Produkte zum Begriff Data-Driven Marketing:
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Mastering Data Modeling: A User Driven Approach
Data modeling is one of the most critical phases in the database application development process, but also the phase most likely to fail. A master data modeler must come into any organization, understand its data requirements, and skillfully model the data for applications that most effectively serve organizational needs. Mastering Data Modeling is a complete guide to becoming a successful data modeler. Featuring a requirements-driven approach, this book clearly explains fundamental concepts, introduces a user-oriented data modeling notation, and describes a rigorous, step-by-step process for collecting, modeling, and documenting the kinds of data that users need. Assuming no prior knowledge, Mastering Data Modeling sets forth several fundamental problems of data modeling, such as reconciling the software developer's demand for rigor with the users' equally valid need to speak their own (sometimes vague) natural language. In addition, it describes the good habits that help you respond to these fundamental problems. With these good habits in mind, the book describes the Logical Data Structure (LDS) notation and the process of controlled evolution by which you can create low-cost, user-approved data models that resist premature obsolescence. Also included is an encyclopedic analysis of all data shapes that you will encounter. Most notably, the book describes The Flow, a loosely scripted process by which you and the users gradually but continuously improve an LDS until it faithfully represents the information needs. Essential implementation and technology issues are also covered. You will learn about such vital topics as: The fundamental problems of data modelingThe good habits that help a data modeler be effective and economicalLDS notation, which encourages these good habitsHow to read an LDS aloud--in declarative English sentencesHow to write a well-formed (syntactically correct) LDSHow to get users to name the parts of an LDS with words from their own business vocabularyHow to visualize data for an LDSA catalog of LDS shapes that recur throughout all data modelsThe Flow--the template for your conversations with usersHow to document an LDS for users, data modelers, and technologistsHow to map an LDS to a relational schemaHow LDS differs from other notations and why "Story interludes" appear throughout the book, illustrating real-world successes of the LDS notation and controlled evolution process. Numerous exercises help you master critical skills. In addition, two detailed, annotated sample conversations with users show you the process of controlled evolution in action.
Preis: 22.46 € | Versand*: 0 € -
Mastering Data Modeling: A User-Driven Approach
Data modeling is one of the most critical phases in the database application development process, but also the phase most likely to fail. A master data modeler must come into any organization, understand its data requirements, and skillfully model the data for applications that most effectively serve organizational needs. Mastering Data Modeling is a complete guide to becoming a successful data modeler. Featuring a requirements-driven approach, this book clearly explains fundamental concepts, introduces a user-oriented data modeling notation, and describes a rigorous, step-by-step process for collecting, modeling, and documenting the kinds of data that users need. Assuming no prior knowledge, Mastering Data Modeling sets forth several fundamental problems of data modeling, such as reconciling the software developer's demand for rigor with the users' equally valid need to speak their own (sometimes vague) natural language. In addition, it describes the good habits that help you respond to these fundamental problems. With these good habits in mind, the book describes the Logical Data Structure (LDS) notation and the process of controlled evolution by which you can create low-cost, user-approved data models that resist premature obsolescence. Also included is an encyclopedic analysis of all data shapes that you will encounter. Most notably, the book describes The Flow, a loosely scripted process by which you and the users gradually but continuously improve an LDS until it faithfully represents the information needs. Essential implementation and technology issues are also covered. You will learn about such vital topics as: The fundamental problems of data modeling The good habits that help a data modeler be effective and economical LDS notation, which encourages these good habits How to read an LDS aloud--in declarative English sentences How to write a well-formed (syntactically correct) LDS How to get users to name the parts of an LDS with words from their own business vocabulary How to visualize data for an LDS A catalog of LDS shapes that recur throughout all data models The Flow--the template for your conversations with users How to document an LDS for users, data modelers, and technologists How to map an LDS to a relational schema How LDS differs from other notations and why "Story interludes" appear throughout the book, illustrating real-world successes of the LDS notation and controlled evolution process. Numerous exercises help you master critical skills. In addition, two detailed, annotated sample conversations with users show you the process of controlled evolution in action.
