Fundamentals of Analytics Engineering (216 results)

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Paperback. Condition: new. Paperback. Mastering Real-Time pipelines;Build fast, scalable systems with Apache spark, kafka and flink Hands-On Real-Time Data Analytics Low-Latency Pipelines with Spark, Kafka, and Flink is a comprehensive, practical guide designed to help you master the art of real-time data processing using three of the most powerful open-source tools-Apache Spark, Apache Kafka, and Apache Flink. Whether you're an experienced data engineer or a beginner looking to dive into real-time analytics, this book offers clear explanations, hands-on examples, and advanced optimization techniques to build fast, scalable, and fault-tolerant data pipelines. In today's fast-paced digital landscape, businesses generate enormous amounts of data every second. Traditional batch processing is no longer sufficient-modern systems demand instant insights to power everything from fraud detection and personalized recommendations to system monitoring and IoT applications. This book equips you with the skills to design and implement real-time data workflows that deliver actionable intelligence with minimal latency. What You Will Learn: 1. Fundamentals of Real-Time Data Processing: Understand the core principles behind event streaming and how real-time analytics differs from traditional batch systems. 2. Master Apache Kafka: Learn to set up, configure, and optimize Kafka for high-throughput, durable, and scalable data ingestion 3. Implement Spark Structured Streaming: Build efficient, micro-batch and continuous applications to transform and analyze streaming data. 4. Leverage Apache Flink for Stateful Processing: Dive deep into Flink's advanced event-time handling, windowing, and exactly-once guarantees. 5. End-to-End Pipeline Design: Learn how to integrate Kafka, Spark, and Flink to create robust, real-time data workflows. 6. Performance Tuning & Optimization: Apply advanced techniques to reduce latency, increase throughput, and ensure fault tolerance. 7. Real-World Use Cases: Explore practical examples of real-time fraud detection, monitoring, and machine learning integration. 8. Monitoring and Debugging: Use tools like Prometheus and Grafana to track performance and diagnose issues in real time. Why This Book? Practical and Hands-On: Includes detailed code examples and real-world case studies. Comprehensive Coverage: Covers everything from foundational concepts to advanced optimizations. Future-Proof Knowledge: Stay ahead by learning cutting-edge technologies and industry best practices. Simplified Explanations: Complex topics are broken down into easy-to-understand language, making this book accessible for all skill levels. Whether you're building pipelines for real-time analytics, optimizing existing workflows, or preparing for the future of streaming data, "Mastering Real-time data pipelines" provides you with the knowledge and tools to succeed in the evolving data landscape. About the AuthorKaelen Bush is a data engineering expert with a passion for building scalable real-time systems. With years of experience in distributed computing, Kaelen specializes in simplifying complex technologies and helping others harness the power of big data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. "The ABCs of AI for Small Business" is your essential roadmap for understanding the fundamentals of AI and its business potential. Written in clear, accessible language, the aim of this book is to demystify AI by providing actionable strategies for streamlining operations, enhancing customer experiences, and boosting the bottom line. From mastering prompt engineering to leveraging AI-powered data analytics, you'll discover how to potentially integrate AI successfully into every facet of your business. This comprehensive guide covers key areas of AI for business such as workflow automation, marketing and sales, as well as customer service; it offers practical insights and strategies for how to successfully adapt and manage AI business solutions to increase productivity, efficiency, and capacity. Additionally, some overarching principles and useful resources are included in this book: You will learn the basics of conducting an Enterprise Resource Planning (ERP) assessment and how to overcome common challenges associated with the AI integration process.You will also see that the integration of AI into any business is an iterative process that relies on setting clear objectives, monitoring the integration process, and modifying your efforts using internal data to optimize the performance of AI tools over time.Finally, navigate to the end of this book for additional resources. You will find a glossary of AI terms and recommended tools.All-in-all this book is designed to provide fundamental insights that help members of the small business community confidently embrace AI and think about how to best transition key systems for the future. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. Are you intrigued by the world of AI and data engineering? Do you find yourself captivated by the potential of these technologies to transform businesses and industries? If so, Data Engineering and AI for Beginners is the introductory guide you've been searching for. In today's fast evolving digital landscape where data is lifeblood, those who harness its flow hold the keys to game-changing insights. This book addresses the very challenges and questions that have been on your mind: What is Data Engineering?How Does AI Fit into the Picture?Is This for Me, a Beginner?What Tools and Technologies Are Involved?Where Does This Lead Me in the Future?Data Engineering and AI for Beginners is a resource tailored to cater to your curiosity and passion for learning, whether you're an eager student, a budding professional, or an experienced practitioner seeking to level up your skills. This book is designed to pique your interest with clear explanations of concepts, a structured learning journey that spans from fundamentals to advanced topics, and comprehensive coverage from a holistic view.You will discover in this book: The Potential of AI Applications in Data EngineeringAI-driven Data Visualisation and StorytellingDeep-dived Insights from Advanced Analytics with AI AI-powered Data Governance and ComplianceAnd Much, Much More!Ask any veteran data engineer who works with artificial intelligence, and you'll hear stories of data transformed into actionable insights, hidden patterns unveiled within mountains of data, and careers elevated by the power of innovation. AI isn't just an option; it's the compass guiding data engineers towards uncharted territories of excellence. Whether you're aiming to solve business problems or to simply demystify AI and data engineering, take the first step and let Data Engineering and AI for Beginners start you on a transformative journey of mastery. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. AI-Powered Project Management: Merging Technology with Team Success by Mohammad Zaripour is an insightful and forward-thinking guide that explores the transformative impact of artificial intelligence (AI) on project management. In an era where technology rapidly evolves, project managers face the challenge of harnessing AI to optimize processes, improve decision-making, and ultimately achieve success. This book offers a comprehensive roadmap for integrating AI into the project management landscape, providing both seasoned professionals and newcomers with the tools they need to navigate this emerging field.In this book, Mohammad Zaripour, a seasoned project management professional with deep expertise in engineering, shares his knowledge and experience to help readers understand the profound ways AI can revolutionize project management. Drawing on his extensive background, Zaripour delves into the complexities of AI, demystifying concepts such as machine learning, predictive analytics, and automation. He illustrates how these technologies can be leveraged to enhance project efficiency, streamline workflows, and improve team collaboration.The book begins by laying a solid foundation in AI fundamentals, making complex topics accessible to readers of all backgrounds. Zaripour then guides readers through the practical applications of AI in project management, offering real-world examples and case studies that highlight the benefits and challenges of AI adoption. From optimizing resource allocation and risk management to enhancing decision-making and communication, the book covers every aspect of AI's role in the modern project management toolkit.As the author explores the integration of AI with agile and traditional project management methodologies, he provides actionable insights on how to align AI technologies with organizational goals. Zaripour's approach is not just theoretical; it is grounded in practical advice that readers can apply immediately to their projects. He emphasizes the importance of ethical considerations and data security, ensuring that AI is used responsibly and effectively in project environments.AI-Powered Project Management: Merging Technology with Team Success also offers a forward-looking perspective, exploring the future of AI in project management and its potential to reshape industries. Mohammad Zaripour's vision is clear: AI is not just a tool for automation; it is a strategic asset that, when properly implemented, can lead to unparalleled project success and innovation.For project managers, team leaders, and business executives seeking to stay ahead of the curve, this book is an essential resource. Zaripour's expertise and clear, engaging writing style make this complex subject matter both accessible and compelling. Whether you are new to AI or looking to deepen your understanding, this book will equip you with the knowledge and confidence to lead your team into the future of project management. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. Conquer Complexity, Unleash Performance: Advanced Parallel ProgrammingParallel programming unlocks the power of multi-core processors and distributed computing systems, enabling you to tackle problems that would be intractable on a single machine. By harnessing this potential, you can significantly accelerate simulations, analyze massive datasets, and solve complex engineering challenges.Worries and Challenges You Face: Optimizing Code: Extracting maximum performance from parallel hardware requires careful code design, efficient algorithms, and a deep understanding of parallel programming concepts.Identifying Bottlenecks: Complex parallel programs can harbor hidden inefficiencies. You might struggle to pinpoint the root causes that hinder your program's performance.Choosing the Right Tools: With a vast array of profiling tools and libraries available, selecting the most suitable ones for your specific needs can be overwhelming.What to Expect from This Book: Master the Fundamentals: Gain a solid foundation in parallel programming concepts, including domain decomposition, communication strategies, and synchronization techniques.Optimize for Performance: Learn advanced profiling and analysis methods to identify bottlenecks and fine-tune your code for peak performance.Practical Applications: Explore real-world case studies that showcase how parallel programming tackles challenges in scientific computing, big data analytics, and image processing.Code Examples and Exercises: Deepen your understanding with practical code examples and exercises that reinforce your learning and equip you to apply your knowledge to real-world problems.Ready to Unlock Your Earnings Potential?Ready to unlock the true potential of parallel programming and push the boundaries of high-performance computing? This book equips you with the knowledge, skills, and practical guidance to become a parallel programming master. Dive in, unleash the hidden power of your code, and solve problems that were once thought impossible. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. Data Engineering with Apache Spark: Handle Big Data with Ease Using Spark's Fast Processing EngineUnlock the full potential of big data with Data Engineering with Apache Spark, the ultimate guide to mastering one of the most powerful data processing frameworks. Designed for data engineers, developers, and analysts, this book takes you through the fundamentals of Apache Spark and equips you to handle massive datasets with speed and precision.With practical examples, real-world use cases, and step-by-step guidance, you'll learn how to leverage Spark's robust features for big data transformation, analysis, and pipeline development. From setting up your Spark environment to deploying advanced data engineering solutions, this book is your roadmap to success.What You'll Learn: The core components of Apache Spark, including Spark SQL, DataFrames, and RDDs.How to design and build scalable data pipelines for real-time and batch processing.Techniques for optimizing Spark jobs to achieve maximum performance.Integrating Spark with big data tools like Hadoop, Kafka, and Hive.Real-world applications of Spark in industries like finance, healthcare, and e-commerce.Best practices for debugging, monitoring, and securing Spark jobs.Whether you're processing terabytes of data, building machine learning pipelines, or creating real-time analytics systems, Data Engineering with Apache Spark gives you the knowledge and confidence to excel in the era of big data.Harness the power of Spark to transform raw data into valuable insights. Start your journey today with Data Engineering with Apache Spark-your essential guide to handling big data with ease. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. Python for Data Engineering: Build ETL Pipelines and Handle Big Data Efficiently with PythonUnlock the full potential of data engineering with "Python for Data Engineering", the essential guide for aspiring data engineers, data scientists, and IT professionals seeking to master the art of building robust ETL pipelines and managing big data using Python. Whether you're just beginning your data engineering journey or looking to enhance your existing skills, this comprehensive handbook provides the tools, techniques, and insights necessary to transform raw data into valuable assets for your organization.Dive into expertly structured chapters that blend theoretical knowledge with practical applications, covering everything from the fundamentals of data engineering and Python programming to advanced topics like distributed computing, real-time data processing, and cloud integration. Learn how to design, develop, and deploy scalable ETL pipelines that efficiently extract, transform, and load data from diverse sources. Discover best practices for handling large datasets, optimizing performance, and ensuring data quality and integrity throughout the data lifecycle."Python for Data Engineering" empowers you to: Master ETL Processes: Understand the core principles of ETL and learn how to implement efficient data extraction, transformation, and loading strategies using Python.Handle Big Data: Explore techniques for managing and processing large-scale datasets with tools like Apache Spark, Hadoop, and Dask, all within the Python ecosystem.Automate Workflows: Streamline data engineering tasks by automating repetitive processes with Python scripts and workflow management tools such as Airflow and Luigi.Design Scalable Pipelines: Build resilient and scalable data pipelines that can handle increasing data volumes and complexity with ease.Ensure Data Quality: Implement robust data validation, cleansing, and monitoring practices to maintain high-quality data standards.Leverage Cloud Services: Integrate Python-based data engineering solutions with leading cloud platforms like AWS, Google Cloud, and Azure for enhanced flexibility and scalability.Optimize Performance: Fine-tune your data engineering workflows for maximum efficiency, reducing latency and improving throughput.Implement Security Best Practices: Protect sensitive data by applying security measures and ensuring compliance with industry standards and regulations.Visualize and Report Data: Create insightful visualizations and reports to communicate data findings effectively using libraries like Matplotlib, Seaborn, and Plotly.Stay Ahead with Advanced Topics: Delve into cutting-edge technologies such as machine learning integration, real-time analytics, and serverless computing to keep your skills current and in demand.Packed with real-world examples, hands-on exercises, and expert tips, "Python for Data Engineering" serves as your indispensable companion in navigating the dynamic field of data engineering. Whether you're building data pipelines for business intelligence, supporting data-driven decision-making, or driving innovation through data analytics, this book equips you with the knowledge and skills to excel.Key Features: Comprehensive coverage of data engineering fundamentals and advanced Python techniquesStep-by-step tutorials for building and deploying ETL pipelinesIn-depth guides to handling and processing big data with Python-based toolsReal-world case studies illustrating best practices and common challengesPractical exercises and projects to reinforce learning and develop h Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. Unlock the power of data and artificial intelligence with the Fundamentals Series-a comprehensive two-book collection designed to equip you with the essential skills to succeed in today's technology-driven world.Whether you're looking to build a career in data analytics, harness the potential of AI, or monetize cutting-edge tools like large language models, this series provides the ultimate roadmap.Inside, you'll discover: Fundamentals of Data Analytics - Learn how to analyze, interpret, and visualize data to drive business success and innovation. Fundamentals of Artificial Intelligence & Large Language Models - Master prompt engineering, understand AI's real-world applications, and explore lucrative opportunities in the AI economy.With beginner-friendly insights, real-world applications, and strategies for success, this series is your key to staying competitive in the digital age. Whether you're a professional, entrepreneur, or curious learner, the Fundamentals Series will help you turn knowledge into opportunity.Don't wait to take advantage of these insights-because plenty of others are already using AI to their financial benefit. Get started now to stay ahead of the game and find financial success! Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. In the age of rapidly evolving digital landscapes, cybersecurity has become more critical than ever. With cyber threats becoming more sophisticated and pervasive, traditional defense mechanisms are struggling to keep up. Enter artificial intelligence (AI) and machine learning (ML) - the game-changing technologies that are redefining the future of cybersecurity. Next-Gen Cybersecurity: AI and ML Innovations for Digital Protection by Davide Mancuso is your comprehensive guide to understanding and harnessing these powerful technologies to protect your organization from the next wave of cyber threats. What This Book Offers: This book explores the intersection of cybersecurity and cutting-edge AI and ML technologies, providing both technical insights and practical strategies for leveraging these innovations. Designed for cybersecurity professionals, IT decision-makers, and anyone interested in the future of digital protection, Next-Gen Cybersecurity offers actionable knowledge to stay ahead of the curve in an increasingly complex cyber world. Key Highlights: AI and ML Fundamentals in Cybersecurity: Learn about the core principles behind artificial intelligence and machine learning and how they can be applied to cybersecurity to solve real-world problems. From understanding the different machine learning models to getting familiar with the latest AI tools, this book covers it all. Innovations in Intrusion Detection and Prevention Systems (IDS/IPS): Dive into next-gen intrusion detection and prevention systems powered by AI. Discover how machine learning is used to detect anomalies and identify threats before they can cause significant damage. Threat Intelligence Automation: Understand how AI