LEADER 00719nam0-22002411i-450- 001 990001181460403321 035 $a000118146 035 $aFED01000118146 035 $a(Aleph)000118146FED01 035 $a000118146 100 $a20000920d1929----km-y0itay50------ba 101 0 $aeng 200 1 $a<>Ricerche Geometrico-Meccaniche di Leonardo da Vinci$fdi MARCOLONGO R. 210 $aRoma$cBardi$d1929 700 1$aMarcolongo,$bRoberto$c$01307 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001181460403321 952 $a117-B-2$b03174$fMA1 959 $aMA1 996 $aRicerche Geometrico-Meccaniche di Leonardo da Vinci$9342237 997 $aUNINA DB $aING01 LEADER 00986nam a22002653i 4500 001 991000722809707536 005 20040128125732.0 008 040220s1958 it a||||||||||||||||ita 035 $ab12660747-39ule_inst 035 $aARCHE-065908$9ExL 040 $aDip.to Scienze pedagogiche$bita$cA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l. 082 04$a136.7 100 1 $aDe Benedetti Gaddini, Renata$0482749 245 13$aIl bambino, il medico, la medicina /$cRenata Gaddini 260 $aBologna :$bG. Malipiero,$c1958 300 $a221 p. :$bill. ;$c20 cm 440 3$aIl fanciullo nel mondo moderno 650 4$aPsicologia infantile 650 4$aPediatria 907 $a.b12660747$b02-04-14$c17-03-04 912 $a991000722809707536 945 $aLE022 MP 70 C 32$g1$i2022000061513$lle022$o-$pE0.00$q-$rl$s- $t0$u3$v0$w3$x0$y.i13167790$z17-03-04 996 $aBambino, il medico, la medicina$9273054 997 $aUNISALENTO 998 $ale022$b17-03-04$cm$da $e-$fita$git $h3$i1 LEADER 07124oam 2200613 450 001 9910555077903321 005 20231222221202.0 010 $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$01384858 702 $aStryker$b Cole 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910555077903321 996 $aSmarter data science$93431685 997 $aUNINA