Preis: 37.44 € | Versand*: 0 € -
Core Data for iOS: Developing Data-Driven Applications for the iPad, iPhone, and iPod touch
Today, virtually every non-trivial iPhone and iPad app must manage data—quickly, smoothly, reliably, and with minimal impact on the CPU to conserve battery life. Core Data, Apple’s ready-made data persistence layer, can help you achieve all these goals. In Core Data for iOS, two leading iOS developers teach you the entire Core Data framework from the ground up. Writing for intermediate-to-advanced iOS developers, Tim Isted and Tom Harrington thoroughly explain how Core Data is used on iOS devices, introduce each of its primary classes, and show how they interact to provide amazing functionality with minimal configuration. You’ll learn how to store, fetch, and validate data; provide it efficiently to views; and much more. Isted and Harrington first give you a firm grounding in the technology, and then present real-world examples. They present multiple sample projects, as well as a start-to-finish, chapter-length case study. Coverage includes • Understanding Core Data’s features, classes, and interactions • Using Core Data in MVC-based iOS app development • Mapping relational data to object models, and building them with Xcode 4’s Data Modeler • Working with managed objects and using UITableView to display them • Creating predicates to match numbers, data, and objects • Maintaining compatibility across versions of an app’s data model • Tracking managed object contexts across view controllers • Using Core Data’s automatic Undo functionality • Integrating abstract entities, entity inheritance, and multiple view controllers into a complete app • Optimizing for iOS devices’ tight memory limits • Diagnosing and fixing common Core Data problems Introducing Addison-Wesley’s new Core Frameworks Series, written for experienced iOS developers by world-class Mac and iOS developers, these are the first comprehensive, code-rich reference guides to Apple’s Core Frameworks.
Preis: 16.04 € | Versand*: 0 € -
Data-Driven Science and Engineering (Brunton, Steven L.~Kutz, J. Nathan)
Data-Driven Science and Engineering , Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material - including lecture videos per section, homeworks, data, and code in MATLAB®, Python, Julia, and R - available on databookuw.com. , > , Auflage: 2nd Edition, Erscheinungsjahr: 20220505, Produktform: Leinen, Autoren: Brunton, Steven L.~Kutz, J. Nathan, Auflage: 22002, Auflage/Ausgabe: 2nd Edition, Abbildungen: Worked examples or Exercises, Themenüberschrift: COMPUTERS / General, Fachschema: Datenverarbeitung / Anwendungen / Technik~EDV / Theorie / Allgemeines~Elektronik - Elektroniker~Englische Bücher / Naturwissenschaften~Ingenieurwissenschaft - Ingenieurwissenschaftler~Intelligenz / Künstliche Intelligenz~KI~Künstliche Intelligenz - AI~Mathematik / Informatik, Computer~Modell~Optimierung~Physik / Mathematik~Regelungstechnik~Mathematik, Fachkategorie: Wahrscheinlichkeitsrechnung und Statistik~Optimierung~Mathematische Physik~Ingenieurswesen, Maschinenbau allgemein~Informationstechnik (IT), allgemeine Themen~Theoretische Informatik~Maschinelles Lernen~Digitale Signalverarbeitung (DSP), Warengruppe: HC/Mathematik/Wahrscheinlichkeitstheorie, Fachkategorie: Regelungstechnik, Text Sprache: eng, Seitenanzahl: XXIV, Seitenanzahl: 590, UNSPSC: 49019900, Warenverzeichnis für die Außenhandelsstatistik: 49019900, Verlag: Cambridge University Pr., Verlag: Cambridge University Pr., Länge: 259, Breite: 182, Höhe: 32, Gewicht: 1408, Produktform: Gebunden, Genre: Importe, Genre: Importe, Katalog: Gesamtkatalog, Katalog: Internationale Lagertitel, Katalog: internationale Titel, Katalog: Kennzeichnung von Titeln mit einer Relevanz > 30, Katalog: Lagerartikel, Book on Demand, ausgew. Medienartikel, Relevanz: 0100, Tendenz: +1, Unterkatalog: AK, Unterkatalog: Bücher, Unterkatalog: Hardcover, Unterkatalog: Lagerartikel,
Preis: 59.81 € | Versand*: 0 €
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Was bedeutet Vertrieb und Marketing?
