Tabla de Contenidos: “…-- 1.2 The Role of Data Science in Marketing -- 1.3 Marketing Analytics Versus Data Science -- 1.4 Key Concepts and Terminology -- 1.4.1 Data Science -- 1.4.2 Data Visualization -- 1.4.3 Customer Segmentation -- 1.4.4 Predictive Analytics -- 1.4.5 Machine Learning -- 1.4.6 Natural Language Processing -- 1.4.7 Marketing Mix Modeling -- 1.4.8 Big Data -- 1.5 Structure of This Book -- 1.6 Practical Example 1: Applying Data Science to Improve Cross-Selling in a Retail Bank Marketing Department -- 1.6.1 Data Collection -- 1.6.2 Data Preparation -- 1.6.3 Customer Segmentation -- 1.6.4 Product Recommendation Modeling -- 1.6.5 Campaign Design -- 1.6.6 A/B Testing and Evaluation -- 1.6.7 Monitoring and Refinement -- 1.7 Practical Example 2: The Impact of Data Science on a Marketing Campaign -- 1.8 Conclusion -- 1.9 References -- Chapter 2 Data Collection and Preparation -- 2.1 Introduction -- 2.2 Data Sources in Marketing: Evolution and the Emergence of Big Data -- 2.2.1 Traditional Data Sources -- 2.2.2 The Emergence of Modern Data Sources -- 2.2.3 Big Data and Its Impact on Marketing -- 2.3 Data Collection Methods -- 2.3.1 Surveys and Questionnaires -- 2.3.2 Web Scraping -- 2.3.3 Application Programming Interfaces -- 2.3.4 Data Purchase -- 2.3.5 Observational Data -- 2.4 Data Preparation -- 2.4.1 Data Cleaning -- 2.4.2 Data Integration -- 2.4.3 Data Transformation -- 2.4.4 Data Reduction -- 2.5 Practical Example: Collecting and Preparing Data for a Customer Churn Analysis -- 2.6 Conclusion -- 2.7 References -- Chapter 3 Descriptive Analytics in Marketing -- 3.1 Introduction -- 3.2 Overview of Descriptive Analytics…”

