Voted one of the 'six best books for data geeks' by The Financial Times. Read the review here. Lecturers, request your electronic inspection copy. Never has it been more essential to work in the world of data. Scholars and students need to be able to analyze, design and curate information into useful tools of communication, insight and understanding. This book is the starting point in learning the process and skills of data visualization, teaching the concepts and skills of how to present data and inspiring effective visual design. Benefits of this book: A flexible step-by-step journey that equips you to achieve great data visualization A curated collection of classic and contemporary examples, giving illustrations of good and bad practice Examples on every page to give creative inspiration Illustrations of good and bad practice show you how to critically evaluate and improve your own work Advice and experience from the best designers in the field Loads of online practical help, checklists, case studies and exercises make this the most comprehensive text available
The first of its kind - a data visualisation book combining beautiful presentation with student-specific guidance and learning features - this resource empowers and inspires readers to be able to present data effectively
Voted one of the 'six best books for data geeks' by The Financial Times. Read the review here Lecturers, request your electronic inspection copy Never has it been more essential to work in the world of data. Scholars and students need to be able to analyze, design and curate information into useful tools of communication, insight and understanding. This book is the starting point in learning the process and skills of data visualization, teaching the concepts and skills of how to present data and inspiring effective visual design. Benefits of this book: A flexible step-by-step journey that equips you to achieve great data visualization A curated collection of classic and contemporary examples, giving illustrations of good and bad practice Examples on every page to give creative inspiration Illustrations of good and bad practice show you how to critically evaluate and improve your own work Advice and experience from the best designers in the field Loads of online practical help, checklists, case studies and exercises make this the most comprehensive text available
A 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.
Computers by Chun-houh Chen,Wolfgang Karl Härdle,Antony Unwin
Author: Chun-houh Chen,Wolfgang Karl Härdle,Antony Unwin
Publisher: Springer Science & Business Media
Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data displays. This book gives an overview of modern data visualization methods, both in theory and practice. It details modern graphical tools such as mosaic plots, parallel coordinate plots, and linked views. Coverage also examines graphical methodology for particular areas of statistics, for example Bayesian analysis, genomic data and cluster analysis, as well software for graphics.
Language Arts & Disciplines by Jonathan Gray,Lucy Chambers,Liliana Bounegru
Author: Jonathan Gray,Lucy Chambers,Liliana Bounegru
Publisher: "O'Reilly Media, Inc."
Category: Language Arts & Disciplines
When you combine the sheer scale and range of digital information now available with a journalist’s "nose for news" and her ability to tell a compelling story, a new world of possibility opens up. With The Data Journalism Handbook, you’ll explore the potential, limits, and applied uses of this new and fascinating field. This valuable handbook has attracted scores of contributors since the European Journalism Centre and the Open Knowledge Foundation launched the project at MozFest 2011. Through a collection of tips and techniques from leading journalists, professors, software developers, and data analysts, you’ll learn how data can be either the source of data journalism or a tool with which the story is told—or both. Examine the use of data journalism at the BBC, the Chicago Tribune, the Guardian, and other news organizations Explore in-depth case studies on elections, riots, school performance, and corruption Learn how to find data from the Web, through freedom of information laws, and by "crowd sourcing" Extract information from raw data with tips for working with numbers and statistics and using data visualization Deliver data through infographics, news apps, open data platforms, and download links
Designing 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 with third-party code, and more. Focusing on those techniques and methods with the broadest applicability across fields, the second edition of Data Visualization: Principles and Practice provides a streamlined introduction to various visualization techniques. The book illustrates a wide variety of applications of data visualizations, illustrating the range of problems that can be tackled by such methods, and emphasizes the strong connections between visualization and related disciplines such as imaging and computer graphics. It covers a wide range of sub-topics in data visualization: data representation; visualization of scalar, vector, tensor, and volumetric data; image processing and domain modeling techniques; and information visualization. See What’s New in the Second Edition: Additional visualization algorithms and techniques New examples of combined techniques for diffusion tensor imaging (DTI) visualization, illustrative fiber track rendering, and fiber bundling techniques Additional techniques for point-cloud reconstruction Additional advanced image segmentation algorithms Several important software systems and libraries Algorithmic and software design issues are illustrated throughout by (pseudo)code fragments written in the C++ programming language. Exercises covering the topics discussed in the book, as well as datasets and source code, are also provided as additional online resources.
A Primer on Making Informative and Compelling Figures
Author: Claus O. Wilke
Publisher: O'Reilly Media
Effective visualization is the best way to communicate information from the increasingly large and complex datasets in natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems and pitfalls and provides simple and clear guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color use as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure that you provide key information in multiple ways Use our directory of visualizations: a graphical guide to the most commonly used types of data visualizations Get extensive examples of good and bad figures; learn how to use figures in a document or report Learn methods for visualizing amounts and proportions, paired data, trends, and time series Visualize distributions with histograms and density plots, boxplots and violin plots, and ridgeline plots
Learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization About This Book This unique guide teaches you how to visualize your cluttered, huge amounts of big data with ease It is rich with ample options and solid use cases for big data visualization, and is a must-have book for your shelf Improve your decision-making by visualizing your big data the right way Who This Book Is For This book is for data analysts or those with a basic knowledge of big data analysis who want to learn big data visualization in order to make their analysis more useful. You need sufficient knowledge of big data platform tools such as Hadoop and also some experience with programming languages such as R. This book will be great for those who are familiar with conventional data visualizations and now want to widen their horizon by exploring big data visualizations. What You Will Learn Understand how basic analytics is affected by big data Deep dive into effective and efficient ways of visualizing big data Get to know various approaches (using various technologies) to address the challenges of visualizing big data Comprehend the concepts and models used to visualize big data Know how to visualize big data in real time and for different use cases Understand how to integrate popular dashboard visualization tools such as Splunk and Tableau Get to know the value and process of integrating visual big data with BI tools such as Tableau Make sense of the visualization options for big data, based upon the best suited visualization techniques for big data In Detail When it comes to big data, regular data visualization tools with basic features become insufficient. This book covers the concepts and models used to visualize big data, with a focus on efficient visualizations. This book works around big data visualizations and the challenges around visualizing big data and address characteristic challenges of visualizing like speed in accessing, understanding/adding context to, improving the quality of the data, displaying results, outliers, and so on. We focus on the most popular libraries to execute the tasks of big data visualization and explore "big data oriented" tools such as Hadoop and Tableau. We will show you how data changes with different variables and for different use cases with step-through topics such as: importing data to something like Hadoop, basic analytics. The choice of visualizations depends on the most suited techniques for big data, and we will show you the various options for big data visualizations based upon industry-proven techniques. You will then learn how to integrate popular visualization tools with graphing databases to see how huge amounts of certain data. Finally, you will find out how to display the integration of visual big data with BI using Cognos BI. Style and approach With the help of insightful real-world use cases, we'll tackle data in the world of big data. The scalability and hugeness of the data makes big data visualizations different from normal data visualizations, and this book addresses all the difficulties encountered by professionals while visualizing their big data.
Technology & Engineering by Charles D. Hansen,Chris R. Johnson
The Visualization Handbook provides an overview of the field of visualization by presenting the basic concepts, providing a snapshot of current visualization software systems, and examining research topics that are advancing the field. This text is intended for a broad audience, including not only the visualization expert seeking advanced methods to solve a particular problem, but also the novice looking for general background information on visualization topics. The largest collection of state-of-the-art visualization research yet gathered in a single volume, this book includes articles by a “who’s who of international scientific visualization researchers covering every aspect of the discipline, including: · Virtual environments for visualization · Basic visualization algorithms · Large-scale data visualization · Scalar data isosurface methods · Visualization software and frameworks · Scalar data volume rendering · Perceptual issues in visualization · Various application topics, including information visualization. * Edited by two of the best known people in the world on the subject; chapter authors are authoritative experts in their own fields; * Covers a wide range of topics, in 47 chapters, representing the state-of-the-art of scientific visualization.
Data Design: Visualising Quantities, Locations, Connections is a highly practical guide to the graphic representation of quantities, locations, connections and other forms of data, founded on solid design principles.
Unchain your data from the desktop with responsive visualizations Building Responsive Data Visualization for the Web is a handbook for any front-end development team needing a framework for integrating responsive web design into the current workflow. Written by a leading industry expert and design lead at Starbase Go, this book provides a wealth of information and practical guidance from the perspective of a real-world designer. You'll walk through the process of building data visualizations responsively as you learn best practices that build upon responsive web design principles, and get the hands-on practice you need with exercises, examples, and source code provided in every chapter. These strategies are designed to be implemented by teams large and small, with varying skill sets, so you can apply these concepts and skills to your project right away. Responsive web design is the practice of building a website to suit base browser capability, then adding features that enhance the experience based on the user's device's capabilities. Applying these ideas to data produces visualizations that always look as if they were designed specifically for the device through which they are viewed. This book shows you how to incorporate these principles into your current practices, with highly practical hands-on training. Examine the hard data surrounding responsive design Master best practices with hands-on exercises Learn data-based document manipulation using D3.js Adapt your current strategies to responsive workflows Data is growing exponentially, and the need to visualize it in any context has become crucial. Traditional visualizations allow important data to become lost when viewed on a small screen, and the web traffic speaks for itself – viewers repeatedly demonstrate their preference for responsive design. If you're ready to create more accessible, take-anywhere visualizations, Building Responsive Data Visualization for the Web is your tailor-made solution.
Author: Ritchie S. King
Publisher: Addison-Wesley Professional
A Data Visualization Guide for Business Professionals
Author: Cole Nussbaumer Knaflic
Publisher: John Wiley & Sons
Category: Business & Economics
Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it!
Digital technology has not only revolutionized the way designers work, but also the kinds of designs they produce. The development of the computer as a design environment has encouraged a new breed of digital designer; keen to explore the unique creative potential of the computer as an input/output device. Data-driven Graphic Design introduces the creative potential of computational data and how it can be used to inform and create everything from typography, print and moving graphics to interactive design and physical installations. Using code as a creative environment allows designers to step outside the boundaries of commercial software tools, and create a set of unique, digitally informed pieces of work. The use of code offers a new way of thinking about and creating design for the digital environment. Each chapter outlines key concepts and techniques, before exploring a range of innovative projects through case studies and interviews with the artists and designers who created them. These provide an inspirational, real-world context for every technique. Finally each chapter concludes with a Code section, guiding you through the process of experimenting with each technique yourself (with sample projects and code examples using the popular Processing language supplied online to get you started).
Medical devices are often very complex, but while there are differences in design from one manufacturer to another, the principles of operation and, more importantly, the physiological and anatomical characteristics on which they operate are universal. Introduction to Biomedical Engineering Technology, Second Edition explains the uses and applications of medical technology and the principles of medical equipment management to familiarize readers with their prospective work environment. Written by an experienced biomedical engineering technologist, the book describes the technological devices, various hardware, tools, and test equipment used in today’s health-care arena. Photographs of representative equipment; the technical, physiological, and anatomical basis for their function; and where they are commonly found in hospitals are detailed for a wide range of biomedical devices, from defibrillators to electrosurgery units. Throughout, the text incorporates real-life examples of the work that biomedical engineering technologists do. Appendices supply useful information such as normal medical values, a list of regulatory bodies, Internet resources, and information on training programs. Thoroughly revised and updated, this second edition includes more examples and illustrations as well as end-of-chapter questions to test readers’ understanding. This accessible text supplies an essential overview of clinical equipment and the devices that are used directly with patients in the course of their care for diagnostic or treatment purposes. The author’s practical approach and organization, outlining everyday functions and applications of the various medical devices, prepares readers for situations they will encounter on the job. What’s New in This Edition: Revised and updated throughout, including a wider range of devices, full-color anatomy illustrations, and more information about test equipment New, integrated end-of-chapter questions More real-life examples of Biomedical Engineering Technologist (BMET) work, including the adventures of "Joe Biomed" and his colleagues New appendices with information about normal medical values, regulatory bodies, educational programs in the United States and Canada, international BMET associations, Internet resources, and lists of test equipment manufacturers More illustrations
A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.