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Data Technology in Materials Modelling (SpringerBriefs in Applied Sciences and Technology)
by Martin Thomas Horsch Silvia Chiacchiera Welchy Leite Cavalcanti Björn SchemberaThis open access book discusses advances in semantic interoperability for materials modelling, aiming at integrating data obtained from different methods and sources into common frameworks, and facilitating the development of platforms where simulation services in computational molecular engineering can be provided as well as coupled and linked to each other in a standardized and reliable way. The Virtual Materials Marketplace (VIMMP), which is open to all service providers and clients, provides a framework for offering and accessing such services, assisting the uptake of novel modelling and simulation approaches by SMEs, consultants, and industrial R&D end users. Semantic assets presented include the EngMeta metadata schema for research data infrastructures in simulation-based engineering and the collection of ontologies from VIMMP, including the ontology for simulation, modelling, and optimization (OSMO) and the VIMMP software ontology (VISO).
Data Thieves in Action: Examining the International Market for Stolen Personal Information (Palgrave Studies in Cybercrime and Cybersecurity)
by Thomas J. Holt Olga Smirnova Yi-Ting ChuaThis book examines the practices of cybercriminals who steal and sell personal information acquired through various means, including mass data breaches, to engage in cybercrime and fraud. Using data from multiple English and Russian language web forums, the authors identify the range of products sold in these active on-line marketplaces and the prospective profits earned by these actors. The social organization of these markets is analysed using sociological theory to understand the sophistication of the markets. Social network analyses of the relational networks of participants are also utilised to examine their sophistication and structure. In doing so, this work will contribute to the development of cybercrime studies, and will appeal to both social and computer scientists alike with an interest in the human aspects of cybercrime.
Data Usability in the Enterprise: How Usability Leads to Optimal Digital Experiences
by Praveen GujarEnsuring data usability is paramount to unlocking a company’s full potential and driving informed decision-making. Part of author Saurav Bhattacharya’s trilogy that covers the essential pillars of digital ecosystems—security, reliability, and usability—this book offers a comprehensive exploration of the fundamental concepts, principles, and practices essential for enhancing data accessibility and effectiveness. You’ll study the core aspects of data design, standardization, and interoperability, gaining the knowledge needed to create and maintain high-quality data environments. By examining the tools and technologies that improve data usability, along with best practices for data visualization and user-centric strategies, this book serves as an invaluable resource for professionals seeking to leverage data more effectively. The book also addresses crucial governance issues, ensuring data quality, integrity, and security are maintained. Through a detailed analysis of data governance frameworks and privacy concerns, you’ll see how to manage data responsibly. Additionally, the book includes compelling case studies that highlight successful data usability implementations, future trends, and the challenges faced in achieving optimal data usability. By fostering a culture of data literacy and usability, this book will help you and your organization navigate the evolving data landscape and harness the power of data for innovation and growth. What You Will Learn Understand the fundamental concepts and importance of data usability, including effective data design, enhancing data accessibility, and ensuring data standardization and interoperability. Review the latest tools and technologies that enhance data usability, best practices for data visualization, and strategies for implementing user-centric data approaches. Ensure data quality and integrity, while navigating data privacy and security concerns. Implement robust data governance frameworks to manage data responsibly and effectively. Who This Book Is For Cybersecurity and IT professionals
Data-Variant Kernel Analysis
by Yuichi MotaiDescribes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include data formations of offline, distributed, online, cloud, and longitudinal data, used for kernel analysis to classify and predict future state. Data-Variant Kernel Analysis: Surveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA) Develops group kernel analysis with the distributed databases to compare speed and memory usages Explores the possibility of real-time processes by synthesizing offline and online databases Applies the assembled databases to compare cloud computing environments Examines the prediction of longitudinal data with time-sequential configurations Data-Variant Kernel Analysis is a detailed reference for graduate students as well as electrical and computer engineers interested in pattern analysis and its application in colon cancer detection.
