -- Curating Data for Training -- Additional Considerations for Creating Value -- Ontologies: A Means for Encapsulating Knowledge -- Fairness, Trust, and Transparency in AI Outcomes -- Accessible, Accurate, Curated, and Organized -- Summary -- Chapter 4 A Look Back on Analytics: More Than One Hammer -- Been Here Before: Reviewing the Enterprise Data Warehouse -- Drawbacks of the Traditional Data Warehouse -- Paradigm Shift; Modern Analytical Environments: The Data Lake -- By Contrast -- Indigenous Data -- Attributes of Difference -- Elements of the Data Lake -- The New Normal: Big Data Is Now Normal Data -- Liberation from the Rigidity of a Single Data Model -- Streaming Data -- Suitable Tools for the Task -- Easier Accessibility -- Reducing Costs -- Scalability -- Data Management and Data Governance for AI -- Schema-on-Read vs. Schema-on-Write -- Summary -- Chapter 5 A Look Forward on Analytics: Not Everything Can Be a Nail -- A Need for Organization -- The Staging Zone -- The Raw Zone; The Discovery and Exploration Zone -- The Aligned Zone -- The Harmonized Zone -- The Curated Zone -- Data Topologies -- Zone Map -- Data Pipelines -- Data Topography -- Expanding, Adding, Moving, and Removing Zones -- Enabling the Zones -- Ingestion -- Data Governance -- Data Storage and Retention -- Data Processing -- Data Access -- Management and Monitoring -- Metadata -- Summary -- Chapter 6 Addressing Operational Disciplines on the AI Ladder -- A Passage of Time -- Create -- Stability -- Barriers -- Complexity -- Execute -- Ingestion -- Visibility -- Compliance -- Operate -- Quality. 330 $aOrganizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how.' Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments. When an organization manages its data effectively, its data science program becomes a fully scalable function that's both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise. By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: -Improving time-to-value with infused AI models for common use cases -Optimizing knowledge work and business processes -Utilizing AI-based business intelligence and data visualization -Establishing a data topology to support general or highly specialized needs -Successfully completing AI projects in a predictable manner -Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations. 606 $aManagement information systems 606 $aDatabase management 606 $aBusiness$vDatabases$xManagement 606 $aInformation storage and retrieval systems$xReliability 606 $aCOMPUTERS$xData Science$xData Modeling & Design$2bisacsh 615 0$aManagement information systems. 615 0$aDatabase management. 615 0$aBusiness$xManagement. 615 0$aInformation storage and retrieval systems$xReliability. 615 7$aCOMPUTERS$xData Science$xData Modeling & Design. 676 $a495.932 686 $a007.609$2njb/09 686 $a005.7$2njb/09 700 $aFishman$b Neal$01676164 702 $aStryker$b Cole 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910812216103321 996 $aSmarter data science$94042188 997 $aUNINA LEADER 05229nam 2201009 450 001 9910819423503321 005 20230126212543.0 010 $a0-520-27775-9 010 $a0-520-96031-9 024 7 $a10.1525/9780520960312 035 $a(CKB)3710000000316765 035 $a(EBL)1732135 035 $a(SSID)ssj0001381142 035 $a(PQKBManifestationID)11773250 035 $a(PQKBTitleCode)TC0001381142 035 $a(PQKBWorkID)11390715 035 $a(PQKB)11663087 035 $a(MiAaPQ)EBC1732135 035 $a(DE-B1597)520949 035 $a(OCoLC)898421637 035 $a(DE-B1597)9780520960312 035 $a(Au-PeEL)EBL1732135 035 $a(CaPaEBR)ebr11003289 035 $a(CaONFJC)MIL688029 035 $a(EXLCZ)993710000000316765 100 $a20150120h20152015 uy 0 101 0 $aeng 135 $aur|nu---|u||u 181 $ctxt 182 $cc 183 $acr 200 10$aDriving after class $eanxious times in an American suburb /$fRachel Heiman 210 1$aOakland, California :$cUniversity of California Press,$d2015. 210 4$dİ2015 215 $a1 online resource (743 p.) 225 1 $aCalifornia Series in Public Anthropology ;$v31 300 $aDescription based upon print version of record. 311 0 $a1-322-56747-6 311 0 $a0-520-27774-0 320 $aIncludes bibliographical references and index. 327 $tFront matter --$tContents --$tIllustrations --$tPreface --$tAcknowledgments --$t1. Introduction: Common Sense in Anxious Times --$t2. Being Post-Brooklyn --$t3. Gate Expectations --$t4. Driving after Class --$t5. Vehicles for Rugged Entitlement --$t6. From White Flight to Community Might --$t7. A Conclusion, or Rather, a Commencement --$tNotes --$tReferences --$tIndex 330 $aA paradoxical situation emerged at the turn of the twenty-first century: the dramatic upscaling of the suburban American dream even as the possibilities for achieving and maintaining it diminished. Having fled to the suburbs in search of affordable homes, open space, and better schools, city-raised parents found their modest homes eclipsed by McMansions, local schools and roads overburdened and underfunded, and their ability to keep up with the pressures of extravagant consumerism increasingly tenuous. How do class anxieties play out amid such disconcerting cultural, political, and economic changes? In this incisive ethnography set in a New Jersey suburb outside New York City, Rachel Heiman takes us into people's homes; their community meetings, where they debate security gates and school redistricting; and even their cars, to offer an intimate view of the tensions and uncertainties of being middle class at that time. With a gift for bringing to life the everyday workings of class in the lives of children, youth, and their parents, Heiman offers an illuminating look at the contemporary complexities of class rooted in racialized lives, hyperconsumption, and neoliberal citizenship. She argues convincingly that to understand our current economic situation we need to attend to the subtle but forceful formation of sensibilities, spaces, and habits that durably motivate people and shape their actions and outlooks. "Rugged entitlement" is Heiman's name for the middle class's sense of entitlement to a way of life that is increasingly untenable and that is accompanied by an anxious feeling that they must vigilantly pursue their own interests to maintain and further their class position. Driving after Class is a model of fine-grained ethnography that shows how families try to make sense of who they are and where they are going in a highly competitive and uncertain time. 410 0$aCalifornia series in public anthropology ;$v31. 606 $aSocial classes$zNew Jersey 606 $aSuburban life$zNew Jersey 606 $aMiddle class$zNew Jersey 607 $aNew Jersey$xSocial conditions 610 $a21st century american culture. 610 $aaffordable homes. 610 $aamerican dream. 610 $aamerican economy. 610 $aanthropology. 610 $abetter schools. 610 $acalifornia series in public anthropology. 610 $acapitalism. 610 $aclass anxieties. 610 $aclass in america. 610 $acommunity meetings. 610 $aconsumerism. 610 $acultural studies. 610 $ademocracy. 610 $aeconomic changes. 610 $aethnographic research. 610 $afamily. 610 $ahyperconsumption. 610 $amiddle class. 610 $aneoliberal citizenship. 610 $anew jersey suburb. 610 $apolitical. 610 $apublic anthropology. 610 $arace and class. 610 $arugged entitlement. 610 $aschool redistricting. 610 $asecurity gates. 610 $asuburban american dream. 615 0$aSocial classes 615 0$aSuburban life 615 0$aMiddle class 676 $a305.5/509749 700 $aHeiman$b Rachel$01601168 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910819423503321 996 $aDriving after class$93924644 997 $aUNINA