and ML are transforming the way threat intelligence is gathered and processed. This book demonstrates how automated systems powered by AI can analyze vast amounts of threat data to predict future attacks and proactively mitigate risks. Behavioral Analytics and Anomaly Detection: Explore how AI-driven behavioral analytics help identify abnormal patterns of activity, allowing businesses to quickly detect insider threats, compromised accounts, and unusual access behaviors that traditional methods might miss. AI in Malware Detection and Reverse Engineering: Discover the advanced capabilities of AI in detecting, analyzing, and reversing malware. This chapter explains how AI-driven systems are revolutionizing the detection and prevention of malware by learning from past attacks and improving over time. Simulating Attacks with AI: Learn how AI can be used to simulate cyberattacks and improve penetration testing. By using AI-driven attack simulations, cybersecurity professionals can better understand potential vulnerabilities and devise stronger defense mechanisms. Natural Language Processing (NLP) for Phishing Detection: Phishing attacks continue to be one of the most prevalent threats, but with the power of natural language processing (NLP) integrated into AI systems, organizations can better detect malicious emails, links, and voice phishing (vishing). About the Author: Davide Mancuso is an expert in cybersecurity with years of experience in developing and implementing AI-powered security solutions. With a background in data science, AI, and cybersecurity, Davide has helped organizations across various industries build robust digital defenses against advanced threats. In Next-Gen Cybersecurity: AI and ML Innovations for Digital Protection, he shares his expertise, offering readers a roadmap to navigating the rapidly changing cybersecurity landscape. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. With the tremendous growth and availability of the data, this book covers understanding the data, while telling a story with visualization including basic concepts about the data, the relationship and the visualizations. All the technical details that include installation and building the different visualizations are explained in a clear and systematic way. Various aspects pertaining to storytelling and visualization are explained in the book through Tableau.FeaturesProvides a hands-on approach in Tableau in a simplified manner with stepsDiscusses the broad background of data and its fundamentals, from the Internet of Everything to analyticsEmphasizes the use of context in delivering the storiesPresents case studies with the building of a dashboardPresents application areas and case studies with identification of the impactful visualizationThis book will be helpful for professionals, graduate students and senior undergraduate students in Manufacturing Engineering, Civil and Mechanical Engineering, Data Analytics and Data Visualization. This book covers understanding of the data, while telling a story with visualization including basic concepts about the data, the relationship, and the visualizations through Tableau. All the technical details that include installation and building the different visualizations are explained in clear and systematic way. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. In an ever-changing environment, organizations are required to identify and implement numerous decisions about strategic and operational activities. Organizations must consider aspects such as research and development, financial planning, data analytics, information technology, marketing, and production. However, organizational leaders must not neglect the importance of a critical element, which is human resources.The ability to cultivate intellectual capacity and healthy workplace culture can guide an organization to leverage unique abilities, achieve meaningful objectives, and maximize the possibility of sustainable success. As a result, the ability to encourage two-way learning relationships through mentorship can help organizations to improve effectiveness. A mentorship program can empower mentors, mentees, and program leaders to share best practices. The mentorship process is integral for promoting the professional and personal growth of stakeholders within an organization. Through a program tailored specifically to organizational needs, the implications of effective mentorship could positively impact individuals, groups, institutions, and broader communities.Mentorship can occur both formally and informally in diverse areas such as education, business, medicine, law, engineering, technology, arts, sciences, and multi-disciplinary domains. This book communicates considerations for the facilitation of mentorship programs and development of mentorship relationships based on experiences from a practitioner lens. Readers are reintroduced to mentorship and will learn about the value that can be created for an organization. The exploration of identities, possible outcomes, approaches, and accessibility can spark ideation about recommendations for the readers organizational context. Readers will embark on a journey to generate new or challenge existing insights through reflective guiding questions. This book communicates critical considerations for the delivery of mentorship programs and the creation of mentorship relationships. Readers are introduced to the mentorship styles and learn about why mentorship must be prioritized in their organizations. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condition: Near Fine. 1st Edition. Hardcover, xvi + 325 pages, NOT ex-library. Three blank stickers inside the front board. Else great condition, clean, bright, tight, with unmarked text. Issued without a dust jacket. -- E-Manufacturing: Fundamentals and Applications provides a comprehensive overview of the integration of information and communication technologies within manufacturing processes. The book explores how e-manufacturing can enhance productivity, quality, and responsiveness in the manufacturing sector. It delves into the fundamental principles underlying e-manufacturing, including digital production systems, real-time data management, and the role of automation and robotics. The text highlights various applications of e-manufacturing, showcasing case studies that illustrate successful implementations across different industries. Furthermore, it discusses the challenges associated with transitioning to e-manufacturing, such as workforce adaptation and technology integration. By addressing both theoretical concepts and practical applications, this volume serves as a valuable resource for academics, researchers, and industry professionals interested in the future of manufacturing in a digital age.