Data versus Democracy: How Big Data Algorithms Shape Opinions and Alter the Course of History
by Kris ShafferHuman attention is in the highest demand it has ever been. The drastic increase in available information has compelled individuals to find a way to sift through the media that is literally at their fingertips. Content recommendation systems have emerged as the technological solution to this social and informational problem, but they’ve also created a bigger crisis in confirming our biases by showing us only, and exactly, what it predicts we want to see. Data versus Democracy investigates and explores how, in the era of social media, human cognition, algorithmic recommendation systems, and human psychology are all working together to reinforce (and exaggerate) human bias. The dangerous confluence of these factors is driving media narratives, influencing opinions, and possibly changing election results. In this book, algorithmic recommendations, clickbait, familiarity bias, propaganda, and other pivotal concepts are analyzed and then expanded upon via fascinating and timely case studies: the 2016 US presidential election, Ferguson, GamerGate, international political movements, and more events that come to affect every one of us. What are the implications of how we engage with information in the digital age? Data versus Democracy explores this topic and an abundance of related crucial questions. We live in a culture vastly different from any that has come before. In a society where engagement is currency, we are the product. Understanding the value of our attention, how organizations operate based on this concept, and how engagement can be used against our best interests is essential in responsibly equipping ourselves against the perils of disinformation.Who This Book Is ForIndividuals who are curious about how social media algorithms work and how they can be manipulated to influence culture. Social media managers, data scientists, data administrators, and educators will find this book particularly relevant to their work.
Data Visualization: Trends and Challenges Toward Multidisciplinary Perception
by S. Margret Anouncia Hardik A. Gohel Subbiah VairamuthuThis book discusses the recent trends and developments in the fields of information processing and information visualization. In view of the increasing amount of data, there is a need to develop visualization techniques to make that data easily understandable. Presenting such approaches from various disciplines, this book serves as a useful resource for graduates.
Data Visualization: Charts, Maps, and Interactive Graphics (ASA-CRC Series on Statistical Reasoning in Science and Society)
by Robert GrantThis is the age of data. There are more innovations and more opportunities for interesting work with data than ever before, but there is also an overwhelming amount of quantitative information being published every day. Data visualisation has become big business, because communication is the difference between success and failure, no matter how clever the analysis may have been. The ability to visualize data is now a skill in demand across business, government, NGOs and academia. Data Visualization: Charts, Maps, and Interactive Graphics gives an overview of a wide range of techniques and challenges, while staying accessible to anyone interested in working with and understanding data. Features: Focusses on concepts and ways of thinking about data rather than algebra or computer code. Features 17 short chapters that can be read in one sitting. Includes chapters on big data, statistical and machine learning models, visual perception, high-dimensional data, and maps and geographic data. Contains more than 125 visualizations, most created by the author. Supported by a website with all code for creating the visualizations, further reading, datasets and practical advice on crafting the images. Whether you are a student considering a career in data science, an analyst who wants to learn more about visualization, or the manager of a team working with data, this book will introduce you to a broad range of data visualization methods. Cover image: Landscape of Change uses data about sea level rise, glacier volume decline, increasing global temperatures, and the increasing use of fossil fuels. These data lines compose a landscape shaped by the changing climate, a world in which we are now living. Copyright © Jill Pelto (jillpelto.com).
Data Visualization: A Practical Introduction
by Kieran HealyThis book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings. Provides hands-on instruction using R and ggplot2 Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistent Includes a library of data sets, code, and functions
Data Visualization: A Practical Introduction
by Kieran HealyAn accessible primer on how to create effective graphics from dataThis book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way.Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible.Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings.Provides hands-on instruction using R and ggplot2Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistentIncludes a library of data sets, code, and functions
Data Visualization: a successful design process
by Andy KirkA comprehensive yet quick guide to the best approaches to designing data visualizations, with real examples and illustrative diagrams. Whatever the desired outcome ensure success by following this expert design process. This book is for anyone who has responsibility for, or is interested in trying to find innovative and effective ways to visually analyze and communicate data. There is no skill, no knowledge and no role-based pre-requisites or expectations of anyone reading this book.
