Data Visualisation

Autore: Andy Kirk
Editore: SAGE
ISBN: 1526482908
Grandezza: 76,43 MB
Formato: PDF, ePub
Vista: 1166
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One of the "six best books for data geeks" - Financial Times With over 200 images and extensive how-to and how-not-to examples, this new edition has everything students and scholars need to understand and create effective data visualisations. Combining ‘how to think’ instruction with a ‘how to produce’ mentality, this book takes readers step-by-step through analysing, designing, and curating information into useful, impactful tools of communication. With this book and its extensive collection of online support, readers can: - Decide what visualisations work best for their data and their audience using the chart gallery - See data visualisation in action and learn the tools to try it themselves - Follow online checklists, tutorials, and exercises to build skills and confidence - Get advice from the UK’s leading data visualisation trainer on everything from getting started to honing the craft. Explore more resources about data visualisation and Andy Kirk.

Low Latency Big Data Visualisation

Autore: Tan Jerome, Nicholas
Editore: KIT Scientific Publishing
ISBN: 3731509407
Grandezza: 35,56 MB
Formato: PDF, Mobi
Vista: 4271
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Data Visualisation With R

Autore: Thomas Rahlf
Editore: Springer
ISBN: 3319497510
Grandezza: 26,91 MB
Formato: PDF, ePub, Mobi
Vista: 9941
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This book introduces readers to the fundamentals of creating presentation graphics using R, based on 100 detailed and complete scripts. It shows how bar and column charts, population pyramids, Lorenz curves, box plots, scatter plots, time series, radial polygons, Gantt charts, heat maps, bump charts, mosaic and balloon charts, and a series of different thematic map types can be created using R’s Base Graphics System. Every example uses real data and includes step-by-step explanations of the figures and their programming. The open source software R is an established standard and a powerful tool for various visualizing applications, integrating nearly all technologies relevant for data visualization. The basic software, enhanced by more than 7000 extension packs currently freely available, is intensively used by organizations including Google, Facebook and the CIA. The book serves as a comprehensive reference guide to a broad variety of applications in various fields. This book is intended for all kinds of R users, ranging from experts, for whom especially the example codes are particularly useful, to beginners, who will find the finished graphics most helpful in learning what R can actually deliver.

Introductory R A Beginner S Guide To Data Visualisation Statistical Analysis And Programming In R

Autore: Robert Knell
Editore: Robert Knell
ISBN: 0957597118
Grandezza: 61,59 MB
Formato: PDF, ePub, Mobi
Vista: 8204
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R is now the most widely used statistical software in academic science and it is rapidly expanding into other fields such as finance. R is almost limitlessly flexible and powerful, hence its appeal, but can be very difficult for the novice user. There are no easy pull-down menus, error messages are often cryptic and simple tasks like importing your data or exporting a graph can be difficult and frustrating. Introductory R is written for the novice user who knows a little about statistics but who hasn't yet got to grips with the ways of R. This new edition is completely revised and greatly expanded with new chapters on the basics of descriptive statistics and statistical testing, considerably more information on statistics and six new chapters on programming in R. Topics covered include: A walkthrough of the basics of R's command line interface Data structures including vectors, matrices and data frames R functions and how to use them Expanding your analysis and plotting capacities with add-in R packages A set of simple rules to follow to make sure you import your data properly An introduction to the script editor and advice on workflow A detailed introduction to drawing publication-standard graphs in R How to understand the help files and how to deal with some of the most common errors that you might encounter. Basic descriptive statistics The theory behind statistical testing and how to interpret the output of statistical tests Thorough coverage of the basics of data analysis in R with chapters on using chi-squared tests, t-tests, correlation analysis, regression, ANOVA and general linear models What the assumptions behind the analyses mean and how to test them using diagnostic plots Explanations of the summary tables produced for statistical analyses such as regression and ANOVA Writing your own functions in R Using table operations to manipulate matrices and data frames Using conditional statements and loops in R programmes. Writing longer R programmes. The techniques of statistical analysis in R are illustrated by a series of chapters where experimental and survey data are analysed. There is a strong emphasis on using real data from real scientific research, with all the problems and uncertainty that implies, rather than well-behaved made-up data that give ideal and easy to analyse results.