Paperback. Condition: new. Paperback. eCommerce Fundamentals is an systematic overview of online business essentials, including strategy formulation, online marketing and sales processes, fulfillment and post-sale processes, and technology and online analytics. It is a high-value, informative eCommerce resource specifically for small business owners and executives, startup entrepreneurs and home-based enterprises with online selling. Unlike most other books on doing online that concentrate on online marketing and sales, eCommerce Fundamentals is comprehensive in discussing not only marketing and sales, but how pre-sales, sale and post-sale processes support your particular brand of Vision, Mission and Strategy (VMS). The three site types (Online Billboards, Online Portals and Online Stores) are explained, and their advantages and disadvantages compared and contrasted. B2C and B2B online selling is the heart of eCommerce and the main focus of eCommerce Fundamentals, but we still take time to explore how to leverage the power of Online for brick-and-mortar retail, hybrid brick-and-mortar, and non-governmental organizations (NGO). The book takes a synergistic approach that emphasizes business process engineering in a Lean/Six Sigma framework. eCommerce Fundamentals shows you how to thrive in business, not just survive. You are introduced to concepts in online commerce that not only level the field against competitors, but more importantly empowers you and your organization to leave them in the dust. There are a handful of key takeaways that alone are worth the read, and that hold the potential to radically transform the success and effectiveness of your business. They represent best practice thinking and innovative breakthroughs, such as in traffic generation strategies, leveraging niche content, conversion enhancement and personalizing user/customer experience (UX/CX). In the conclusion, "the Human(H) -digital(d) Apex (Hd)tm" is introduced, a next generation in UX/CX that "ingrains humanity in the digital world". Organizational development and entrepreneurial management is touched on relative to building a thriving eCommerce business. A lot of ground is covered in eCommerce Fundamentals and by design it is not a 300 or 400 page text book. Business process engineering is personal, and needs to be right-sized and right-fitted to your specific business situation. Let's write the next 100 pages together as we collaborate on how you too can apply the power of Online and accomplish your entrepreneurial vision and mission, and realize your person dreams along the way! When you have finished reading eCommerce Fundamentals, please see the special consultation offer at the close of the book. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condition: new. Paperback. Big Data Analytics and Intelligent Techniques for Smart Cities covers fundamentals, advanced concepts, and applications of big data analytics for smart cities in a single volume. This comprehensive reference text discusses big data theory modeling and simulation for smart cities and examines case studies in a single volume. The text discusses how to develop a smart city and state-of-the-art system design, system verification, real-time control and adaptation, Internet of Things, and testbeds. It covers applications of smart cities as they relate to smart transportation/connected vehicle (CV) and intelligent transportation systems (ITS) for improved mobility, safety, and environmental protection. It will be useful as a reference text for graduate students in different areas including electrical engineering, computer science engineering, civil engineering, and electronics and communications engineering.Features: Technologies and algorithms associated with the application of big data for smart cities Discussions on big data theory modeling and simulation for smart cities Applications of smart cities as they relate to smart transportation and intelligent transportation systems (ITS) Discussions on concepts including smart education, smart culture, and smart transformation management for social and societal changes Big Data Analytics and Intelligent Techniques for Smart Cities covers fundamentals, advanced concepts, and applications of big data analytics for smart cities in a single volume. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features: Covers the fundamentals of ML and DL in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in ML/DL models Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios.Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India.Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India.Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey. The book incorporates the many facets of computational intelligence, such as machine learning and deep learning, to provide groundbreaking developments in healthcare applications. It discusses theory, analytical methods, numerical simulation, scientific techniques, analytical outcomes, and computational structuring. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. Unlock the power of data and artificial intelligence with the Fundamentals Series-a comprehensive two-book collection designed to equip you with the essential skills to succeed in today's technology-driven world.Whether you're looking to build a career in data analytics, harness the potential of AI, or monetize cutting-edge tools like large language models, this series provides the ultimate roadmap.Inside, you'll discover: Fundamentals of Data Analytics - Learn how to analyze, interpret, and visualize data to drive business success and innovation. Fundamentals of Artificial Intelligence & Large Language Models - Master prompt engineering, understand AI's real-world applications, and explore lucrative opportunities in the AI economy.With beginner-friendly insights, real-world applications, and strategies for success, this series is your key to staying competitive in the digital age. Whether you're a professional, entrepreneur, or curious learner, the Fundamentals Series will help you turn knowledge into opportunity.Don't wait to take advantage of these insights-because plenty of others are already using AI to their financial benefit. Get started now to stay ahead of the game and find financial success! Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condition: new. Paperback. Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems.The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications.Academic scientists, researchers, and students in the various domains of computer science engineering, electronics and communication engineering, and information technology, as well as industrial engineers, biomedical engineers, and management, will find this book useful. By the end of this book, you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems.Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This volume provides the important concepts necessary for a physician to participate in a reengineering process, develop decision-making skills based on probability and logic rather than 'rules,' and to measure and analyze meaningful outcomes of care delivery. This approach has been developed over ten years in a medical student-based program and has been enthusiastically embraced by medical students without backgrounds in engineering or statistics. More specifically, this text will introduce physicians to relevant and available computer software, combined with an in depth knowledge of measurement, variation, and uncertainty. It provides a basis for the transformation of data into information, information into knowledge, and knowledge into wisdom. The first quarter of the book will address understanding and visualizing data, using statistical and graphic analysis. The next quarter addresses the fundamentals of applied statistics, and the application ofconditional probability to clinical decision making. The next quarter addresses the four 'cornerstones' of modern analytics: regression, classification, association analysis, and clustering. The final section addresses the identification of outliers and their importance in understanding, the assessment of cause and effect and the limitations associated with retrospective data analysis. This toolbox will prepare the interested physician to actively engage in the identification of problem areas, the design of process-based solutions, and the continuous assessment of outcomes of clinical practice. Armed with this toolbox, the reader will be 'prepared to make a difference' in the rapidly changing world of healthcare delivery.Measurement and Analysis in Transforming Healthcare Delivery is an excellent resource for general practitioners, health administrators, and all medical professionals interacting with healthcare delivery.