Data Visualization: Principles and Practice, Second Edition
by Alexandru C. TeleaDesigning a complete visualization system involves many subtle decisions. When designing a complex, real-world visualization system, such decisions involve many types of constraints, such as performance, platform (in)dependence, available programming languages and styles, user-interface toolkits, input/output data format constraints, integration wi
Data Visualization: Representing Information on Modern Web
by Simon Timms Andy Kirk Swizec Teller Ændrew RininslandUnleash the power of data by creating interactive, engaging, and compelling visualizations for the web About This Book * Get a portable, versatile, and flexible data visualization design approach that will help you navigate the complex path towards success * Get thorough explanation of the many visual variables and visualization taxonomy to provide you with a menu of creative options * A comprehensive and contemporary introduction to data-driven visualization design and the most effective approaches to designing impact-maximizing and cognition-amplifying visualizations Who This Book Is For This course is for developers who are excited about data and who want to share that excitement with others and it will be handy for the web developers or data scientists who want to create interactive visualizations for the web. Prior knowledge of developing web applications is required. You should have a working knowledge of both JavaScript and HTML. What You Will Learn * Harness the power of D3 by building interactive and real-time data-driven web visualizations * Find out how to use JavaScript to create compelling visualizations of social data * Identify the purpose of your visualization and your project's parameters to determine overriding design considerations across your project's execution * Apply critical thinking to visualization design and get intimate with your dataset to identify its potential visual characteristics * Explore the various features of HTML5 to design creative visualizations * Discover what data is available on Stack Overflow, Facebook, Twitter, and Google+ * Gain a solid understanding of the common D3 development idioms * Find out how to write basic D3 code for server using Node.js In Detail Do you want to create more attractive charts? Or do you have huge data sets and need to unearth the key insights in a visual manner? Data visualization is the representation and presentation of data, using proven design techniques to bring alive the patterns, stories, and key insights that are locked away. This learning path is divided into three modules. The first module will equip you with the key techniques required to overcome contemporary data visualization challenges. In the second module, Social Data Visualization with HTML5 and JavaScript, it teaches you how to leverage HTML5 techniques through JavaScript to build visualizations. In third module, Learning d3.js Data Visualization, will lead you to D3, which has emerged as one of the leading platforms to develop beautiful, interactive visualizations over the web. By the end of this course, you will have unlocked the mystery behind successful data visualizations. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: ? Data Visualization: a successful design process by Andy Kirk ? Social Data Visualization with HTML5 and JavaScript by Simon Timms ? Learning d3.js Data Visualization, Second Edition by Ændrew Rininsland and Swizec Teller Style and approach This course includes all the resources that will help you jump into creating interactive and engaging visualizations for the web. Through this comprehensive course, you'll learn how to create engaging visualizations for the web to represent your data from start to finish!
Data Visualization and Knowledge Engineering: Spotting Data Points with Artificial Intelligence (Lecture Notes on Data Engineering and Communications Technologies #32)
by Jude Hemanth Madhulika Bhatia Oana GemanThis book presents the fundamentals and advances in the field of data visualization and knowledge engineering, supported by case studies and practical examples. Data visualization and engineering has been instrumental in the development of many data-driven products and processes. As such the book promotes basic research on data visualization and knowledge engineering toward data engineering and knowledge. Visual data exploration focuses on perception of information and manipulation of data to enable even non-expert users to extract knowledge. A number of visualization techniques are used in a variety of systems that provide users with innovative ways to interact with data and reveal patterns. A variety of scalable data visualization techniques are required to deal with constantly increasing volume of data in different formats. Knowledge engineering deals with the simulation of the exchange of ideas and the development of smart information systems in which reasoning and knowledge play an important role. Presenting research in areas like data visualization and knowledge engineering, this book is a valuable resource for students, scholars and researchers in the field. Each chapter is self-contained and offers an in-depth analysis of real-world applications. It discusses topics including (but not limited to) spatial data visualization; biomedical visualization and applications; image/video summarization and visualization; perception and cognition in visualization; visualization taxonomies and models; abstract data visualization; information and graph visualization; knowledge engineering; human–machine cooperation; metamodeling; natural language processing; architectures of database, expert and knowledge-based systems; knowledge acquisition methods; applications, case studies and management issues: data administration issues and knowledge; tools for specifying and developing data and knowledge bases using tools based on communication aspects involved in implementing, designing and using KBSs in cyberspace; Semantic Web.
