Effective Data Visualization

The Right Chart for the Right Data

Author: Stephanie D. H. Evergreen

Publisher: SAGE Publications

ISBN:

Category: Social Science

Page: 352

View: 402

Written by sought-after speaker, designer, and researcher Stephanie D. H. Evergreen, Effective Data Visualization shows readers how to create Excel charts and graphs that best communicate data findings. This comprehensive how-to guide functions as a set of blueprints—supported by research and the author’s extensive experience with clients in industries all over the world—for conveying data in an impactful way. Delivered in Evergreen’s humorous and approachable style, the book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for making the chosen graph in Excel. New to the Second Edition is a completely re-written chapter on qualitative data; inclusion of 9 new quantitative graph types; new shortcuts in Excel; and entirely new chapter on Sharing Your Data with the World which includes advice on using dashboards; and lots of new examples throughout. The Second Edition is also presented in full color.

Big Data Visualization

Author: James D. Miller

Publisher: Packt Publishing Ltd

ISBN:

Category: Computers

Page: 304

View: 334

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.

Data Visualization

Principles and Practice, Second Edition

Author: Alexandru C. Telea

Publisher: CRC Press

ISBN:

Category: Computers

Page: 617

View: 607

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.

Designing Data Visualizations

Representing Informational Relationships

Author: Julie Steele

Publisher: "O'Reilly Media, Inc."

ISBN:

Category: Computers

Page: 93

View: 637

"Intentional communcation from data to display"--Cover.

Cool Infographics

Effective Communication with Data Visualization and Design

Author: Randy Krum

Publisher: John Wiley & Sons

ISBN:

Category: Computers

Page: 368

View: 256

Make information memorable with creative visual designtechniques Research shows that visual information is more quickly andeasily understood, and much more likely to be remembered. Thisinnovative book presents the design process and the best softwaretools for creating infographics that communicate. Including aspecial section on how to construct the increasingly popularinfographic resume, the book offers graphic designers, marketers,and business professionals vital information on the most effectiveways to present data. Explains why infographics and data visualizations work Shares the tools and techniques for creating greatinfographics Covers online infographics used for marketing, including socialmedia and search engine optimization (SEO) Shows how to market your skills with a visual, infographicresume Explores the many internal business uses of infographics,including board meeting presentations, annual reports, consumerresearch statistics, marketing strategies, business plans, andvisual explanations of products and services to your customers With Cool Infographics, you'll learn to createinfographics to successfully reach your target audience and tellclear stories with your data.

Handbook of Data Visualization

Author: Chun-houh Chen

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 936

View: 453

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.

Applied Data Visualization with R and ggplot2

Create useful, elaborate, and visually appealing plots

Author: Dr. Tania Moulik

Publisher: Packt Publishing Ltd

ISBN:

Category: Computers

Page: 140

View: 298

Develop informative and aesthetic visualizations that enable effective data analysis in less time Key Features Discover structure of ggplot2, grammar of graphics, and geometric objects Study how to design and implement visualization from scratch Explore the advantages of using advanced plots Book Description Applied Data Visualization with R and ggplot2 introduces you to the world of data visualization by taking you through the basic features of ggplot2. To start with, you’ll learn how to set up the R environment, followed by getting insights into the grammar of graphics and geometric objects before you explore the plotting techniques. You’ll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your data into aesthetical graphs. Once you’ve grasped the basics, you’ll move on to studying simple plots such as histograms and advanced plots such as superimposing and density plots. You’ll also get to grips with plotting trends, correlations, and statistical summaries. By the end of this book, you’ll have created data visualizations that will impress your clients. What you will learn Set up the R environment, RStudio, and understand structure of ggplot2 Distinguish variables and use best practices to visualize them Change visualization defaults to reveal more information about data Implement the grammar of graphics in ggplot2 such as scales and faceting Build complex and aesthetic visualizations with ggplot2 analysis methods Logically and systematically explore complex relationships Compare variables in a single visual, with advanced plotting methods Who this book is for Applied Data Visualization with R and ggplot2 is for you if you are a professional working with data and R. This book is also for students who want to enhance their data analysis skills by adding informative and professional visualizations. It is assumed that you know basics of the R language and its commands and objects.

Mastering Python Data Visualization

Author: Kirthi Raman

Publisher: Packt Publishing Ltd

ISBN:

Category: Computers

Page: 372

View: 633

Generate effective results in a variety of visually appealing charts using the plotting packages in Python About This Book Explore various tools and their strengths while building meaningful representations that can make it easier to understand data Packed with computational methods and algorithms in diverse fields of science Written in an easy-to-follow categorical style, this book discusses some niche techniques that will make your code easier to work with and reuse Who This Book Is For If you are a Python developer who performs data visualization and wants to develop existing knowledge about Python to build analytical results and produce some amazing visual display, then this book is for you. A basic knowledge level and understanding of Python libraries is assumed. What You Will Learn Gather, cleanse, access, and map data to a visual framework Recognize which visualization method is applicable and learn best practices for data visualization Get acquainted with reader-driven narratives and author-driven narratives and the principles of perception Understand why Python is an effective tool to be used for numerical computation much like MATLAB, and explore some interesting data structures that come with it Explore with various visualization choices how Python can be very useful in computation in the field of finance and statistics Get to know why Python is the second choice after Java, and is used frequently in the field of machine learning Compare Python with other visualization approaches using Julia and a JavaScript-based framework such as D3.js Discover how Python can be used in conjunction with NoSQL such as Hive to produce results efficiently in a distributed environment In Detail Python has a handful of open source libraries for numerical computations involving optimization, linear algebra, integration, interpolation, and other special functions using array objects, machine learning, data mining, and plotting. Pandas have a productive environment for data analysis. These libraries have a specific purpose and play an important role in the research into diverse domains including economics, finance, biological sciences, social science, health care, and many more. The variety of tools and approaches available within Python community is stunning, and can bolster and enhance visual story experiences. This book offers practical guidance to help you on the journey to effective data visualization. Commencing with a chapter on the data framework, which explains the transformation of data into information and eventually knowledge, this book subsequently covers the complete visualization process using the most popular Python libraries with working examples. You will learn the usage of Numpy, Scipy, IPython, MatPlotLib, Pandas, Patsy, and Scikit-Learn with a focus on generating results that can be visualized in many different ways. Further chapters are aimed at not only showing advanced techniques such as interactive plotting; numerical, graphical linear, and non-linear regression; clustering and classification, but also in helping you understand the aesthetics and best practices of data visualization. The book concludes with interesting examples such as social networks, directed graph examples in real-life, data structures appropriate for these problems, and network analysis. By the end of this book, you will be able to effectively solve a broad set of data analysis problems. Style and approach The approach of this book is not step by step, but rather categorical. The categories are based on fields such as bioinformatics, statistical and machine learning, financial computation, and linear algebra. This approach is beneficial for the community in many different fields of work and also helps you learn how one approach can make sense across many fields

Data Fluency

Empowering Your Organization with Effective Data Communication

Author: Zach Gemignani

Publisher: John Wiley & Sons

ISBN:

Category: Computers

Page: 288

View: 895

Provides information about effective visualization and communication approaches to analytical data presentation, covering such topics as audience and user needs, data presentation tools, layout and styling, and common design failures.