Paperback. Condition: new. Paperback. Transform Numbers into Knowledge - One MATLAB Command at a TimeUnlock the full analytical power of MATLAB and elevate your understanding of probability, statistics, and data analytics from classroom theory to professional-grade practice. Crafted for learners who demand mathematical depth and hands-on code proficiency, this definitive reference condenses an entire semester's worth of lectures into 98 ultra-focused chapters. Whether you're decoding random variables, stress-testing hypotheses, or engineering Monte Carlo simulations, each topic is delivered with crystal-clear explanations and ready-to-run scripts.What Makes This Textbook Stand Out?- End-to-End Roadmap - Progress seamlessly from foundational matrix algebra through advanced statistical inference, ensuring no conceptual gaps.- Code-Driven Learning - Every formula is paired with MATLAB examples, empowering you to verify theory with live computations.- High-Impact Topics Covered - Random Number Generation & Simulation - Bayesian Updating & Conditional Probability - Hypothesis Testing, Confidence Intervals, p-Values - Maximum Likelihood & Moment-Based Estimation - Central Limit Theorem & Law of Large Numbers - Regression Analysis, ANOVA, Logistic Models - Principal Component Analysis & Clustering Algorithms- Reproducible Workflows - Set seeds, vectorize calculations, and benchmark numerical precision without trial-and-error.- Instant Application - Ideal for data science projects, engineering research, quantitative finance, and academic coursework."Finally, a MATLAB-centric statistics text that speaks fluent mathematics and executable code in the same breath." Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This volume provides the important concepts necessary for a physician to participate in a reengineering process, develop decision-making skills based on probability and logic rather than 'rules,' and to measure and analyze meaningful outcomes of care delivery. This approach has been developed over ten years in a medical student-based program and has been enthusiastically embraced by medical students without backgrounds in engineering or statistics. More specifically, this text will introduce physicians to relevant and available computer software, combined with an in depth knowledge of measurement, variation, and uncertainty. It provides a basis for the transformation of data into information, information into knowledge, and knowledge into wisdom. The first quarter of the book will address understanding and visualizing data, using statistical and graphic analysis. The next quarter addresses the fundamentals of applied statistics, and the application ofconditional probability to clinical decision making. The next quarter addresses the four 'cornerstones' of modern analytics: regression, classification, association analysis, and clustering. The final section addresses the identification of outliers and their importance in understanding, the assessment of cause and effect and the limitations associated with retrospective data analysis. This toolbox will prepare the interested physician to actively engage in the identification of problem areas, the design of process-based solutions, and the continuous assessment of outcomes of clinical practice. Armed with this toolbox, the reader will be 'prepared to make a difference' in the rapidly changing world of healthcare delivery.Measurement and Analysis in Transforming Healthcare Delivery is an excellent resource for general practitioners, health administrators, and all medical professionals interacting with healthcare delivery.