Data Visualization and Storytelling with Tableau (Innovations in Multimedia, Virtual Reality and Augmentation)
by Mamta Mittal Nidhi Grover RahejaTableau, one of the most widely used visualization tools, helps in illustrating the ideas of data visualization and storytelling. Through Tableau’s Data Visualization and Storytelling feature, aspiring data scientists and analysts can develop their visual analytics skills and use them in both academic and business contexts.Data Visualization and Storytelling with Tableau enables budding data analysts and data scientists to develop and sharpen their skills in the field of visual analytics and apply them in business scenarios as well as in academic context. This book approaches the Data Visualization workflow from a practical point of view, emphasizing the steps involved and the outcomes attained. A major focus of this book is the application and deployment of real-time case studies. Later chapters in this book provide comprehensive coverage for advanced topics such as data storytelling, data insights, color selection in graphs, publishing in tableau public, and misleading visualizations. Thus, this book emphasizes the need to visually examine and evaluate data through stories and interactive dashboards that are made up of appropriate graphs and charts. The case studies covered in this book are a natural extension of the visualization topics that are covered in each chapter. The intention is to empower readers to generate various dashboards, stories, graphs, charts, and maps to visualize and analyze data and support decision-making in business. Advanced charts that are pertinent to project management operations are also thoroughly explored, including comparison charts, distribution charts, composition charts, and maps. All these concepts will lay a solid foundation for data visualization applications in the minds of readers.This book is meant for data analysts, computer scientists/engineers, and industry professionals who are interested in creating different types of visualization graphs for a given data problem and drawing interesting insights from the plotted trends in order to make better business decisions in the future. Features: Introduces the world of Business Intelligence to readers through visualizations in Tableau. Discusses the need and relevance of each business graph with the help of a corresponding real-time case study. Explores the art of picking a suitable graph with an appropriate color scheme for a given scenario. Establishes the process of gaining relevant insights from the analysis of visualizations created. Provides guidance in creating innovative dashboards and driving the readers through the process of innovative storytelling with data in Tableau. Implements the concept of Exploratory Data Analysis (EDA) in Tableau.
Data Visualization For Dummies
by Mico Yuk Stephanie DiamondA straightforward, full-color guide to showcasing data so your audience can see what you mean, not just read about itBig data is big news! Every company, industry, not-for-profit, and government agency wants and needs to analyze and leverage datasets that can quickly become ponderously large. Data visualization software enables different industries to present information in ways that are memorable and relevant to their mission. This full-color guide introduces you to a variety of ways to handle and synthesize data in much more interesting ways than mere columns and rows of numbers. Learn meaningful ways to show trending and relationships, how to convey complex data in a clear, concise diagram, ways to create eye-catching visualizations, and much more!Effective data analysis involves learning how to synthesize data, especially big data, into a story and present that story in a way that resonates with the audienceThis full-color guide shows you how to analyze large amounts of data, communicate complex data in a meaningful way, and quickly slice data into various viewsExplains how to automate redundant reporting and analyses, create eye-catching visualizations, and use statistical graphics and thematic cartographyEnables you to present vast amounts of data in ways that won't overwhelm your audiencePart technical manual and part analytical guidebook, Data Visualization For Dummies is the perfect tool for transforming dull tables and charts into high-impact visuals your audience will notice...and remember.
Data Visualization for People of All Ages (ISSN)
by Nancy OrganData visualization is the art and science of making information visible. On paper and in our imaginations, it’s a language of shapes and colors that holds our best ideas and most important questions. As we find ourselves swimming in data of all kinds, visualization can help us to understand, express, and explore the richness of the world around us. No matter your age or background, this book opens the door to new ways of thinking and sharing through the power of data visualization.Data Visualization for People of All Ages is a field guide to visual literacy, born from the author’s personal experience working with world-class scholars, engineers, and scientists. By walking through the different ways of showing data—including color, angle, position, and length—you’ll learn how charts and graphs truly work so that no visualization is ever a mystery or out of reach. It doesn’t stop at what fits on a page, either. You’ll journey into cutting-edge topics like data sonification and data physicalization, using sound and touch to share data across the different senses. Packed with practical examples and exercises to help you connect the dots, this book will teach you how to create and understand data visualizations on your own—all without writing a single line of code or getting tangled up in software.Written with accessibility in mind, this book invites everyone to the table to share the joy of one of today’s most necessary skills. Perfect for home or classroom use, this friendly companion gives people of all ages everything they need to start visualizing with confidence.