Was bedeutet Vertrieb und Marketing? Vertrieb bezieht sich auf den Prozess des Verkaufs von Produkten oder Dienstleistungen an Kunden, während Marketing die Strategien und Aktivitäten umfasst, die darauf abzielen, das Interesse der Kunden zu wecken und sie zum Kauf zu bewegen. Zusammen bilden Vertrieb und Marketing die Grundlage für den Erfolg eines Unternehmens, da sie helfen, Produkte oder Dienstleistungen bekannt zu machen, Kunden zu gewinnen und Umsätze zu steigern. Beide Bereiche arbeiten eng zusammen, um die Bedürfnisse der Kunden zu verstehen, die richtige Zielgruppe anzusprechen und die Marke eines Unternehmens zu stärken. Letztendlich sind Vertrieb und Marketing entscheidend für das Wachstum und die Rentabilität eines Unternehmens.
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Was macht man im Marketing und Vertrieb?
Im Marketing und Vertrieb werden Strategien entwickelt, um Produkte oder Dienstleistungen erfolgreich auf dem Markt zu positionieren und zu verkaufen. Dazu gehören Marktanalysen, Zielgruppenbestimmung, Werbekampagnen, Vertriebskanäle und Preisgestaltung. Im Marketing werden Marken aufgebaut, Imagepflege betrieben und Kundenbeziehungen gepflegt. Im Vertrieb werden Verkaufsgespräche geführt, Verträge abgeschlossen und Kundenakquise betrieben. Letztendlich geht es darum, die Bedürfnisse der Kunden zu verstehen und sie von den eigenen Produkten oder Dienstleistungen zu überzeugen.
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Was bedeuten Data Science und Data Engineering?
Data Science bezieht sich auf die Analyse und Interpretation von Daten, um Erkenntnisse und Muster zu gewinnen, die bei der Lösung von Problemen und der Unterstützung von Entscheidungsprozessen helfen. Data Engineering hingegen bezieht sich auf die Entwicklung und Verwaltung von Dateninfrastrukturen, um sicherzustellen, dass Daten effizient erfasst, gespeichert, verarbeitet und analysiert werden können. Data Engineering legt den Fokus auf die technische Seite der Datenverarbeitung, während Data Science sich auf die Analyse und Interpretation der Daten konzentriert.
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Wie beeinflusst Bannerwerbung das Online-Marketing und die Kundengewinnung?
Bannerwerbung beeinflusst das Online-Marketing, indem sie die Sichtbarkeit einer Marke erhöht und potenzielle Kunden auf Produkte oder Dienstleistungen aufmerksam macht. Durch gezielte Platzierung von Bannern auf relevanten Websites können Unternehmen ihre Zielgruppe erreichen und die Kundengewinnung steigern. Die Klickrate und Conversion-Rate von Bannerwerbung sind wichtige Kennzahlen, um den Erfolg einer Kampagne zu messen und das Marketingbudget effektiv einzusetzen.