Paperback. Condition: new. Paperback. The book comprehensively covers a wide range of evolutionary computer vision methods and applications, feature selection and extraction for training and classification, and metaheuristic algorithms in image processing. It further discusses optimized image segmentation, its analysis, pattern recognition, and object detection.Features:Discusses machine learning-based analytics such as GAN networks, autoencoders, computational imaging, and quantum computingCovers deep learning algorithms in computer visionShowcases novel solutions such as multi-resolution analysis in imaging processing, and metaheuristic algorithms for tackling challenges associated with image processingHighlight optimization problems such as image segmentation and minimized feature design vectorPresents platform and simulation tools for image processing and segmentationThe book aims to get the readers familiar with the fundamentals of computational intelligence as well as the recent advancements in related technologies like smart applications of digital images, and other enabling technologies from the context of image processing and computer vision. It further covers important topics such as image watermarking, steganography, morphological processing, and optimized image segmentation. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in fields including electrical engineering, electronics, communications engineering, and computer engineering. The book comprehensively covers a wide range of evolutionary computer vision methods and applications, feature selection and extraction for training and classification, and metaheuristic algorithms in image processing. It further discusses optimized image segmentation, its analysis, pattern recognition, and object detection. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Taschenbuch. Condition: Neu. Neuware -Master's Thesis from the year 2019 in the subject Computer Science - Theory, grade: A\*, Management Center Innsbruck, language: English, abstract: This Master Thesis introduces theoretical fundamentals of Predictive Policing tools used in German police institutions such as Hot-Spot techniques, Near-Repeat approaches, Risk-terrain Analysis and Concentric-Zone Model. In times of Big Data, police work has also changed and the usage of forecasting technologies in order to prevent crime does not only vary state-wide in definitions but also in its application. Therefore, objectives and appliances are described in general. Additionally, a chronological transformation is established in order to compare lineages in Germany with those in the USA. Since Predictive Policing polarises, the research question deals with potential opportunities and challenges police institutions and the society have to deal with, when it comes to leveraging data-analytical forecasting technologies in order to prevent crime.The motivation for writing the Master Thesis about the present topic stems from the fact that it is highly current and has not yet been thoroughly studied. Preventing crime and thus ensuring a safe environment is an important field of research in our society and should be guaranteed with problem-oriented policing. Since there are varying considerations and application measures of PP according to different country side frameworks, the Thesis provides an overview about technical functioning and practical appliance within Germany. Therefore, content provides on the one hand added value for lecturers and students in the field of Public Security Management and related studies or police officers in the upper grade of the civil service. On the other hand, it serves to educate citizens about how far the technologies have progressed in this area and to what extent this will influence the lives of citizens in the future. Many police departments worldwide test software-based forecasting technologies according to their relevance in practice. Forecasting systems work with data sets about already registered crime activities. Those datasets are then complemented with socio-spatial, calendar and meteorological data. Since the amount of collected and analyzed data increases day by day, the question arises as to what extent Machine Learning and Artificial Intelligence will influence the human advice origin to predict and prevent crime.BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt 96 pp. Englisch.

Taschenbuch. Condition: Neu. Neuware -Sensor Technologies: Healthcare, Wellness and Environmental Applications explores the key aspects of sensor technologies, covering wired, wireless, and discrete sensors for the specific application domains of healthcare, wellness and environmental sensing. It discusses the social, regulatory, and design considerations specific to these domains.The book provides an application-based approach using real-world examples to illustrate the application of sensor technologies in a practical and experiential manner. The book guides the reader from the formulation of the research question, through the design and validation process, to the deployment and management phase of sensor applications. The processes and examples used in the book are primarily based on research carried out by Intel or joint academic research programs.¿Sensor Technologies: Healthcare, Wellness and Environmental Applications provides an extensive overview of sensing technologies and their applications in healthcare, wellness, and environmental monitoring. From sensor hardware to system applications and case studies, this book gives readers an in-depth understanding of the technologies and how they can be applied. I would highly recommend it to students or researchers who are interested in wireless sensing technologies and the associated applications.¿Dr. Benny LoLecturer, The Hamlyn Centre, Imperial College of London¿This timely addition to the literature on sensors covers the broad complexity of sensing, sensor types, and the vast range of existing and emerging applications in a very clearly written and accessible manner. It is particularly good at capturing the exciting possibilities that will occur as sensor networks merge with cloud-based ¿big datä analytics to provide a host of new applications that will impact directly on the individual in ways we cannot fully predict at present. It really brings this home through the use of carefully chosen case studies that bring the overwhelming concept of 'big data' down to the personal level of individual life and health.¿Dermot DiamondDirector, National Centre for Sensor Research, Principal Investigator, CLARITY Centre for Sensor Web Technologies, Dublin City University'Sensor Technologies: Healthcare, Wellness and Environmental Applications takes the reader on an end-to-end journey of sensor technologies, covering the fundamentals from an engineering perspective, introducing how the data gleaned can be both processed and visualized, in addition to offering exemplar case studies in a number of application domains. It is a must-read for those studying any undergraduate course that involves sensor technologies. It also provides a thorough foundation for those involved in the research and development of applied sensor systems. I highly recommend it to any engineer who wishes to broaden their knowledge in this area!'Chris NugentProfessor of Biomedical Engineering, University of UlsterAPress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 332 pp. Englisch.

