Gather and analyze data successfully, identify trends, and then create overarching strategies and actionable next steps - all through Excel. This book will show even those who lack a technical background how to make advanced interactive reports with only Excel at hand. Advanced visualization is available to everyone, and this step-by-step guide will show you how. The information in this book is presented in an accessible and understandable way for everyone, regardless of the level of technical skills and proficiency in MS Excel. The dashboard development process is given in the format of step-by-step instructions, taking you through each step in detail. Universal checklists and recommendations of a practicing business analyst and trainer will help in solving various tasks when working with data visualization. Illustrations will help you perceive information easily and quickly. Make Your Data Speak will show you how to master the main rules, techniques and tricks of professional data visualization in just a few days. What You'll Learn See how interactive dashboards can be useful for a business Review basic rules for building dashboards Understand why it's important to pay attention to colors and fonts when developing a dashboard Create interactive management reports in Excel Who This Book is For Company executives and divisional managers, Middle managers, business analysts

This book addresses the challenging topic of modeling adaptive networks, which often manifest inherently complex behavior. Networks by themselves can usually be modeled using a neat, declarative, and conceptually transparent Network-Oriented Modeling approach. In contrast, adaptive networks are networks that change their structure; for example, connections in Mental Networks usually change due to learning, while connections in Social Networks change due to various social dynamics. For adaptive networks, separate procedural specifications are often added for the adaptation process. Accordingly, modelers have to deal with a less transparent, hybrid specification, part of which is often more at a programming level than at a modeling level. This book presents an overall Network-Oriented Modeling approach that makes designing adaptive network models much easier, because the adaptation process, too, is modeled in a neat, declarative, and conceptually transparent Network-Oriented Modeling manner, like the network itself. Thanks to this approach, no procedural, algorithmic, or programming skills are needed to design complex adaptive network models. A dedicated software environment is available to run these adaptive network models from their high-level specifications. Moreover, because adaptive networks are described in a network format as well, the approach can simply be applied iteratively, so that higher-order adaptive networks in which network adaptation itself is adaptive (second-order adaptation), too can be modeled just as easily. For example, this can be applied to model metaplasticity in cognitive neuroscience, or second-order adaptation in biological and social contexts. The book illustrates the usefulness of this approach via numerous examples of complex (higher-order) adaptive network models for a wide variety of biological, mental, and social processes. The book is suitable for multidisciplinary Master's and Ph.D. students without assuming much prior knowledge, although also some elementary mathematical analysis is involved. Given the detailed information provided, it can be used as an introduction to Network-Oriented Modeling for adaptive networks. The material is ideally suited for teaching undergraduate and graduate students with multidisciplinary backgrounds or interests. Lecturers will find additional material such as slides, assignments, and software.

Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments. The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book's second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security. Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing. After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques.

This new book in our popular 'Learning' series offers an easy-to-use resource for newcomers to the MySQL relational database. This tutorial explains in plain English how to set up MySQL and related software from the beginning, and how to do common tasks. The book also provides you with a foundation for more advanced database management. Readers new to the subject will gain a thorough grounding in database concepts and the most reliable ways to use MySQL. You'll learn the basics of Structured Query Language (SQL), which is used to insert and manipulate data, along with ways to: design a database; secure a database server; get more value out of a database; and, interact with the database through the medium most people prefer: a web server. Learning MySQL is the ideal introduction to relational databases, and MySQL in particular, for the growing number of people who are turning to databases for both large systems and small web-based applications: The perfect tutorial for the fast-growing database market.

Urban Informatics: Using Big Data to Understand and Serve Communities introduces the reader to the tools of data management, analysis, and manipulation using R statistical software. Designed for undergraduate and above level courses, this book is an ideal onramp for the study of urban informatics and how to translate novel data sets into new insights and practical tools. The book follows a unique pedagogical approach developed by the author to enable students to build skills by pursuing projects that inspire and motivate them. Each chapter has an Exploratory Data Assignment that prompts readers to practice their new skills on a data set of their choice. These assignments guide readers through the process of becoming familiar with the contents of a novel data set and communicating meaningful insights from the data to others. Key Features: The technical curriculum consists of both data management and analytics, including both as needed to become acquainted with and reveal the content of a new data set. Content that is contextualized in real-world applications relevant to community concerns. Unit-level assignments that educators might use as midterms or otherwise. These include Community Experience assignments that prompt students to evaluate the assumptions they have made about their data against real world information. All data sets are publicly available through the Boston Data Portal.