Data Visualization

Autore: Robert Grant
Editore: Chapman & Hall/CRC
ISBN: 9781138707603
Grandezza: 72,86 MB
Formato: PDF, Kindle
Vista: 6143
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This 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.

Principal Manifolds For Data Visualization And Dimension Reduction

Autore: Alexander N. Gorban
Editore: Springer Science & Business Media
ISBN: 3540737502
Grandezza: 33,63 MB
Formato: PDF, ePub, Docs
Vista: 3181
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The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described. Presentation of algorithms is supplemented by case studies. The volume ends with a tutorial PCA deciphers genome.

Innovative Approaches Of Data Visualization And Visual Analytics

Autore: Huang, Mao Lin
Editore: IGI Global
ISBN: 1466643102
Grandezza: 60,28 MB
Formato: PDF, ePub, Mobi
Vista: 734
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Due to rapid advances in hardware and software technologies, network infrastructure and data have become increasingly complex, requiring efforts to more effectively comprehend and analyze network topologies and information systems. Innovative Approaches of Data Visualization and Visual Analytics evaluates the latest trends and developments in force-based data visualization techniques, addressing issues in the design, development, evaluation, and application of algorithms and network topologies. This book will assist professionals and researchers working in the fields of data analysis and information science, as well as students in computer science and computer engineering, in developing increasingly effective methods of knowledge creation, management, and preservation.

Data Visualization

Autore: Lauren Magnuson
Editore: Rowman & Littlefield Publishers
ISBN: 9781442271104
Grandezza: 25,72 MB
Formato: PDF, Kindle
Vista: 2737
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Data Visualization: A Guide to Visual Storytelling for Libraries is a practical guide to the skills and tools needed to create beautiful and meaningful visual stories through data visualization. Learn how to sift through complex datasets to better understand a variety of metrics, such as trends in user behavior and electronic resource usage, return on investment (ROI) and impact metrics, and data about library collections and repositories. A 20-page color insert showcases an extensiveselection of visualizations using a variety of technologies and programming languages that can serve as inspiration for creating your own visualizations. Understanding and communicating trends from your organization s data is essential. Whether you are looking to make more informed decisions by visualizing organizational data, or to tell the story of your library s impact on your community, this book will give you the tools to make it happen."

Data Mining And Data Visualization

Autore:
Editore: Elsevier
ISBN: 9780080459400
Grandezza: 46,52 MB
Formato: PDF, ePub, Mobi
Vista: 6390
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Data Mining and Data Visualization focuses on dealing with large-scale data, a field commonly referred to as data mining. The book is divided into three sections. The first deals with an introduction to statistical aspects of data mining and machine learning and includes applications to text analysis, computer intrusion detection, and hiding of information in digital files. The second section focuses on a variety of statistical methodologies that have proven to be effective in data mining applications. These include clustering, classification, multivariate density estimation, tree-based methods, pattern recognition, outlier detection, genetic algorithms, and dimensionality reduction. The third section focuses on data visualization and covers issues of visualization of high-dimensional data, novel graphical techniques with a focus on human factors, interactive graphics, and data visualization using virtual reality. This book represents a thorough cross section of internationally renowned thinkers who are inventing methods for dealing with a new data paradigm. Distinguished contributors who are international experts in aspects of data mining Includes data mining approaches to non-numerical data mining including text data, Internet traffic data, and geographic data Highly topical discussions reflecting current thinking on contemporary technical issues, e.g. streaming data Discusses taxonomy of dataset sizes, computational complexity, and scalability usually ignored in most discussions Thorough discussion of data visualization issues blending statistical, human factors, and computational insights

Handbook Of Data Visualization

Autore: Chun-houh Chen
Editore: Springer Science & Business Media
ISBN: 9783540330370
Grandezza: 21,32 MB
Formato: PDF, ePub, Mobi
Vista: 8892
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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.