Bündel. Condition: Neu. Neuware -- A comprehensive overview of the various fields of application of data science and artificial intelligence.- Case studies from practice to make the described concepts tangible.- Practical examples to help you carry out simple data analysis projects.- BONUS in print edition: Elektronisches Buch insideData Science, Big Data, Artificial Intelligence and Generative AI are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them.Using exercises and real-world examples, it will show you how to apply data science methods, build data platforms, and deploy data- and ML-driven projects to production. It will help you understand - and explain to various stakeholders - how to generate value from such endeavors. Along the way, it will bring essential data science concepts to life, including statistics, mathematics, and machine learning fundamentals, and explore crucial topics like critical thinking, legal and ethical considerations, and building high-performing data teams.Readers of all levels of data familiarity - from aspiring data scientists to expert engineers to data leaders - will ultimately learn: how can an organization become more data-driven, what challenges might it face, and how can they as individuals help make that journey a success.The team of authors consists of data professionals from business and academia, including data scientists, engineers, business leaders and legal experts. All are members of the Vienna Data Science Group (VDSG), an NGO that aims to establish a platform for exchanging knowledge on the application of data science, AI and machine learning, and raising awareness of the opportunities and potential risks of these technologies.WHATS INSIDE //- Critical Thinking and Data Culture: How evidence driven decision making is the base for effective AI.- Machine Learning Fundamentals: Foundations of mathematics, statistics, and ML algorithms and architectures- Natural Language Processing and Computer Vision: How to extract valuable insights from text, images and video data, for real world applications.- Foundation Models and Generative AI: Understand the strengths and challenges of generative models for text, images, video, and more.- ML and AI in Production: Turning experimentation into a working data science product.- Presenting your Results: Essential presentation techniques for data scientists. 876 pp. Englisch.

Buch. Condition: Neu. Neuware - An essential introduction to data analytics and Machine Learning techniques in the business sectorIn Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs--especially of key results--and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves.The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems.The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech.After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction.This book can help readers become well-equipped with the following skills:\* To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions\* To apply effective data dimension reduction tools to enhance supervised learning\* To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purposeThe book covers the competencies tested by several professional examinatio.

Hardcover. Condition: new. Hardcover. ARTIFICIAL INTELLIGENCE (AI) IN FORENSIC SCIENCES Foundational text for teaching and learning within the field of Artificial Intelligence (AI) as it applies to forensic science Artificial Intelligence (AI) in Forensic Sciences presents an overview of the state-of-the-art applications of Artificial Intelligence within Forensic Science, covering issues with validation and new crimes that use AI; issues with triage, preselection, identification, argumentation and explain ability; demonstrating uses of AI in forensic science; and providing discussions on bias when using AI. The text discusses the challenges for the legal presentation of AI data and interpretation and offers solutions to this problem while addressing broader practical and emerging issues in a growing area of interest in forensics. It builds on key developing areas of focus in academic and government research, providing an authoritative and well-researched perspective. Compiled by two highly qualified editors with significant experience in the field, and part of the Wiley AAFS series Forensic Science in Focus, Artificial Intelligence (AI) in Forensic Sciences includes information on: Cyber IoT, fundamentals on AI in forensic science, speaker and facial comparison, and deepfake detectionDigital-based evidence creation, 3D and AI, interoperability of standards, and forensic audio and speech analysisText analysis, video and multimedia analytics, reliability, privacy, network forensics, intelligence operations, argumentation support in court, and case applicationsIdentification of genetic markers, current state and federal legislation with regards to AI, and forensics and fingerprint analysis Providing comprehensive coverage of the subject, Artificial Intelligence (AI) in Forensic Sciences is an essential advanced text for final year undergraduates and masters students in forensic science, as well as universities teaching forensics (police, IT security, digital science and engineering), forensic product vendors and governmental and cyber security agencies. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Taschenbuch. Condition: Neu. Neuware -This book is an exhaustive guide to designing and implementing data solutions on Azure. It covers the process of managing data from end to end, starting from data collection all the way through transformation, distribution, and consumption.Modern Data Architecture on Azure begins with an introduction to the fundaments of data management, followed by a demonstration of how to build relational and non-relational data solutions on Azure. Here, you will learn data processing for complex analysis and how to work with CSV and JSON files. Moving forward, you will learn the foundational concepts of big data architecture, along with data management patterns and technology options offered by Azure. From there, yoüll be walked through the data architecture process, including data consortium on Azure, enterprise data governance, and much more. The book culminates with a deep dive into data architecture frameworks with data modeling.After reading this book, you will have a thorough understanding of data design and analytics using Azure, allowing you to collect and analyze massive amounts of data to optimize business performance, forecast future results, and more.What Will You LearnUnderstand the fundamentals of data architecture including data management, data handling ethics, data governance, and metadata managementAnalyze and understand business needs to choose the right Azure services and make informed business decisionsUnderstand Azure Cloud Data design patterns for relational and non-relational data, batch real-time processing, and ETL/ELT pipelinesModernize data architecture using Azure to leverage data and AI to enable digital transformation by securing and optimizing overall data lifecycle managementWho Is This Book For:Data solution architects, data engineers, and IT consultants who want to gain a better understanding of modern data architecture design and implementation on Azure.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 224 pp. Englisch.

Taschenbuch. Condition: Neu. Neuware -This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems.Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data.Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor.Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples.Praise for Data Mining: The Textbook¿As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It¿s a must-have for students and professors alike!' -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology'This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners.' -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at ChicagoSpringer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 768 pp. Englisch.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples.Praise for Data Mining: The Textbook - 'As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It's a must-have for students and professors alike!' -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology'This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners.' -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago.