Advertisement
| data science visualization examples: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data science visualization examples: Data Visualization Kieran Healy, 2018-12-18 An accessible primer on how to create effective graphics from data This 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 science visualization examples: Storytelling with Data Cole Nussbaumer Knaflic, 2015-10-09 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! |
| data science visualization examples: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| data science visualization examples: Visualization Analysis and Design Tamara Munzner, 2014-12-01 Learn How to Design Effective Visualization SystemsVisualization Analysis and Design provides a systematic, comprehensive framework for thinking about visualization in terms of principles and design choices. The book features a unified approach encompassing information visualization techniques for abstract data, scientific visualization techniques |
| data science visualization examples: Fundamentals of Data Visualization Claus O. Wilke, 2019-03-18 Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the 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 it provides 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 as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story |
| data science visualization examples: Python Data Visualization Essentials Guide Kallur Rahman, 2021-07-30 Build your data science skills. Start data visualization Using Python. Right away. Become a good data analyst by creating quality data visualizations using Python. KEY FEATURES ● Exciting coverage on loads of Python libraries, including Matplotlib, Seaborn, Pandas, and Plotly. ● Tons of examples, illustrations, and use-cases to demonstrate visual storytelling of varied datasets. ● Covers a strong fundamental understanding of exploratory data analysis (EDA), statistical modeling, and data mining. DESCRIPTION Data visualization plays a major role in solving data science challenges with various capabilities it offers. This book aims to equip you with a sound knowledge of Python in conjunction with the concepts you need to master to succeed as a data visualization expert. The book starts with a brief introduction to the world of data visualization and talks about why it is important, the history of visualization, and the capabilities it offers. You will learn how to do simple Python-based visualization with examples with progressive complexity of key features. The book starts with Matplotlib and explores the power of data visualization with over 50 examples. It then explores the power of data visualization using one of the popular exploratory data analysis-oriented libraries, Pandas. The book talks about statistically inclined data visualization libraries such as Seaborn. The book also teaches how we can leverage bokeh and Plotly for interactive data visualization. Each chapter is enriched and loaded with 30+ examples that will guide you in learning everything about data visualization and storytelling of mixed datasets. WHAT YOU WILL LEARN ● Learn to work with popular Python libraries and frameworks, including Seaborn, Bokeh, and Plotly. ● Practice your data visualization understanding across numerous datasets and real examples. ● Learn to visualize geospatial and time-series datasets. ● Perform correlation and EDA analysis using Pandas and Matplotlib. ● Get to know storytelling of complex and unstructured data using Bokeh and Pandas. ● Learn best practices in writing clean and short python scripts for a quicker visual summary of datasets. WHO THIS BOOK IS FOR This book is for all data analytics professionals, data scientists, and data mining hobbyists who want to be strong data visualizers by learning all the popular Python data visualization libraries. Prior working knowledge of Python is assumed. TABLE OF CONTENTS 1. Introduction to Data Visualization 2. Why Data Visualization 3. Various Data Visualization Elements and Tools 4. Using Matplotlib with Python 5. Using NumPy and Pandas for Plotting 6. Using Seaborn for Visualization 7. Using Bokeh with Python 8. Using Plotly, Folium, and Other Tools for Data Visualization 9. Hands-on Examples and Exercises, Case Studies, and Further Resources |
| data science visualization examples: The Grammar of Graphics Leland Wilkinson, 2013-03-09 Written for statisticians, computer scientists, geographers, research and applied scientists, and others interested in visualizing data, this book presents a unique foundation for producing almost every quantitative graphic found in scientific journals, newspapers, statistical packages, and data visualization systems. It was designed for a distributed computing environment, with special attention given to conserving computer code and system resources. While the tangible result of this work is a Java production graphics library, the text focuses on the deep structures involved in producing quantitative graphics from data. It investigates the rules that underlie pie charts, bar charts, scatterplots, function plots, maps, mosaics, and radar charts. These rules are abstracted from the work of Bertin, Cleveland, Kosslyn, MacEachren, Pinker, Tufte, Tukey, Tobler, and other theorists of quantitative graphics. |
| data science visualization examples: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| data science visualization examples: Information is Beautiful David McCandless, 2009 Miscellaneous facts and ideas are interconnected and represented in a visual format, a visual miscellaneum, which represents a series of experiments in making information approachable and beautiful -- from p.007 |
| data science visualization examples: Hands-On Data Visualization Jack Dougherty, Ilya Ilyankou, 2021-04-30 Tell your story and show it with data, using free and easy-to-learn tools on the web. This introductory book teaches you how to design interactive charts and customized maps for your website, beginning with simple drag-and-drop tools such as Google Sheets, Datawrapper, and Tableau Public. You'll also gradually learn how to edit open source code templates like Chart.js, Highcharts, and Leaflet on GitHub. Hands-On Data Visualization for All takes you step-by-step through tutorials, real-world examples, and online resources. This hands-on resource is ideal for students, nonprofit organizations, small business owners, local governments, journalists, academics, and anyone who wants to take data out of spreadsheets and turn it into lively interactive stories. No coding experience is required. Build interactive charts and maps and embed them in your website Understand the principles for designing effective charts and maps Learn key data visualization concepts to help you choose the right tools Convert and transform tabular and spatial data to tell your data story Edit and host Chart.js, Highcharts, and Leaflet map code templates on GitHub Learn how to detect bias in charts and maps produced by others |
| data science visualization examples: Blindsight Peter Watts, 2006-10-03 Hugo and Shirley Jackson award-winning Peter Watts stands on the cutting edge of hard SF with his acclaimed novel, Blindsight Two months since the stars fell... Two months of silence, while a world held its breath. Now some half-derelict space probe, sparking fitfully past Neptune's orbit, hears a whisper from the edge of the solar system: a faint signal sweeping the cosmos like a lighthouse beam. Whatever's out there isn't talking to us. It's talking to some distant star, perhaps. Or perhaps to something closer, something en route. So who do you send to force introductions with unknown and unknowable alien intellect that doesn't wish to be met? You send a linguist with multiple personalities, her brain surgically partitioned into separate, sentient processing cores. You send a biologist so radically interfaced with machinery that he sees x-rays and tastes ultrasound. You send a pacifist warrior in the faint hope she won't be needed. You send a monster to command them all, an extinct hominid predator once called vampire, recalled from the grave with the voodoo of recombinant genetics and the blood of sociopaths. And you send a synthesist—an informational topologist with half his mind gone—as an interface between here and there. Pray they can be trusted with the fate of a world. They may be more alien than the thing they've been sent to find. At the Publisher's request, this title is being sold without Digital Rights Management Software (DRM) applied. |
| data science visualization examples: Designing Data Visualizations Noah Iliinsky, Julie Steele, 2011-09-16 Data visualization is an efficient and effective medium for communicating large amounts of information, but the design process can often seem like an unexplainable creative endeavor. This concise book aims to demystify the design process by showing you how to use a linear decision-making process to encode your information visually. Delve into different kinds of visualization, including infographics and visual art, and explore the influences at work in each one. Then learn how to apply these concepts to your design process. Learn data visualization classifications, including explanatory, exploratory, and hybrid Discover how three fundamental influences—the designer, the reader, and the data—shape what you create Learn how to describe the specific goal of your visualization and identify the supporting data Decide the spatial position of your visual entities with axes Encode the various dimensions of your data with appropriate visual properties, such as shape and color See visualization best practices and suggestions for encoding various specific data types |
| data science visualization examples: Data Visualization Frits H. Post, Gregory M. Nielson, Georges-Pierre Bonneau, 2012-12-06 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 science visualization examples: Dear Data Giorgia Lupi, Stefanie Posavec, 2016-09-13 Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates the infinitesimal, incomplete, imperfect, yet exquisitely human details of life, in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear Data reproduces in pinpoint detail the full year's set of cards, front and back, providing a remarkable portrait of two artists connected by their attention to the details of their lives—including complaints, distractions, phone addictions, physical contact, and desires. These details illuminate the lives of two remarkable young women and also inspire us to map our own lives, including specific suggestions on what data to draw and how. A captivating and unique book for designers, artists, correspondents, friends, and lovers everywhere. |