Tabla de Contenidos: “…II Engineering IoT Networks -- ch. 3 Smart Objects: The "Things" in IoT -- Sensors, Actuators, and Smart Objects -- Sensors -- Actuators -- Micro-Electro-Mechanical Systems (MEMS) -- Smart Objects -- Smart Objects: A Definition -- Trends in Smart Objects -- Sensor Networks -- Wireless Sensor Networks (WSNs) -- Communication Protocols for Wireless Sensor Networks -- Summary -- ch. 4 Connecting Smart Objects -- Communications Criteria -- Range -- Frequency Bands -- Power Consumption -- Topology -- Constrained Devices -- Constrained-Node Networks -- Data Rate and Throughput -- Latency and Determinism -- Overhead and Payload -- IoT Access Technologies -- IEEE 802.15.4 -- Standardization and Alliances -- Physical Layer -- MAC Layer -- Topology -- Security -- Competitive Technologies -- IEEE 802.15.4 Conclusions -- IEEE 802.15.4g and 802.15.4e -- Standardization and Alliances -- Physical Layer -- MAC Layer -- Topology -- Security -- Competitive Technologies -- IEEE 802.15.4g and 802.15.4e Conclusions -- IEEE 1901.2a -- Standardization and Alliances -- Physical Layer -- MAC Layer -- Topology -- Security -- Competitive Technologies -- IEEE 1901.2a Conclusions -- IEEE 802.1 lah -- Standardization and Alliances -- Physical Layer -- MAC Layer -- Topology -- Security -- Competitive Technologies -- IEEE 802.1 lah Conclusions -- LoRaWAN -- Standardization and Alliances -- Physical Layer -- MAC Layer -- Topology -- Security -- Competitive Technologies -- LoRaWAN Conclusions -- NB-IoT and Other LTE Variations -- Standardization and Alliances -- LTE Cat 0 -- LTE-M -- NB-IoT -- Topology -- Competitive Technologies -- NB-IoT and Other LTE Variations Conclusions -- Summary -- ch. 5 IP as the IoT Network Layer -- The Business Case for IP -- The Key Advantages of Internet Protocol -- Adoption or Adaptation of the Internet Protocol -- The Need for Optimization -- Constrained Nodes -- Constrained Networks -- IP Versions -- Optimizing IP for IoT -- From 6LoWPAN to 6Lo -- Header Compression -- Fragmentation -- Mesh Addressing -- Mesh-Under Versus Mesh-Over Routing -- 6L0 Working Group -- 6TiSCH -- RPL -- Objective Function (OF) -- Rank -- RPL Headers -- Metrics -- Authentication and Encryption on Constrained Nodes -- ACE -- DICE -- Profiles and Compliances -- Internet Protocol for Smart Objects (IPSO) Alliance -- Wi-SUN Alliance -- Thread -- IPv6 Ready Logo -- Summary -- ch. 6 Application Protocols for IoT -- The Transport Layer -- IoT Application Transport Methods -- Application Layer Protocol Not Present -- SCADA -- A Little Background on SCADA -- Adapting SCADA for IP -- Tunneling Legacy SCADA over IP Networks -- SCADA Protocol Translation -- SCADA Transport over LLNs with MAP-T -- Generic Web-Based Protocols -- IoT Application Layer Protocols -- CoAP -- Message Queuing Telemetry Transport (MQTT) -- Summary -- ch. 7 Data and Analytics for IoT -- An Introduction to Data Analytics for IoT -- Structured Versus Unstructured Data -- Data in Motion Versus Data at Rest -- IoT Data Analytics Overview -- IoT Data Analytics Challenges -- Machine Learning -- Machine Learning Overview -- Supervised Learning -- Unsupervised Learning -- Neural Networks -- Machine Learning and Getting Intelligence from Big Data -- Predictive Analytics -- Big Data Analytics Tools and Technology -- Massively Parallel Processing Databases -- NoSQL Databases -- Hadoop -- YARN -- The Hadoop Ecosystem -- Apache Kafka -- Lambda Architecture -- Edge Streaming Analytics -- Comparing Big Data and Edge Analytics -- Edge Analytics Core Functions -- Distributed Analytics Systems -- Network Analytics -- Flexible NetFlow Architecture -- FNF Components -- Flexible NetFlow in Multiservice IoT Networks -- Summary -- References -- ch. 8 Securing IoT -- A Brief History of OT Security -- Common Challenges in OT Security -- Erosion of Network Architecture -- Pervasive Legacy Systems -- Insecure Operational Protocols -- Modbus -- DNP3 (Distributed Network Protocol) -- ICCP (Inter-Control Center Communications Protocol) -- OPC (OLE for Process Control) -- International Electrotechnical Commission (IEC) Protocols -- Other Protocols -- Device Insecurity -- Dependence on External Vendors -- Security Knowledge -- How IT and OT Security Practices and Systems Vary -- The Purdue Model for Control Hierarchy -- OT Network Characteristics Impacting Security -- Security Priorities: Integrity, Availability, and Confidentiality -- Security Focus -- Formal Risk Analysis Structures: OCTAVE and FAIR -- OCTAVE -- FAIR -- The Phased Application of Security in an Operational Environment -- Secured Network Infrastructure and Assets -- Deploying Dedicated Security Appliances -- Higher-Order Policy Convergence and Network Monitoring -- Summary -- pt. …”

“…En la actualidad los datos han cobrado una importancia esencial en las organizaciones que se están transformando digitalmente y convirtiéndoseen data centric para poder ofrecer un servicio de excelencia a todos sus stakeholders y tomar las mejores decisiones.En efecto, los avances tecnológicos que estamos viviendo en los últimos años, que nos permiten recoger y almacenar enormes cantidades de datos (por mediode dispositivos móviles, sensores, Internet de las cosas, big data, etc.), tratarlos mediante diferentes algoritmos analíticos avanzados (machine learning,business intelligence, etc) , y disponer de una prácticamente ilimitada cantidad de procesamiento (en forma de servicio, mediante la computación en la nube), han convertido los datos en el nuevo petróleo del siglo XXI.Pero para que los datos sean el activo más importante de las organizaciones, debe tener la calidad adecuada; ya que los resultados de cualquier algoritmoy de cualquier decisión que se tome, no será mejor que los datos sobre los que se basa. …”
Enlace del recurso