Data Visualization in Excel: A Guide for Beginners, Intermediates, and Wonks (AK Peters Visualization Series)
by Jonathan SchwabishThis book closes the gap between what people think Excel can do and what they can achieve in the tool. Over the past few years, recognition of the importance of effectively visualizing data has led to an explosion of data analysis and visualization software tools. But for many people, Microsoft Excel continues to be the workhorse for their data visualization needs, not to mention the only tool that many data workers have access to. Although Excel is not a specialist data visualization platform, it does have strong capabilities. The default chart types do not need to be the limit of the tool’s data visualization capabilities, and users can extend its features by understanding some key elements and strategies. Data Visualization in Excel provides a step-by-step guide to creating more advanced and often more effective data visualizations in Excel and is the perfect guide for anyone who wants to create better, more effective, and more engaging data visualizations.
Data Visualization in R and Python
by Marco CremoniniCommunicate the data that is powering our changing world with this essential text The advent of machine learning and neural networks in recent years, along with other technologies under the broader umbrella of ‘artificial intelligence,’ has produced an explosion in Data Science research and applications. Data Visualization, which combines the technical knowledge of how to work with data and the visual and communication skills required to present it, is an integral part of this subject. The expansion of Data Science is already leading to greater demand for new approaches to Data Visualization, a process that promises only to grow. Data Visualization in R and Python offers a thorough overview of the key dimensions of this subject. Beginning with the fundamentals of data visualization with Python and R, two key environments for data science, the book proceeds to lay out a range of tools for data visualization and their applications in web dashboards, data science environments, graphics, maps, and more. With an eye towards remarkable recent progress in open-source systems and tools, this book offers a cutting-edge introduction to this rapidly growing area of research and technological development. Data Visualization in R and Python readers will also find: Coverage suitable for anyone with a foundational knowledge of R and PythonDetailed treatment of tools including the Ggplot2, Seaborn, and Altair libraries, Plotly/Dash, Shiny, and othersCase studies accompanying each chapter, with full explanations for data operations and logic for each, based on Open Data from many different sources and of different formats Data Visualization in R and Python is ideal for any student or professional looking to understand the working principles of this key field.
Data Visualization Made Simple: Insights into Becoming Visual
by Kristen SosulskiData Visualization Made Simple is a practical guide to the fundamentals, strategies, and real-world cases for data visualization, an essential skill required in today’s information-rich world. With foundations rooted in statistics, psychology, and computer science, data visualization offers practitioners in almost every field a coherent way to share findings from original research, big data, learning analytics, and more. In nine appealing chapters, the book: examines the role of data graphics in decision-making, sharing information, sparking discussions, and inspiring future research; scrutinizes data graphics, deliberates on the messages they convey, and looks at options for design visualization; and includes cases and interviews to provide a contemporary view of how data graphics are used by professionals across industries Both novices and seasoned designers in education, business, and other areas can use this book’s effective, linear process to develop data visualization literacy and promote exploratory, inquiry-based approaches to visualization problems.
Data Visualization with D3 4.x Cookbook - Second Edition
by Nick Qi ZhuIf you are a developer familiar with HTML, CSS, and JavaScript, and you wish to get the most out of D3, then this book is for you. This book can also serve as a desktop quick-reference guide for experienced data visualization developers. You’ll also find this book useful if you’re a D3 user who wants to take advantage of the new features introduced in D3 4.0. You need previous experience of D3.
Data Visualization with D3 and AngularJS
by Christoph KornerIf you are a web developer with experience in AngularJS and want to implement interactive visualizations using D3.js, this book is for you. Knowledge of SVG or D3.js will give you an edge to get the most out of this book.
Data Visualization with D3 and AngularJS
by Christoph Korner<P><P>Build dynamic and interactive visualizations from real-world data with D3 on AngularJS <P><P>About This Book <P><P>Explore the powerful vector graphics capabilities of modern browsers to build customized cross-platform visualizations using D3.js's data-driven techniques <P><P>Learn how to modularize a visualization into reusable and testable components using the powerful concepts of modern web application design with AngularJS <P><P>This is a step-by-step learning guide closely focused on developing responsive data visualization apps and AngularJS best practices with D3.js <P><P>Who This Book Is For <P><P>If you are a web developer with experience in AngularJS and want to implement interactive visualizations using D3.js, this book is for you. Knowledge of SVG or D3.js will give you an edge to get the most out of this book. <P><P>What You Will Learn <P><P>Design, implement, and integrate an interactive dashboard to visualize server logs in real time using D3 graphics <P><P>Learn cross-platform vector graphics to implement a dashboard visualization <P><P>Perform data-driven transformations on selected HTML and SVG nodes <P><P>Map, group, and filter datasets and create scales and axes <P><P>Modularize data visualization information into reusable components to seamlessly integrate them into an AngularJS application <P><P>Load, parse, and preprocess external data and autoupdate the visualization <P><P>Design various chart types such as scatter, line, bar, or area and extend built-in shapes <P><P>Create custom animations and transitions for the visualization <P><P>Implement interactions and controls for the visualization preserving two-way binding between D3 and AngularJS components <P><P>In Detail <P><P>Using D3.js, the powerful JavaScript toolkit for creating cross-platform vector graphics, you can now combine performance with maximum compatibility to build a web-based visualization and present data in an interactive and convenient way. We'll reach top-notch reusability and testability by combining D3 graphics with our favorite web application framework, AngularJS. <P><P>This book teaches the basics of vector graphics, D3, and AngularJS integration, and then dives into controlling, manipulating, and filtering data. You will learn about the testability of components and how to implement custom interactions, filters, and controllers; discover how to parse and map data in D3.js; and get a grasp on drawing D3.js built-in shapes and curves. After reading the last few chapters, you'll be able to bring life to your visualizations with more features of D3.js such as interactions, animations, and transitions. You will finish your journey by implementing a parser for different server application logs and display them on a Google Analytics style interactive dashboard.
Data Visualization with d3.js
by Swizec TellerThis book is a mini tutorial with plenty of code examples and strategies to give you many options when building your own visualizations.This book is ideal for anyone interested in data visualization. Some rudimentary knowledge of JavaScript is required.
Data Visualization with D3.js Cookbook
by Nick Qi ZhuPacked with practical recipes, this is a step-by-step guide to learning data visualization with D3 with the help of detailed illustrations and code samples.If you are a developer familiar with HTML, CSS, and JavaScript, and you wish to get the most out of D3, then this book is for you. This book can also serve as a desktop quick-reference guide for experienced data visualization developers.
Data Visualization with Excel Dashboards and Reports
by Dick KusleikaLarge corporations like IBM and Oracle are using Excel dashboards and reports as a Business Intelligence tool, and many other smaller businesses are looking to these tools in order to cut costs for budgetary reasons. An effective analyst not only has to have the technical skills to use Excel in a productive manner but must be able to synthesize data into a story, and then present that story in the most impactful way. Microsoft shows its recognition of this with Excel. In Excel, there is a major focus on business intelligence and visualization. Data Visualization with Excel Dashboards and Reports fills the gap between handling data and synthesizing data into meaningful reports. This title will show readers how to think about their data in ways other than columns and rows. Most Excel books do a nice job discussing the individual functions and tools that can be used to create an "Excel Report". Titles on Excel charts, Excel pivot tables, and other books that focus on "Tips and Tricks" are useful in their own right; however they don't hit the mark for most data analysts. The primary reason these titles miss the mark is they are too focused on the mechanical aspects of building a chart, creating a pivot table, or other functionality. They don't offer these topics in the broader picture by showing how to present and report data in the most effective way. What are the most meaningful ways to show trending? How do you show relationships in data? When is showing variances more valuable than showing actual data values? How do you deal with outliers? How do you bucket data in the most meaningful way? How do you show impossible amounts of data without inundating your audience? In Data Visualization with Excel Reports and Dashboards, readers will get answers to all of these questions. Part technical manual, part analytical guidebook; this title will help Excel users go from reporting data with simple tables full of dull numbers, to creating hi-impact reports and dashboards that will wow management both visually and substantively. This book offers a comprehensive review of a wide array of technical and analytical concepts that will help users create meaningful reports and dashboards. After reading this book, the reader will be able to: Analyze large amounts of data and report their data in a meaningful way Get better visibility into data from different perspectives Quickly slice data into various views on the fly Automate redundant reporting and analyses Create impressive dashboards and What-If analyses Understand the fundamentals of effective visualization Visualize performance comparisons Visualize changes and trends over time