Data Visualization Handbook

Autore: Juuso Koponen
Editore: Aalto korkeakoulusäätiö
ISBN: 9526074483
Grandezza: 39,74 MB
Formato: PDF, Docs
Vista: 1990
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The data visualization handbook is a practical guide to creating compelling graphics to explain or explore data. It is primarily aimed for designers, journalists, researchers, analysts, and other professionals who want to learn the basics of visualization, but also includes plenty of material for people with intermediate level visualization skills.

Graph Based Clustering And Data Visualization Algorithms

Autore: Ágnes Vathy-Fogarassy
Editore: Springer Science & Business Media
ISBN: 1447151585
Grandezza: 62,90 MB
Formato: PDF, ePub
Vista: 1487
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This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on the synergistic combination of clustering, graph-theory, neural networks, data visualization, dimensionality reduction, fuzzy methods, and topology learning. The work contains numerous examples to aid in the understanding and implementation of the proposed algorithms, supported by a MATLAB toolbox available at an associated website.

Data Visualization

Autore: Frits H. Post
Editore: Springer Science & Business Media
ISBN: 9781402072598
Grandezza: 70,47 MB
Formato: PDF
Vista: 792
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Data visualization is currently a very active and vital area of research, teaching and development. The term unites the established field of scientific visualization and the more recent field of information visualization. The success of data visualization is due to the soundness of the basic idea behind it: the use of computer-generated images to gain insight and knowledge from data and its inherent patterns and relationships. A second premise is the utilization of the broad bandwidth of the human sensory system in steering and interpreting complex processes, and simulations involving data sets from diverse scientific disciplines and large collections of abstract data from many sources. These concepts are extremely important and have a profound and widespread impact on the methodology of computational science and engineering, as well as on management and administration. The interplay between various application areas and their specific problem solving visualization techniques is emphasized in this book. Reflecting the heterogeneous structure of Data Visualization, emphasis was placed on these topics: -Visualization Algorithms and Techniques; -Volume Visualization; -Information Visualization; -Multiresolution Techniques; -Interactive Data Exploration. Data Visualization: The State of the Art presents the state of the art in scientific and information visualization techniques by experts in this field. It can serve as an overview for the inquiring scientist, and as a basic foundation for developers. This edited volume contains chapters dedicated to surveys of specific topics, and a great deal of original work not previously published illustrated by examples from a wealth of applications. The book will also provide basic material for teaching the state of the art techniques in data visualization. Data Visualization: The State of the Art is designed to meet the needs of practitioners and researchers in scientific and information visualization. This book is also suitable as a secondary text for graduate level students in computer science and engineering.

Data Visualization In R With Ggplot2

Autore: Kara Woo
Editore:
ISBN:
Grandezza: 44,92 MB
Formato: PDF, Docs
Vista: 584
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"In this course, Kara Woo teaches you how to create great looking, insightful data visualizations using the R package ggplot2. Designed for researchers, data journalists, and budding data scientists with basic R knowledge (i.e., how to install packages, read data, perform simple data manipulations), this video covers the principles of data visualization and the specifics of how to use ggplot2 to create and customize a variety of visualizations. After completing the course you'll have a thorough understanding of ggplot2 and a basic comprehension of visualization allowing you to work with the advanced tools required on complex projects and interactive visualizations."--Resource description page.