Tabla de Contenidos: “…-- No servers to manage -- Pay-per-invocation billing model -- Ability to automatically scale with usage -- Built-in availability and fault tolerance -- Design patterns -- When to use serverless -- The sweet spot -- Classes of serverless pattern -- Three-tier web application patterns -- ETL patterns -- Big data patterns -- Automation and deployment patterns -- Serverless frameworks -- Summary -- Chapter 2: A Three-Tier Web Application Using REST -- Serverless tooling -- System architecture -- Presentation layer -- Logic layer -- Data layer -- Logic layer -- Application code and function layout -- Organization of the Lambda functions -- Organization of the application code -- Configuration with environment variables -- Code structure -- Function layout -- Presentation layer -- File storage with S3 -- CDN with CloudFront -- Data layer -- Writing our logic layer -- Application entrypoint -- Application logic -- Wiring handler.py to Lambda via API Gateway -- Deploying the REST API -- Deploying the Postgres database -- Setting up static assets -- Viewing the deployed web application -- Running tests -- Iteration and deployment -- Deploying the entire stack -- Deploying the application code -- Summary -- Chapter 3: A Three-Tier Web Application Pattern with GraphQL -- Introduction to GraphQL -- System architecture -- Logic layer -- Organization of the Lambda functions -- Organization of the application code -- Function layout -- Presentation layer -- Writing the logic layer -- Implementing the entry point -- Implementing GraphQL queries -- Implementing GraphQL mutations -- Deployment -- Viewing the deployed application -- Iteration and deployment -- Summary…”

“…As a computing and networking architecture, fog enables key applications in wireless 5G, the Internet of things (IoT), and big data. The authors cover the fundamental trade-offs to major applications of fog. …”

“…Dort herrscht Goldgräberstimmung – trotz enormem Innovationsdruck: Big Data soll Licht in manches Dunkel bringen und offene Fragen beantworten. …”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: A Gentle Introduction to Machine Learning -- Introduction - classic and adaptive machines -- Only learning matters -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Beyond machine learning - deep learning and bio-inspired adaptive systems -- Machine learning and big data -- Further reading -- Summary -- Chapter 2: Important Elements in Machine Learning -- Data formats -- Multiclass strategies -- One-vs-all -- One-vs-one -- Learnability -- Underfitting and overfitting -- Error measures -- PAC learning -- Statistical learning approaches -- MAP learning -- Maximum-likelihood learning -- Elements of information theory -- References -- Summary -- Chapter 3: Feature Selection and Feature Engineering -- scikit-learn toy datasets -- Creating training and test sets -- Managing categorical data -- Managing missing features -- Data scaling and normalization -- Feature selection and filtering -- Principal component analysis -- Non-negative matrix factorization -- Sparse PCA -- Kernel PCA -- Atom extraction and dictionary learning -- References -- Summary -- Chapter 4: Linear Regression -- Linear models -- A bidimensional example -- Linear regression with scikit-learn and higher dimensionality -- Regressor analytic expression -- Ridge, Lasso, and ElasticNet -- Robust regression with random sample consensus -- Polynomial regression -- Isotonic regression -- References -- Summary -- Chapter 5: Logistic Regression -- Linear classification -- Logistic regression -- Implementation and optimizations -- Stochastic gradient descent algorithms -- Finding the optimal hyperparameters through grid search -- Classification metrics -- ROC curve -- Summary -- Chapter 6: Naive Bayes…”

“…Why have developers at places like Facebook and Twitter increasingly turned to graph databases to manage their highly connected big data? The short answer is that graphs offer superior speed and flexibility to get the job done. …”