Ähnliche Suchbegriffe für Data-Driven Marketing:
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Policy Driven Data Center with ACI, The: Architecture, Concepts, and Methodology
Using the policy driven data center approach, networking professionals can make their data center topologies faster to configure and more portable. They can also build cloud infrastructure faster than before. All of this can be achieved by using REST and python together with the latest Cisco technology called Application Centric Infrastructure (ACI). The Policy Driven Data Center with ACI helps Architects, IT administrators, Network Administrators and Engineers to build and troubleshoot multipurpose cloud architectures. Cisco data center experts Lucien Avramov and Maurizio Portolani thoroughly explain the architecture, concepts, and methodology of the policy driven data center. The authors cover the key technology concepts, the tools for modern data centers including python scripting and REST, the design consideration and methodology of modern fabrics including VXLAN-based forwarding, the policy model theory and concepts, how to build a multi-hypervisor and bare-metal infrastructure including OpenStack, the service integration, and advanced telemetry capabilities for troubleshooting. The book concludes by discussing universal data center switch architecture concepts in order to clearly understand switching concepts and the newer trends in the Nexus 9000 product portfolio. Drawing on their extensive experience in enterprise engagements, the authors present effective solutions for virtualized data centers, high performance computing, ultra-low latency environments, and large-scale data centers. In addition to discussing relevant concepts and methodologies, the authors address design considerations associated with hardware, topologies, automation, and scalability. Technical professionals will find invaluable guidance on migrating current data center environments to a policy driven data center.
Preis: 42.79 € | Versand*: 0 € -
Policy Driven Data Center with ACI, The: Architecture, Concepts, and Methodology
Using the policy driven data center approach, networking professionals can make their data center topologies faster to configure and more portable. They can also build cloud infrastructure faster than before. All of this can be achieved by using REST and python together with the latest Cisco technology called Application Centric Infrastructure (ACI). The Policy Driven Data Center with ACI helps Architects, IT administrators, Network Administrators and Engineers to build and troubleshoot multipurpose cloud architectures. Cisco data center experts Lucien Avramov and Maurizio Portolani thoroughly explain the architecture, concepts, and methodology of the policy driven data center. The authors cover the key technology concepts, the tools for modern data centers including python scripting and REST, the design consideration and methodology of modern fabrics including VXLAN-based forwarding, the policy model theory and concepts, how to build a multi-hypervisor and bare-metal infrastructure including OpenStack, the service integration, and advanced telemetry capabilities for troubleshooting. The book concludes by discussing universal data center switch architecture concepts in order to clearly understand switching concepts and the newer trends in the Nexus 9000 product portfolio. Drawing on their extensive experience in enterprise engagements, the authors present effective solutions for virtualized data centers, high performance computing, ultra-low latency environments, and large-scale data centers. In addition to discussing relevant concepts and methodologies, the authors address design considerations associated with hardware, topologies, automation, and scalability. Technical professionals will find invaluable guidance on migrating current data center environments to a policy driven data center.
Preis: 32.09 € | Versand*: 0 € -
Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing
Using Agile methods, you can bring far greater innovation, value, and quality to any data warehousing (DW), business intelligence (BI), or analytics project. However, conventional Agile methods must be carefully adapted to address the unique characteristics of DW/BI projects. In Agile Analytics, Agile pioneer Ken Collier shows how to do just that. Collier introduces platform-agnostic Agile solutions for integrating infrastructures consisting of diverse operational, legacy, and specialty systems that mix commercial and custom code. Using working examples, he shows how to manage analytics development teams with widely diverse skill sets and how to support enormous and fast-growing data volumes. Collier’s techniques offer optimal value whether your projects involve “back-end” data management, “front-end” business analysis, or both. Part I focuses on Agile project management techniques and delivery team coordination, introducing core practices that shape the way your Agile DW/BI project community can collaborate toward success Part II presents technical methods for enabling continuous delivery of business value at production-quality levels, including evolving superior designs; test-driven DW development; version control; and project automation Collier brings together proven solutions you can apply right now—whether you’re an IT decision-maker, data warehouse professional, database administrator, business intelligence specialist, or database developer. With his help, you can mitigate project risk, improve business alignment, achieve better results—and have fun along the way.
Preis: 29.95 € | Versand*: 0 € -
Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing
Using Agile methods, you can bring far greater innovation, value, and quality to any data warehousing (DW), business intelligence (BI), or analytics project. However, conventional Agile methods must be carefully adapted to address the unique characteristics of DW/BI projects. In Agile Analytics, Agile pioneer Ken Collier shows how to do just that. Collier introduces platform-agnostic Agile solutions for integrating infrastructures consisting of diverse operational, legacy, and specialty systems that mix commercial and custom code. Using working examples, he shows how to manage analytics development teams with widely diverse skill sets and how to support enormous and fast-growing data volumes. Collier’s techniques offer optimal value whether your projects involve “back-end” data management, “front-end” business analysis, or both. Part I focuses on Agile project management techniques and delivery team coordination, introducing core practices that shape the way your Agile DW/BI project community can collaborate toward success Part II presents technical methods for enabling continuous delivery of business value at production-quality levels, including evolving superior designs; test-driven DW development; version control; and project automation Collier brings together proven solutions you can apply right now—whether you’re an IT decision-maker, data warehouse professional, database administrator, business intelligence specialist, or database developer. With his help, you can mitigate project risk, improve business alignment, achieve better results—and have fun along the way.
Preis: 22.46 € | Versand*: 0 €
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Was ist der Unterschied zwischen Vertrieb und Marketing?
Der Vertrieb bezieht sich auf den Prozess des Verkaufs und der Distribution von Produkten oder Dienstleistungen an Kunden. Es umfasst Aktivitäten wie die Kundenakquise, Verkaufsgespräche, Vertragsverhandlungen und die Lieferung der Produkte. Marketing hingegen bezieht sich auf die strategische Planung und Umsetzung von Aktivitäten, um das Bewusstsein für ein Produkt oder eine Dienstleistung zu schaffen, potenzielle Kunden anzuziehen und die Nachfrage zu steigern. Dazu gehören Aktivitäten wie Marktforschung, Werbung, Öffentlichkeitsarbeit und Markenbildung.
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Was ist der Unterschied zwischen Marketing und Vertrieb?
Was ist der Unterschied zwischen Marketing und Vertrieb? Marketing bezieht sich auf die Strategien und Aktivitäten, die darauf abzielen, potenzielle Kunden anzusprechen, ihr Interesse zu wecken und sie über die Produkte oder Dienstleistungen zu informieren. Vertrieb hingegen bezieht sich auf den Prozess, bei dem diese potenziellen Kunden zu tatsächlichen Käufern gemacht werden, indem Verkaufsgespräche geführt, Verträge abgeschlossen und Transaktionen abgewickelt werden. Marketing legt den Grundstein für den Vertrieb, indem es die Markenbekanntheit steigert und Interesse weckt, während der Vertrieb den direkten Verkaufsprozess übernimmt und die Kundenbeziehungen pflegt. Insgesamt arbeiten Marketing und Vertrieb eng zusammen, um den Umsatz zu steigern und das Unternehmen erfolgreich am Markt zu positionieren.
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Warum Data Scientist?
Warum Data Scientist? Data Scientist sind gefragt, weil sie komplexe Daten analysieren und interpretieren können, um fundierte Entscheidungen zu treffen. Sie spielen eine entscheidende Rolle bei der Optimierung von Geschäftsprozessen und der Entwicklung innovativer Produkte. Zudem bieten Data Science Karrieremöglichkeiten in verschiedenen Branchen und ermöglichen es, mit modernsten Technologien und Tools zu arbeiten. Nicht zuletzt ist Data Science ein spannendes und dynamisches Feld, das ständig neue Herausforderungen und Möglichkeiten bietet.
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Was heißt data?
Was heißt data? Data ist der englische Begriff für Daten, also Informationen oder Fakten, die gesammelt und analysiert werden können. Daten können in verschiedenen Formen vorliegen, wie zum Beispiel Zahlen, Texte, Bilder oder Videos. Sie sind essentiell für Entscheidungsprozesse in Unternehmen, Wissenschaft und vielen anderen Bereichen. Die Analyse von Daten ermöglicht es, Muster, Trends und Zusammenhänge zu erkennen und fundierte Schlussfolgerungen zu ziehen. In der heutigen digitalen Welt spielt die Verarbeitung und Interpretation von Daten eine immer größere Rolle.
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