Data Visualization

Autore: Robert Collins
Editore: Createspace Independent Publishing Platform
ISBN: 9781723234750
Grandezza: 63,66 MB
Formato: PDF, ePub, Mobi
Vista: 345
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This book is a guide for you on how to present data using graphics. The various tools that can be used for presenting data visually have been discussed. The author guides you on how to create various graphics using data in R programming language. The author also guides you on how to present data graphically in Python using Matplotlib and Pandas libraries. Tableau is a graphical user interface tool good for business intelligence. The tool can help its users present their data visually. The author guides you on how to create various graphics to represent your data in Tableau. Microsoft Excel is also a good tool for data analysis and visualization. The author guides you on the various ways to present your data visually in Excel. What is Data Visualization? Data Visualization in R Data Visualization in Python Data Visualization with Tableau Data Visualization in Excel Keywords: data visualisation r, pandas programming, data visualisation python, tableau data, matplotlib python, pandas python, pandas, data visualisation books, data visualisation for dummies, data visualisation excel, data visualization tableau, data visualization a practical introduction, tableau data visualizations.

Interactive Data Visualization With Python

Autore: Abha Belorkar
Editore: Packt Publishing Ltd
ISBN: 1800201060
Grandezza: 55,83 MB
Formato: PDF
Vista: 8130
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Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python Key Features Study and use Python interactive libraries, such as Bokeh and Plotly Explore different visualization principles and understand when to use which one Create interactive data visualizations with real-world data Book Description With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories. What you will learn Explore and apply different interactive data visualization techniques Manipulate plotting parameters and styles to create appealing plots Customize data visualization for different audiences Design data visualizations using interactive libraries Use Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plots Customize data visualization for different scenarios Who this book is for This book intends to provide a solid training ground for Python developers, data analysts and data scientists to enable them to present critical data insights in a way that best captures the user's attention and imagination. It serves as a simple step-by-step guide that demonstrates the different types and components of visualization, the principles, and techniques of effective interactivity, as well as common pitfalls to avoid when creating interactive data visualizations. Students should have an intermediate level of competency in writing Python code, as well as some familiarity with using libraries such as pandas.

Security Data Visualization

Autore: Greg Conti
Editore: No Starch Press
ISBN: 1593271433
Grandezza: 15,61 MB
Formato: PDF, ePub, Docs
Vista: 5093
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An introduction to a range of cyber security issues explains how to utilize graphical approaches to displaying and understanding computer security data, such as network traffic, server logs, and executable files, offering guidelines for identifying a network attack, how to assess a system for vulnerabilities with Afterglow and RUMINT visualization software, and how to protect a system from additional attacks. Original. (Intermediate)

Data Visualization Representing Information On Modern Web

Autore: Andy Kirk
Editore: Packt Publishing Ltd
ISBN: 1787125076
Grandezza: 24,17 MB
Formato: PDF, Docs
Vista: 701
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Unleash 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. After getting familiar with key concepts of data visualization, it's time to incorporate it with various technologies. In the second module, Social Data Visualization with HTML5 and JavaScript, it teaches you how to leverage HTML5 techniques through JavaScript to build visualizations. It also clears up how the often complicated OAuth protocol works to help you unlock a universe of social media data from sites such as Twitter, Facebook, and Google+. Once you are familiar with the concepts of incorporating data visualization with HTML5 and JavaScript, 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. This module provides a strong foundation in designing compelling web visualizations with D3.js. 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 Ǯdrew 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 For Success

Autore: Steven Braun
Editore:
ISBN: 9781864707205
Grandezza: 46,33 MB
Formato: PDF
Vista: 7638
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-Top designers discuss their approaches to data visualisation, offering insight into their commonly used design methods and tips for beginning practitioners in the field -The 40 featured designers are all very experienced, and are renowned for creating reputable works of high level and value What is data visualization? In Data Visualization for Success, 40 designers and their works demonstrate that data visualization is a vibrant and constantly evolving field that is as multimodal as it is interdisciplinary. Through the works showcased here, these designers discuss some of their approaches to working with data visualisation, offering insight into the design methods they commonly use and providing tips that will help beginning practitioners in the field. This book shows that data visualization is a practice and discipline whose fluid boundaries continue to be moved in new, exciting, and unprecedented directions by emerging and seasoned designers alike.

Data Visualization

Autore: Alexandru C. Telea
Editore: CRC Press
ISBN: 1466585269
Grandezza: 41,66 MB
Formato: PDF, ePub
Vista: 4207
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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.