“… It's tough to argue with R as a high-quality, cross-platform, open source statistical software product-unless you're in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Foreword -- Contributors -- Table of Contents -- Preface -- Part 1: What Data-Centric Machine Learning Is and Why We Need It -- Chapter 1: Exploring Data-Centric Machine Learning -- Understanding data-centric ML -- The origins of data centricity -- The components of ML systems -- Data is the foundational ingredient -- Data-centric versus model-centric ML -- Data centricity is a team sport -- The importance of quality data in ML -- Identifying high-value legal cases with natural language processing -- Predicting cardiac arrests in emergency calls -- Summary -- References -- Chapter 2: From Model-Centric to Data-Centric - ML's Evolution -- Exploring why ML development ended up being mostly model-centric -- The 1940s to 1970s - the early days -- The 1980s to 1990s - the rise of personal computing and the internet -- The 2000s - the rise of tech giants -- 2010-now - big data drives AI innovation -- Model-centricity was the logical evolutionary outcome -- Unlocking the opportunity for small data ML -- Why we need data-centric AI more than ever -- The cascading effects of data quality -- Avoiding data cascades and technical debt -- Summary -- References -- Part 2: The Building Blocks of Data-Centric ML -- Chapter 3: Principles of Data-Centric ML -- Sometimes, all you need is the right data -- Principle 1 - data should be the center of ML development -- A checklist for data-centricity -- Principle 2 - leverage annotators and SMEs effectively -- Direct labeling with human annotators -- Verifying output quality with human annotators -- Codifying labeling rules with programmatic labeling -- Principle 3 - use ML to improve your data -- Principle 4 - follow ethical, responsible, and well-governed ML practices -- Summary -- References -- Chapter 4: Data Labeling Is a Collaborative Process…”

“…With the surge in big data and AI, organizations can rapidly create data products. …”

Tabla de Contenidos: “….) -- Una clasificación de los saberes digitales enestudiantes de la Facultad de Ciencias Administrativas y Sociales -- Dispositivo interactivo para apoyar el desarrollo de la comunicación oral en personas con discapacidad auditiva en el Benemérito Comité Pro Ciegos -- Supra-adaptación como mecanismo para propiciar el aprendizaje significativo -- Análisis de accesibilidad de documentos electrónicos: Caso de estudio Instituciones Educativas -- Guía para escribir y presentar una ponencia en un congreso científico -- Algoritmos híbridos para la segmentación de imágenes en regiones de color casi homogéneas -- La Gestión del Conocimiento desde el Observatorio Virtual Accesible -- Diseñando un curso virtual con apoyo de herramientas tecnológicas -- Monitoreo de paneles fotovoltaicos mediante tecnolog ́ıa m ́ovil y ubicua -- Implementación de Arquitectura Cloud Híbrida -- Estudio de la viabilidad de externalizar servicios -- Monitoreo ambiental de calidad del aire en la ciudad deLeón -Nicaragua -- Implementación de una Solución de Big Data en laInformación Tributaría -- Aplicación de inteligencia de negocios para construir indicadores de evaluación académicos…”

Tabla de Contenidos: “…DE LAS NUBES A LA NIEBLA, HASTA LLEGAR AL BORDE -- IOT Y BLOCKCHAIN -- IOT Y REALIDAD VIRTUAL, AUMENTADA Y MIXTA -- IOT Y LOS GEMELOS DIGITALES -- IOT E INTELIGENCIA ARTIFICIAL (MACHINE LEARNING) -- IOT Y BIG DATA -- BIBLIOGRAFÍA…”

Tabla de Contenidos: “…-- Summary -- Key Learnings -- Notes -- Further Reading -- Chapter 2 The Journey -- Starting the Supply Chain Journey -- Introducing Sales &amp -- Operations Planning (S&amp -- OP) into the Supply Chain Journey -- Sales &amp -- Operations Planning Connection -- Transitioning to a Demand-Driven Supply Chain -- The Digitalization of the Supply Chain -- Leveraging New Scalable Technology -- Benefits -- Summary -- Key Learnings -- Notes -- Chapter 3 The Data -- What Is Big Data? -- Data versus Actionable Information -- Consumer/Customer Orientation -- Eliminating Information Silos -- Sales &amp -- Operations Planning -- Technology Can Do It Better and Faster -- A Structured Process Supported by Technology -- Why Is Downstream Data Important? …”

Tabla de Contenidos: “…Machine-to-machine and the big data opportunity; 1.1.2. Machine-to-machine technology landscape; 1.1.3. …”

Tabla de Contenidos: “…-- Chapter 7 Types of Artificial Intelligence -- Chapter 8 Some Subdomains of Artificial Intelligence -- Chapter 9 Big Data and Data Analytics -- Chapter 10 Generative Artificial Intelligence -- Industry Competing in Developing Generative AI -- Optimizing the Supply Chain -- Applications of AI in Supply Chain Management for Business Scalability -- Mind-Blowing Generative AI Statistics -- Chapter 11 The State of Artificial Intelligence and Smart Cybersecurity: Some Insights and Statistics -- AI and ML for Analytics -- The State of AI in Business -- AI Priorities and Plans -- AI Market Statistics -- AI Growth -- Obstacles to AI Adoption -- Benefits of AI Adoption -- Impact of AI on Jobs and the Employment Market -- ML and AI Stats -- Voice Search and AI Stats -- Virtual Assistants -- AI in the Retail Industry -- AI in Customer Service -- Chatbots and AI Facts and Figures -- AI in Marketing and Sales…”

Tabla de Contenidos: “…2.6.4 Snowflake Schemas2.7 Data Transformation with Extract Transform Load, Extract Load Transform, and Replication; 2.7.1 Extract Transform Load; 2.7.2 Extract Load Transform; 2.7.3 Replication; 2.8 Overview of Business Intelligence Architectures; 2.9 New Forms of Reporting and Analytics; 2.9.1 Operational Reporting and Analytics; 2.9.2 Deep and Big Data Analytics; 2.9.3 Self-Service Reporting and Analytics; 2.9.4 Unrestricted Ad-Hoc Analysis; 2.9.5 360-Degree Reporting; 2.9.6 Exploratory Analysis; 2.9.7 Text-Based Analysis; 2.10 Disadvantages of Classic Business Intelligence Systems…”

Tabla de Contenidos: “…Mercader -- Economía colaborativa / Luis A. Velasco -- Big data y derecho de la competencia / Carmen Herrero Suárez -- Fintech & Insurtech / María Rubio -- La contratación pública de servicios digitales / Luis S. …”

Tabla de Contenidos: “…Cybersecurity in Artificial Intelligence (AI) -- 7.1.2. Cybersecurity in Big Data -- 7.1.3. Cybersecurity in Telemedicine -- 7.1.4. …”

Tabla de Contenidos: “…Data Transmission (EDI and XML) -- Databases and Business Analytics -- Application Systems -- Enterprise Resource Planning (ERP) -- Procurement Systems -- Advanced Planning and Scheduling -- Transportation Planning Systems -- Demand Planning -- Customer Relationship Management (CRM) and Sales Force Automation (SFA) -- Supply Chain Management (SCM) -- Inventory Management Systems -- Manufacturing Execution Systems (MES ) -- Transportation Scheduling Systems -- Warehouse Management Systems (WMS) -- New Supply Chain Technology -- Industrial Robots -- Drones and Driverless Vehicles -- Artificial Intelligence (AI) -- Internet of Things (IoT) and Big Data -- Real-Time Product Information -- 3D Printing and Additive Manufacturing -- Simulation Modeling -- Impact on Supply Chain Operations -- Assessing Technology and System Needs -- E-Business and Supply Chain Integration -- Chapter Summary -- Chapter 5: Metrics for Measuring Supply Chain Performance -- Useful Model of Markets and Their Supply Chains -- Market Performance Categories -- Customer Service -- Internal Efficiency -- Demand Flexibility -- Product Development -- A Framework for Performance Measurement -- Customer Service Metrics -- Build to Stock -- Build to Order -- Internal Efficiency Metrics -- Inventory Value -- Inventory Turns -- Return on Sales -- Cash-to-Cash Cycle Time -- Demand Flexibility Metrics -- Upside Flexibility -- Outside Flexibility -- Product Development Metrics -- Operations that Enable Supply Chain Performance -- Collecting and Displaying Performance Data -- Three Levels of Detail -- The Data Warehouse -- Spotlighting Problems and Finding Opportunities -- Markets Migrate from One Quadrant to Another -- Sharing Data Across the Supply Chain -- Chapter Summary -- Chapter 6: Supply Chain Coordination -- The Bullwhip Effect -- Coordination in the Supply Chain -- Demand Forecasting…”