| data science visualization examples: R Graphics Essentials for Great Data Visualization Alboukadel Kassambara, 2017-11-14 Data visualization is one of the most important part of data science. Many books and courses present a catalogue of graphics but they don't teach you which charts to use according to the type of the data. In this book, we start by presenting the key graphic systems and packages available in R, including R base graphs, lattice and ggplot2 plotting systems. Next, we provide more than 200 practical examples to create great graphics for the right data using either the ggplot2 package and extensions or the traditional R graphics. With this book, you 'll learn: - How to quickly create beautiful graphics using ggplot2 packages - How to properly customize and annotate the plots - Type of graphics for visualizing categorical and continuous variables - How to add automatically p-values to box plots, bar plots and alternatives - How to add marginal density plots and correlation coefficients to scatter plots - Key methods for analyzing and visualizing multivariate data - R functions and packages for plotting time series data - How to combine multiple plots on one page to create production-quality figures. |
| data science visualization examples: Interactive Visual Data Analysis Christian Tominski, Heidrun Schumann, 2020-04-01 In the age of big data, being able to make sense of data is an important key to success. Interactive Visual Data Analysis advocates the synthesis of visualization, interaction, and automatic computation to facilitate insight generation and knowledge crystallization from large and complex data. The book provides a systematic and comprehensive overview of visual, interactive, and analytical methods. It introduces criteria for designing interactive visual data analysis solutions, discusses factors influencing the design, and examines the involved processes. The reader is made familiar with the basics of visual encoding and gets to know numerous visualization techniques for multivariate data, temporal data, geo-spatial data, and graph data. A dedicated chapter introduces general concepts for interacting with visualizations and illustrates how modern interaction technology can facilitate the visual data analysis in many ways. Addressing today’s large and complex data, the book covers relevant automatic analytical computations to support the visual data analysis. The book also sheds light on advanced concepts for visualization in multi-display environments, user guidance during the data analysis, and progressive visual data analysis. The authors present a top-down perspective on interactive visual data analysis with a focus on concise and clean terminology. Many real-world examples and rich illustrations make the book accessible to a broad interdisciplinary audience from students, to experts in the field, to practitioners in data-intensive application domains. Features: Dedicated to the synthesis of visual, interactive, and analysis methods Systematic top-down view on visualization, interaction, and automatic analysis Broad coverage of fundamental and advanced visualization techniques Comprehensive chapter on interacting with visual representations Extensive integration of automatic computational methods Accessible portrayal of cutting-edge visual analytics technology Foreword by Jack van Wijk For more information, you can also visit the author website, where the book's figures are made available under the CC BY Open Access license. |
| data science visualization examples: Better Data Visualizations Jonathan Schwabish, 2021-02-09 Now more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present their work visually. This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do’s and don’ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart’s design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits for your data, and clearly communicate your message. |
| data science visualization examples: Visualize This Nathan Yau, 2011-06-13 Practical data design tips from a data visualization expert of the modern age Data doesn't decrease; it is ever-increasing and can be overwhelming to organize in a way that makes sense to its intended audience. Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships. Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data-native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing. |
| data science visualization examples: Visualizing Data Ben Fry, 2008 Provides information on the methods of visualizing data on the Web, along with example projects and code. |
| data science visualization examples: Good Charts Scott Berinato, 2016-04-26 Dataviz—the new language of business A good visualization can communicate the nature and potential impact of information and ideas more powerfully than any other form of communication. For a long time “dataviz” was left to specialists—data scientists and professional designers. No longer. A new generation of tools and massive amounts of available data make it easy for anyone to create visualizations that communicate ideas far more effectively than generic spreadsheet charts ever could. What’s more, building good charts is quickly becoming a need-to-have skill for managers. If you’re not doing it, other managers are, and they’re getting noticed for it and getting credit for contributing to your company’s success. In Good Charts, dataviz maven Scott Berinato provides an essential guide to how visualization works and how to use this new language to impress and persuade. Dataviz today is where spreadsheets and word processors were in the early 1980s—on the cusp of changing how we work. Berinato lays out a system for thinking visually and building better charts through a process of talking, sketching, and prototyping. This book is much more than a set of static rules for making visualizations. It taps into both well-established and cutting-edge research in visual perception and neuroscience, as well as the emerging field of visualization science, to explore why good charts (and bad ones) create “feelings behind our eyes.” Along the way, Berinato also includes many engaging vignettes of dataviz pros, illustrating the ideas in practice. Good Charts will help you turn plain, uninspiring charts that merely present information into smart, effective visualizations that powerfully convey ideas. |
| data science visualization examples: Data Visualization in Society Martin Engebretsen, Helen Kennedy, 2020-03-21 Today we are witnessing an increased use of data visualization in society. Across domains such as work, education and the news, various forms of graphs, charts and maps are used to explain, convince and tell stories. In an era in which more and more data are produced and circulated digitally, and digital tools make visualization production increasingly accessible, it is important to study the conditions under which such visual texts are generated, disseminated and thought to be of societal benefit. This book is a contribution to the multi-disciplined and multi-faceted conversation concerning the forms, uses and roles of data visualization in society. Do data visualizations do 'good' or 'bad'? Do they promote understanding and engagement, or do they do ideological work, privileging certain views of the world over others? The contributions in the book engage with these core questions from a range of disciplinary perspectives. |
| data science visualization examples: Interactive Data Visualization for the Web Scott Murray, 2013-03-15 Create and publish your own interactive data visualization projects on the Web, even if you have no experience with either web development or data visualization. It’s easy with this hands-on guide. You’ll start with an overview of data visualization concepts and simple web technologies, and then learn how to use D3, a JavaScript library that lets you express data as visual elements in a web page. Interactive Data Visualization for the Web makes these skills available at an introductory level for designers and visual artists without programming experience, journalists interested in the emerging data journalism processes, and others keenly interested in visualization and publicly available data sources. Get a practical introduction to data visualization, accessible for beginners Focus on web-based tools that help you publish your creations quickly to a wide audience Learn about interactivity so you can engage users in exploring your data |
| data science visualization examples: Handbook of Data Visualization Chun-houh Chen, Wolfgang Karl Härdle, Antony Unwin, 2007-12-18 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 science visualization examples: Visualizing with Text Richard Brath, 2020-11-01 Visualizing with Text uncovers the rich palette of text elements usable in visualizations from simple labels through to documents. Using a multidisciplinary research effort spanning across fields including visualization, typography, and cartography, it builds a solid foundation for the design space of text in visualization. The book illustrates many new kinds of visualizations, including microtext lines, skim formatting, and typographic sets that solve some of the shortcomings of well-known visualization techniques. Key features: More than 240 illustrations to aid inspiration of new visualizations Eight new approaches to data visualization leveraging text Quick reference guide for visualization with text Builds a solid foundation extending current visualization theory Bridges between visualization, typography, text analytics, and natural language processing The author website, including teaching exercises and interactive demos and code, can be found here. Designers, developers, and academics can use this book as a reference and inspiration for new approaches to visualization in any application that uses text. |
| data science visualization examples: Data Visualisation with R Thomas Rahlf, 2019-11-23 This book introduces readers to the fundamentals of creating presentation graphics using R, based on 111 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. This second edition contains additional examples for cartograms, chord-diagrams and networks, and interactive visualizations with Javascript. 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 14000 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. |
| data science visualization examples: The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting Data, Facts, and Figures Dona M. Wong, 2013-12-16 The definitive guide to the graphic presentation of information. In today’s data-driven world, professionals need to know how to express themselves in the language of graphics effectively and eloquently. Yet information graphics is rarely taught in schools or is the focus of on-the-job training. Now, for the first time, Dona M. Wong, a student of the information graphics pioneer Edward Tufte, makes this material available for all of us. In this book, you will learn: to choose the best chart that fits your data; the most effective way to communicate with decision makers when you have five minutes of their time; how to chart currency fluctuations that affect global business; how to use color effectively; how to make a graphic “colorful” even if only black and white are available. The book is organized in a series of mini-workshops backed up with illustrated examples, so not only will you learn what works and what doesn’t but also you can see the dos and don’ts for yourself. This is an invaluable reference work for students and professional in all fields. |
| data science visualization examples: Data Visualization Robert Grant, 2018-12-07 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. 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 science visualization examples: A History of Data Visualization and Graphic Communication Michael Friendly, Howard Wainer, 2021-06-08 A comprehensive history of data visualization—its origins, rise, and effects on the ways we think about and solve problems. With complex information everywhere, graphics have become indispensable to our daily lives. Navigation apps show real-time, interactive traffic data. A color-coded map of exit polls details election balloting down to the county level. Charts communicate stock market trends, government spending, and the dangers of epidemics. A History of Data Visualization and Graphic Communication tells the story of how graphics left the exclusive confines of scientific research and became ubiquitous. As data visualization spread, it changed the way we think. Michael Friendly and Howard Wainer take us back to the beginnings of graphic communication in the mid-seventeenth century, when the Dutch cartographer Michael Florent van Langren created the first chart of statistical data, which showed estimates of the distance from Rome to Toledo. By 1786 William Playfair had invented the line graph and bar chart to explain trade imports and exports. In the nineteenth century, the “golden age” of data display, graphics found new uses in tracking disease outbreaks and understanding social issues. Friendly and Wainer make the case that the explosion in graphical communication both reinforced and was advanced by a cognitive revolution: visual thinking. Across disciplines, people realized that information could be conveyed more effectively by visual displays than by words or tables of numbers. Through stories and illustrations, A History of Data Visualization and Graphic Communication details the 400-year evolution of an intellectual framework that has become essential to both science and society at large. |
| data science visualization examples: Data Visualization Made Simple Kristen Sosulski, 2018-09-27 Data 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 science visualization examples: Interactive Web-Based Data Visualization with R, plotly, and shiny Carson Sievert, 2020-01-30 The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more. Key Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous, discrete, and multivariate data Learn numerous ways to visualize geo-spatial data This book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics. |
| data science visualization examples: Beginning Data Science in R Thomas Mailund, 2017-03-09 Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language. |
| data science visualization examples: Visualizing Data William S. Cleveland, 1993 |
| data science visualization examples: Effective Data Visualization Stephanie D. H. Evergreen, 2019-04-03 NOW IN FULL COLOR! 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 their data findings. This comprehensive how-to guide functions as a set of blueprints—supported by both 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 building the chosen graph in Excel. Now in full color with new examples throughout, the Second Edition includes a revamped chapter on qualitative data, nine new quantitative graph types, new shortcuts in Excel, and an entirely new chapter on Sharing Your Data With the World, which provides advice on using dashboards. New from Stephanie Evergreen! The Data Visualization Sketchbook provides advice on getting started with sketching and offers tips, guidance, and completed sample sketches for a number of reporting formats. Bundle Effective Data Visualization, 2e, and The Data Visualization Sketchbook, using ISBN 978-1-5443-7178-8! |
| data science visualization examples: Research Data Visualization and Scientific Graphics MARTINS. ZAUMANIS, 2021-07-22 Poor data charts and graphics are hindering the effective transfer of knowledge in academia. To get your research noticed and respected, this step-by-step guide will demonstrate essential techniques for creating informative scientific visualizations. By reading this book, you will learn: ✓ Eight bulletproof progressions for turning research data into convincing charts ✓ Eleven graphical features for converting scientific concepts into self-explanatory diagrams and scientific illustrations ✓ Straightforward visualization principles that help to interpret research results The straightforward approaches presented in this book are designed with efficiency in mind. By reviewing a variety of good and bad examples, you will learn how to include scientific visualization in your research communication routine. No artistic talent required. Clear data charts and informative graphics draw citations to papers, make presentations memorable, and encourage reviewers to approve research proposals. This book will show you how to make it happen. What's included: 1) Web resources, including a comparison of different software and online tools for data visualization and scientific illustrations 2) Two printable cheat sheets that summarize the advice from the book 3) A book full of actionable advice for efficiently creating convincing data charts and illuminating scientific graphics About the author My name is Martins Zaumanis and I am obsessed with finding ways to communicate science visually. For example, during the last half a year of my Ph.D., I spent almost every evening hand-drawing my research just so that I could present a memorable TEDx talk. But I don't expect you to become obsessed. Quite the opposite: I developed an approach that will allow you to create great data charts and scientific illustrations without taking away time from what you love most - research |
| data science visualization examples: Making Data Visual Danyel Fisher, Miriah Meyer, 2017-12-20 You have a mound of data sitting in front of you and a suite of computation tools at your disposal. And yet, you're stumped as to how to turn that data into insight. Which part of that data actually matters, and where is this insight hidden? If you're a data scientist who struggles to navigate the murky space between data and insight, this book will help you think about and reshape data for visual data exploration. It's ideal for relatively new data scientists, who may be computer-knowledgeable and data-knowledgeable, but do not yet know how to create effective, explorable representations of data. With this book, you'll learn: Task analysis, driven by a series of leading questions that draw out the important aspects of the data to be explored; Visualization patterns, each of which take a different perspective on data and answer different questions; A taxonomy of visualizations for common data types; Techniques for gathering design requirements; When and where to make use of statistical methods.-- |
| data science visualization examples: Data Science Revealed Tshepo Chris Nokeri, 2021-03-21 Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model. The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification problems using artificial neural networks such as restricted Boltzmann machines, multi-layer perceptrons, and deep belief networks. The book discusses unsupervised learning clustering techniques such as the K-means method, agglomerative and Dbscan approaches, and dimension reduction techniques such as Feature Importance, Principal Component Analysis, and Linear Discriminant Analysis. And it introduces driverless artificial intelligence using H2O. After reading this book, you will be able to develop, test, validate, and optimize statistical machine learning and deep learning models, and engineer, visualize, and interpret sets of data. What You Will Learn Design, develop, train, and validate machine learning and deep learning models Find optimal hyper parameters for superior model performance Improve model performance using techniques such as dimension reduction and regularization Extract meaningful insights for decision making using data visualization Who This Book Is For Beginning and intermediate level data scientists and machine learning engineers |
| data science visualization examples: Graphic Presentation Willard Cope Brinton, 2022-10-26 This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. |
| data science visualization examples: Visualizations and Dashboards for Learning Analytics Muhittin Sahin, Dirk Ifenthaler, 2021-12-16 This edited volume fills the gaps in existing literature on visualization and dashboard design for learning analytics. To do so, it presents critical tips to stakeholders and acts as guide to efficient implementation. The book covers the following topics: visualization and dashboard design for learning analytics, visualization and dashboard preferences of stakeholders, learners’ patterns on the dashboard, usability of visualization techniques and the dashboard, dashboard and intervention design, learning and instructional design for learning analytics, privacy and security issues about the dashboard, and future directions of visualization and dashboard design. This book will be of interest to researchers with interest in learning analytics and data analytics, teachers and students in higher education institutions and instructional designers, as it includes contributions from a wide variety of educational and psychological researchers, engineers, instructional designers, learning scientists, and computer scientists interested in learning analytics. |
| data science visualization examples: Data Visualization with Python and JavaScript Kyran Dale, 2016-06-30 Learn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript libraries—including Scrapy, Matplotlib, Pandas, Flask, and D3—for crafting engaging, browser-based visualizations. As a working example, throughout the book Dale walks you through transforming Wikipedia’s table-based list of Nobel Prize winners into an interactive visualization. You’ll examine steps along the entire toolchain, from scraping, cleaning, exploring, and delivering data to building the visualization with JavaScript’s D3 library. If you’re ready to create your own web-based data visualizations—and know either Python or JavaScript— this is the book for you. Learn how to manipulate data with Python Understand the commonalities between Python and JavaScript Extract information from websites by using Python’s web-scraping tools, BeautifulSoup and Scrapy Clean and explore data with Python’s Pandas, Matplotlib, and Numpy libraries Serve data and create RESTful web APIs with Python’s Flask framework Create engaging, interactive web visualizations with JavaScript’s D3 library |
Dashboards and Data Visualization, with Examples - Data …
DATA SCIENCE REPORT SERIES Dashboards and Data Visualization, with Examples Maia Pelletier1, Patrick Boily1,2,3 Abstract A data dashboard is any visual display of data used to …
Data Visualization
Dashboards act as a data visualization tool where users can easily analyze trends and statistics. It can be a powerful way of communicating results of a Data Science project. Examples: Dash by …
How to Make Effective (and Beautiful) Plots - Princeton …
Principles for better visualizations based in visual science and visual processing theory. What is the main point of the following chart? Rough preview of the coding difference... Why are …
An Introduction to Data Visualization - NASA Technical …
•Find some data, a data dictionary, and any info you can. • Make some specific questions/do Exploratory Data Analysis (EDA) to further refine. • Sketch out some charts that could answer …
Data Visualization Examples - grginc.com
Below are several examples of data visualization methods we have used in recent reports, along with a brief description of what is being illustrated. Example 1
Introduction to Data Visualization
Visualization in Data Science can be used to: - Explore data - Analyze data - Communicate findings - Quickly draw attention to key messages
Data Visualization - Guide to Intelligent Data Science
What is the best way of plotting a dataset? − Read from CSV file, Excel file and SQLite. − bar chart and histogram − parallel coordinates − box plot − scatter plot − table view. − Percentiles …
Data Visualization in Data Science - Cambridge University …
evaluated a variety of data visualization examples, along with common mistakes and helpful approaches. Based on what we've learned, developing an efficient and ethical way to visualize
DaVE - A Curated Database of Visualization Examples
In this work, we present DaVE1 - a curated database of visualiza-tion examples targeted at novice and experienced HPC users alike, that allows for easy access to state-of-the-art and advanced …
Chapter 10 Data visualization principles - stat.ucla.edu
guide for effective data visualization. Much of this section is based on a talk by Karl Broman 33 titled “Creating Effective Figures and Tables” 34 and includes some of the figures which were …
Chapter 8 Visualizing data distributions - University of …
Our first data visualization building block is learning to summarize lists of factors or numeric vectors. More often than not, the best way to share or explore this summary is through data …
CST 3602 Data Visualization - City Tech
Analyze data visualization examples and reason about the quality of these visualization examples. 4. Create data visualizations following the principles of good visualization. Demonstrate the …
Introduction to Data Visualization - inria.hal.science
In this course, students will learn how to produce data-driven visualizations for both exploration and communication purposes, given the types of data and visualization goals and tasks. By …
Data Visualization: Tableau
Dashboards act as a data visualization tool where users can easily analyze trends and statistics. It can be a powerful way of communicating results of a Data Science project. Examples: Dash by …
The Shaffer 4 C's of Data Visualization Clean Examples Default …
Feb 17, 2011 · The Shaffer 4 C's of Data Visualization "Clean" Examples Here is an excellent example of these practices in use taken from: TDWI Best Practices Report entitled …
Visualization of Multivariate Data - University of South Carolina
occurs when many data records have the same or similar values or the number of data records is large relative to the display • Interaction techniques are often applied to address the problems • …
Student Worksheet: Grappling With Graphs and Other Data
1. How would you define a data visualization? Give examples of different types of data visualizations. 2. What is the purpose of a data visualization? Why are they helpful? 3.
Introduction to Data Visualization - University of San Francisco
• Understand what makes a visualization effective • Critically evaluate a visual representation of data by looking at various examples in media (newspapers, television and so on) • Gain hands …
Visual Data Analytics - LJLL
Scienti˝c Visualization is concerned with exploring data and in-formation in such a way as to gain understanding and insight into the data. The goal of scienti˝c visualization is to promote a …
Visualization Examples for Cyber Security - Wright State …
Loading the heterogeneous data source (netflow, IPS log and host status log), OCEANS provides a multi-level visualization showing temporal overview, IP connections and detailed …
Dashboards and Data Visualization, with Examples - Data …
DATA SCIENCE REPORT SERIES Dashboards and Data Visualization, with Examples Maia Pelletier1, Patrick Boily1,2,3 Abstract A data dashboard is any visual display of data used to …
Data Visualization
Dashboards act as a data visualization tool where users can easily analyze trends and statistics. It can be a powerful way of communicating results of a Data Science project. Examples: Dash by …
How to Make Effective (and Beautiful) Plots - Princeton …
Principles for better visualizations based in visual science and visual processing theory. What is the main point of the following chart? Rough preview of the coding difference... Why are …
An Introduction to Data Visualization - NASA Technical …
•Find some data, a data dictionary, and any info you can. • Make some specific questions/do Exploratory Data Analysis (EDA) to further refine. • Sketch out some charts that could answer …
Data Visualization Examples - grginc.com
Below are several examples of data visualization methods we have used in recent reports, along with a brief description of what is being illustrated. Example 1
Introduction to Data Visualization
Visualization in Data Science can be used to: - Explore data - Analyze data - Communicate findings - Quickly draw attention to key messages
Data Visualization - Guide to Intelligent Data Science
What is the best way of plotting a dataset? − Read from CSV file, Excel file and SQLite. − bar chart and histogram − parallel coordinates − box plot − scatter plot − table view. − Percentiles …
Data Visualization in Data Science - Cambridge University …
evaluated a variety of data visualization examples, along with common mistakes and helpful approaches. Based on what we've learned, developing an efficient and ethical way to visualize
DaVE - A Curated Database of Visualization Examples
In this work, we present DaVE1 - a curated database of visualiza-tion examples targeted at novice and experienced HPC users alike, that allows for easy access to state-of-the-art and advanced …
Chapter 10 Data visualization principles - stat.ucla.edu
guide for effective data visualization. Much of this section is based on a talk by Karl Broman 33 titled “Creating Effective Figures and Tables” 34 and includes some of the figures which were …
Chapter 8 Visualizing data distributions - University of …
Our first data visualization building block is learning to summarize lists of factors or numeric vectors. More often than not, the best way to share or explore this summary is through data …
CST 3602 Data Visualization - City Tech
Analyze data visualization examples and reason about the quality of these visualization examples. 4. Create data visualizations following the principles of good visualization. Demonstrate the …
Introduction to Data Visualization - inria.hal.science
In this course, students will learn how to produce data-driven visualizations for both exploration and communication purposes, given the types of data and visualization goals and tasks. By …
Data Visualization: Tableau
Dashboards act as a data visualization tool where users can easily analyze trends and statistics. It can be a powerful way of communicating results of a Data Science project. Examples: Dash by …
The Shaffer 4 C's of Data Visualization Clean Examples …
Feb 17, 2011 · The Shaffer 4 C's of Data Visualization "Clean" Examples Here is an excellent example of these practices in use taken from: TDWI Best Practices Report entitled …
Visualization of Multivariate Data - University of South Carolina
occurs when many data records have the same or similar values or the number of data records is large relative to the display • Interaction techniques are often applied to address the problems …
Student Worksheet: Grappling With Graphs and Other Data …
1. How would you define a data visualization? Give examples of different types of data visualizations. 2. What is the purpose of a data visualization? Why are they helpful? 3.
Introduction to Data Visualization - University of San Francisco
• Understand what makes a visualization effective • Critically evaluate a visual representation of data by looking at various examples in media (newspapers, television and so on) • Gain hands …
Visual Data Analytics - LJLL
Scienti˝c Visualization is concerned with exploring data and in-formation in such a way as to gain understanding and insight into the data. The goal of scienti˝c visualization is to promote a …
Visualization Examples for Cyber Security - Wright State …
Loading the heterogeneous data source (netflow, IPS log and host status log), OCEANS provides a multi-level visualization showing temporal overview, IP connections and detailed …