Learn the intricate workings of DAX and the mechanics that are necessary to solve advanced Power BI challenges. This book is all about DAX (Data Analysis Expressions), the formula language used in Power BI-Microsoft's leading self-service business intelligence application-and covers other products such as PowerPivot and SQL Server Analysis Services Tabular. You will learn how to leverage the advanced applications of DAX to solve complex tasks. Often a task seems complex due to a lack of understanding, or a misunderstanding of core principles, and how certain components interact with each other. The authors of this book use solutions and examples to teach you how to solve complex problems. They explain the intricate workings of important concepts such as Filter Context and Context Transition. You will learn how Power BI, through combining DAX building blocks (such as measures, table filtering, and data lineage), can yield extraordinary analytical power. Throughout Pro Dax with Power BI these building blocks are used to create and compose solutions for advanced DAX problems, so you can independently build solutions to your own complex problems, and gain valuable insight from your data. What You Will Learn Understand the intricate workings of DAX to solve advanced problems Deconstruct problems into manageable parts in order to create your own recipes Apply predefined solutions for addressing problems, and link back step-by-step to the mechanics of DAX, to know the foundation of this powerful query language Get fully on board with DAX, a new and evolving language, by learning best practices Who This Book Is For Anyone who wants to use Power BI to build advanced and complex models. Some experience writing DAX is helpful, but not essential if you have experience with other data query languages such as MDX or SQL.

Administrators of academic professional and technical communication (PTSC) programs have long relied upon lore--stories of what works--to understand and communicate about the work of program administration. Stories are interesting, telling, engaging, and necessary. But a discipline focused primarily on stories, especially the ephemeral stories narrated at conferences and deliberated at department meetings, usually suffice primarily to solve immediate problems and address day-to-day concerns and activities. This edited collection captures some of those stories and layers them with theoretical perspectives and reflection, to enhance their usefulness to the PTSC program administration community at large. Like the ephemeral stories PTSC program administrators are accustomed to, the stories told in this volume are set within specific institutional contexts that reflect specific institutional challenges. They emphasize the intellectual traces--the debts the authors owe to those who have informed and transformed their administrative work. In so doing, this collection creates another conversation--albeit a robust, diverse, and theoretically informed one--around which program leaders might define or redefine their roles and re-envision their administrative work as the rich, complex, intellectual engagement that we find it to be. This volume asks authors to move beyond a notion of administration as an activity based solely in institutional details and processes. In so doing, they emphasize theory as they share their reflections on core administrative processes and significant moments in the histories of their associated programs, thereby affording opportunities for critical examination in conjunction with practical advice.

Administrators of academic professional and technical communication (PTSC) programs have long relied upon lore--stories of what works--to understand and communicate about the work of program administration. Stories are interesting, telling, engaging, and necessary. But a discipline focused primarily on stories, especially the ephemeral stories narrated at conferences and deliberated at department meetings, usually suffice primarily to solve immediate problems and address day-to-day concerns and activities. This edited collection captures some of those stories and layers them with theoretical perspectives and reflection, to enhance their usefulness to the PTSC program administration community at large. Like the ephemeral stories PTSC program administrators are accustomed to, the stories told in this volume are set within specific institutional contexts that reflect specific institutional challenges. They emphasize the intellectual traces--the debts the authors owe to those who have informed and transformed their administrative work. In so doing, this collection creates another conversation--albeit a robust, diverse, and theoretically informed one--around which program leaders might define or redefine their roles and re-envision their administrative work as the rich, complex, intellectual engagement that we find it to be. This volume asks authors to move beyond a notion of administration as an activity based solely in institutional details and processes. In so doing, they emphasize theory as they share their reflections on core administrative processes and significant moments in the histories of their associated programs, thereby affording opportunities for critical examination in conjunction with practical advice.

Linked Data Management presents techniques for querying and managing Linked Data that is available on today's Web. The book shows how the abundance of Linked Data can serve as fertile ground for research and commercial applications. The text focuses on aspects of managing large-scale collections of Linked Data. It offers a detailed introduction to Linked Data and related standards, including the main principles distinguishing Linked Data from standard database technology. Chapters also describe how to generate links between datasets and explain the overall architecture of data integration systems based on Linked Data. A large part of the text is devoted to query processing in different setups. After presenting methods to publish relational data as Linked Data and efficient centralized processing, the book explores lookup-based, distributed, and parallel solutions. It then addresses advanced topics, such as reasoning, and discusses work related to read-write Linked Data for system interoperation. Despite the publication of many papers since Tim Berners-Lee developed the Linked Data principles in 2006, the field lacks a comprehensive, unified overview of the state of the art. Suitable for both researchers and practitioners, this book provides a thorough, consolidated account of the new data publishing and data integration paradigm. While the book covers query processing extensively, the Linked Data abstraction furnishes more than a mechanism for collecting, integrating, and querying data from the open Web-the Linked Data technology stack also allows for controlled, sophisticated applications deployed in an enterprise environment.

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. With this updated second edition, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask. This book provides you with the know-how to dig those answers out.

This book provides a broad introduction to search engines by integrating five different perspectives on Web search and search engines that are usually dealt with separately: the technical perspective, the user perspective, the internet-based research perspective, the economic perspective, and the societal perspective. After a general introduction to the topic, two foundational chapters present how search tools can cover the Web's content and how search engines achieve this by crawling and processing the found documents. The next chapter on user behavior covers how people phrase their search queries and interact with search engines. This knowledge builds the foundation for describing how results are ranked and presented. The following three chapters then deal with the economic side of search engines, i.e., Google and the search engine market, search engine optimization (SEO), and the intermingling of organic and sponsored search results. Next, the chapter on search skills presents techniques for improving searches through advanced search interfaces and commands. Following that, the Deep Web and how its content can be accessed is explained. The two subsequent chapters cover ways to improve the quality of search results, while the next chapter describes how to access the Deep Web. Last but not least, the following chapter deals with the societal role of search engines before the final chapter concludes the book with an outlook on the future of Web search. With this book, students and professionals in disciplines like computer science, online marketing, or library and information science will learn how search engines work, what their main shortcomings are at present, and what prospects there are for their further development. The different views presented will help them to understand not only the basic technologies but also the implications the current implementations have concerning economic exploitation and societal impact.

Blockchain is an emerging platform for developing decentralized applications and data storage, over and beyond its role as a platform for cryptocurrencies. This reference text provides a comprehensive discussion on blockchain technology from research and application perspective. Discusses different approaches for building distributed applications (DAPPS). Provides detailed listing and discussion of blockchain technology applications in solving real life problems. Covers proof of work (PoW) based blockchain consensus, and proof of stake (PoS) based blockchain consensus. Discusses blockchain algorithms including practical byzantine fault tolerance (PBFT) and simplified byzantine fault tolerance (SBFT). It comprehensively covers important topics including blockchain consensus algorithms, Ethereum, Hyperledger, blockchain scalability, smart contracts with solidity, ERC20 standards, building DApp with Golang, building DApp using Hyperledger, building PoCs with Hyperledger fabric, blockchain as a server, blockchain security and privacy. The text will serve as a useful text for senior undergraduate and graduate students in interdisciplinary areas including electronics and communications engineering, electrical engineering, computer science, and information technology.

There is an ongoing data explosion transpiring that will make previous creations, collections, and storage of data look trivial. Big Data, Mining, and Analytics: Components of Strategic Decision Making ties together big data, data mining, and analytics to explain how readers can leverage them to extract valuable insights from their data. Facilitating a clear understanding of big data, it supplies authoritative insights from expert contributors into leveraging data resources, including big data, to improve decision making. Illustrating basic approaches of business intelligence to the more complex methods of data and text mining, the book guides readers through the process of extracting valuable knowledge from the varieties of data currently being generated in the brick and mortar and internet environments. It considers the broad spectrum of analytics approaches for decision making, including dashboards, OLAP cubes, data mining, and text mining. Includes a foreword by Thomas H. Davenport, Distinguished Professor, Babson College; Fellow, MIT Center for Digital Business; and Co-Founder, International Institute for Analytics Introduces text mining and the transforming of unstructured data into useful information Examines real time wireless medical data acquisition for today's healthcare and data mining challenges Presents the contributions of big data experts from academia and industry, including SAS Highlights the most exciting emerging technologies for big data Filled with examples that illustrate the value of analytics throughout, the book outlines a conceptual framework for data modeling that can help you immediately improve your own analytics and decision-making processes. It also provides in-depth coverage of analyzing unstructured data with text mining methods.

In today's competitive business environment, most companies realize that the better they can manage their customer relationships, the more successful they will become. Customer Relationship Management (CRM) software systems are key tools for companies to manage the customer-facing processes of their businesses. However, many companies have resisted implementing this most critical customer-oriented application due in large part to the lack of a single-point resource on implementing a CRM system. This book attempts to fill that gap. Implementing SAP (R) CRM will help technologists and managers come to grips with the vision, concept, and technology of CRM. It begins by laying out the groundwork for understanding CRM. It explains the concept and context of CRM and the tangible business benefits of CRM adoption. Demonstrating a professional approach to the evaluation and selection of SAP, it details the critical success factors (CSFs), patterns, and anti-patterns of a successful SAP CRM implementation. CRM implementations can add significant benefit to the company's bottom line only if the company first transforms itself into a customer-centric and customer-responsive enterprise. This book explains what it means to be a customer-centric and responsive enterprise, and provides a framework for business operations based on customer relationships, rather than the traditional four Ps (product, positioning, price, promotion). It further spells out business process reengineering (BPR) strategies to configure internal business processes and operations with SAP CRM to improve customer-facing strategies, services, and relationships.

Understanding Databases: Concepts and Practice is an accessible, highly visual introduction to database systems for undergraduate students across many majors. Designed for self-contained first courses in the subject, this interactive e-textbook covers fundamental database topics including conceptual design, the relational data model, relational algebra and calculus, Structured Query Language (SQL), database manipulation, transaction management, and database design theory. Visual components and self-assessment features provide a more engaging and immersive method of learning that enables students to develop a solid foundation in both database theory and practical application. Concise, easy-to-digest chapters offer ample opportunities for students to practice and master the material, and include a variety of solved real-world problems, self-check questions, and hands-on collaborative activities that task students to build a functioning database. This Enhanced eText also offers interactive multiple-choice questions with immediate feedback that allow students to self-assess as they proceed through the book. Case studies, illustrative examples, color summary figures and tables with annotations, and other pedagogical tools are integrated throughout the text to increase comprehension and retention of key concepts and help strengthen students' problem-solving skills.

Cryptography, the science of encoding and decoding information, allows people to do online banking, online trading, and make online purchases, without worrying that their personal information is being compromised. The dramatic increase of information transmitted electronically has led to an increased reliance on cryptography. This book discusses the theories and concepts behind modern cryptography and demonstrates how to develop and implement cryptographic algorithms using C++ programming language. Written for programmers and engineers, Practical Cryptography explains how you can use cryptography to maintain the privacy of computer data. It describes dozens of cryptography algorithms, gives practical advice on how to implement them into cryptographic software, and shows how they can be used to solve security problems. Covering the latest developments in practical cryptographic techniques, this book shows you how to build security into your computer applications, networks, and storage. Suitable for undergraduate and postgraduate students in cryptography, network security, and other security-related courses, this book will also help anyone involved in computer and network security who wants to learn the nuts and bolts of practical cryptography.

Originating from Facebook, LinkedIn, Twitter, Instagram, YouTube, and many other networking sites, the social media shared by users and the associated metadata are collectively known as user generated content (UGC). To analyze UGC and glean insight about user behavior, robust techniques are needed to tackle the huge amount of real-time, multimedia, and multilingual data. Researchers must also know how to assess the social aspects of UGC, such as user relations and influential users. Mining User Generated Content is the first focused effort to compile state-of-the-art research and address future directions of UGC. It explains how to collect, index, and analyze UGC to uncover social trends and user habits. Divided into four parts, the book focuses on the mining and applications of UGC. The first part presents an introduction to this new and exciting topic. Covering the mining of UGC of different medium types, the second part discusses the social annotation of UGC, social network graph construction and community mining, mining of UGC to assist in music retrieval, and the popular but difficult topic of UGC sentiment analysis. The third part describes the mining and searching of various types of UGC, including knowledge extraction, search techniques for UGC content, and a specific study on the analysis and annotation of Japanese blogs. The fourth part on applications explores the use of UGC to support question-answering, information summarization, and recommendations.

A Versatile Framework for Handling Subdivided Geometric Objects Combinatorial Maps: Efficient Data Structures for Computer Graphics and Image Processing gathers important ideas related to combinatorial maps and explains how the maps are applied in geometric modeling and image processing. It focuses on two subclasses of combinatorial maps: n-Gmaps and n-maps. Suitable for researchers and graduate students in geometric modeling, computational and discrete geometry, computer graphics, and image processing and analysis, the book presents the data structures, operations, and algorithms that are useful in handling subdivided geometric objects. It shows how to study data structures for the explicit representation of subdivided geometric objects and describes operations for handling the structures. The book also illustrates results of the design of data structures and operations.

Big Data: A Business and Legal Guide supplies a clear understanding of the interrelationships between Big Data, the new business insights it reveals, and the laws, regulations, and contracting practices that impact the use of the insights and the data. Providing business executives and lawyers (in-house and in private practice) with an accessible primer on Big Data and its business implications, this book will enable readers to quickly grasp the key issues and effectively implement the right solutions to collecting, licensing, handling, and using Big Data. The book brings together subject matter experts who examine a different area of law in each chapter and explain how these laws can affect the way your business or organization can use Big Data. These experts also supply recommendations as to the steps your organization can take to maximize Big Data opportunities without increasing risk and liability to your organization. Provides a new way of thinking about Big Data that will help readers address emerging issues Supplies real-world advice and practical ways to handle the issues Uses examples pulled from the news and cases to illustrate points Includes a non-technical Big Data primer that discusses the characteristics of Big Data and distinguishes it from traditional database models Taking a cross-disciplinary approach, the book will help executives, managers, and counsel better understand the interrelationships between Big Data, decisions based on Big Data, and the laws, regulations, and contracting practices that impact its use. After reading this book, you will be able to think more broadly about the best way to harness Big Data in your business and establish procedures to ensure that legal considerations are part of the decision.

CRYPTOGRAPHY, INFORMATION THEORY, AND ERROR-CORRECTION A rich examination of the technologies supporting secure digital information transfers from respected leaders in the field As technology continues to evolve Cryptography, Information Theory, and Error-Correction: A Handbook for the 21ST Century is an indispensable resource for anyone interested in the secure exchange of financial information. Identity theft, cybercrime, and other security issues have taken center stage as information becomes easier to access. Three disciplines offer solutions to these digital challenges: cryptography, information theory, and error-correction, all of which are addressed in this book. This book is geared toward a broad audience. It is an excellent reference for both graduate and undergraduate students of mathematics, computer science, cybersecurity, and engineering. It is also an authoritative overview for professionals working at financial institutions, law firms, and governments who need up-to-date information to make critical decisions. The book's discussions will be of interest to those involved in blockchains as well as those working in companies developing and applying security for new products, like self-driving cars. With its reader-friendly style and interdisciplinary emphasis this book serves as both an ideal teaching text and a tool for self-learning for IT professionals, statisticians, mathematicians, computer scientists, electrical engineers, and entrepreneurs. Six new chapters cover current topics like Internet of Things security, new identities in information theory, blockchains, cryptocurrency, compression, cloud computing and storage. Increased security and applicable research in elliptic curve cryptography are also featured. The book also: Shares vital, new research in the field of information theory Provides quantum cryptography updates Includes over 350 worked examples and problems for greater understanding of ideas. Cryptography, Information Theory, and Error-Correction guides readers in their understanding of reliable tools that can be used to store or transmit digital information safely.

Leverage the power of Talent Intelligence (TI) to make evidence-informed decisions that drive business performance by using data about people, skills, jobs, business functions and geographies. Improved access to people and business data has created huge opportunities for the HR function. However, simply having access to this data is not enough. HR professionals need to know how to analyse the data, know what questions to ask of it and where and how the insights from the data can add the most value. Talent Intelligence is a practical guide that explains everything HR professionals need to know to achieve this. It outlines what Talent Intelligence (TI) is why it's important, how to use it to improve business results and includes guidance on how HR professionals can build the business case for it. This book also explains how and why talent intelligence is different from workforce planning, sourcing research and standard predictive HR analytics and shows how to assess where in the organization talent intelligence can have the biggest impact and how to demonstrate the results to all stakeholders. Most importantly, this book covers KPIs and metrics for success, short-term and long-term TI goals, an outline of what success looks like and the skills needed for effective Talent Intelligence. It also features case studies from organizations including Philips, Barclays and Kimberly-Clark.

Do you enjoy completing puzzles? Perhaps one of the most challenging (yet rewarding) puzzles is delivering a successful data warehouse suitable for data mining and analytics. The Analytical Puzzle describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organisation. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualisation and mobile devices. This book describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organisation. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualisation and mobile devices.

With today's consumers spending more time on their mobiles than on their PCs, new methods of empirical stochastic modeling have emerged that can provide marketers with detailed information about the products, content, and services their customers desire. Data Mining Mobile Devices defines the collection of machine-sensed environmental data pertaining to human social behavior. It explains how the integration of data mining and machine learning can enable the modeling of conversation context, proximity sensing, and geospatial location throughout large communities of mobile users. Examines the construction and leveraging of mobile sites Describes how to use mobile apps to gather key data about consumers' behavior and preferences Discusses mobile mobs, which can be differentiated as distinct marketplaces-including Apple (R), Google (R), Facebook (R), Amazon (R), and Twitter (R) Provides detailed coverage of mobile analytics via clustering, text, and classification AI software and techniques Mobile devices serve as detailed diaries of a person, continuously and intimately broadcasting where, how, when, and what products, services, and content your consumers desire. The future is mobile-data mining starts and stops in consumers' pockets. Describing how to analyze Wi-Fi and GPS data from websites and apps, the book explains how to model mined data through the use of artificial intelligence software. It also discusses the monetization of mobile devices' desires and preferences that can lead to the triangulated marketing of content, products, or services to billions of consumers-in a relevant, anonymous, and personal manner.

The Complete Book of Data Anonymization: From Planning to Implementation supplies a 360-degree view of data privacy protection using data anonymization. It examines data anonymization from both a practitioner's and a program sponsor's perspective. Discussing analysis, planning, setup, and governance, it illustrates the entire process of adapting and implementing anonymization tools and programs. Part I of the book begins by explaining what data anonymization is. It describes how to scope a data anonymization program as well as the challenges involved when planning for this initiative at an enterprisewide level. Part II describes the different solution patterns and techniques available for data anonymization. It explains how to select a pattern and technique and provides a phased approach towards data anonymization for an application. A cutting-edge guide to data anonymization implementation, this book delves far beyond data anonymization techniques to supply you with the wide-ranging perspective required to ensure comprehensive protection against misuse of data.

Visualization and Verbalization of Data shows how correspondence analysis and related techniques enable the display of data in graphical form, which results in the verbalization of the structures in data. Renowned researchers in the field trace the history of these techniques and cover their current applications. The first part of the book explains the historical origins of correspondence analysis and associated methods. The second part concentrates on the contributions made by the school of Jean-Paul Benzecri and related movements, such as social space and geometric data analysis. Although these topics are viewed from a French perspective, the book makes them understandable to an international audience. Throughout the text, well-known experts illustrate the use of the methods in practice. Examples include the spatial visualization of multivariate data, cluster analysis in computer science, the transformation of a textual data set into numerical data, the use of quantitative and qualitative variables in multiple factor analysis, different possibilities of recoding data prior to visualization, and the application of duality diagram theory to the analysis of a contingency table.