Information visualization Books

Book SynopsisAncient scriptures from the most popular book of all time, revolutionised to reveal its themes and narratives for a modern generation.Billions of people across the world find solace, meaning and wisdom from reading the Bible. But for some, it can be a difficult book to engage with and understand. In this this pioneering book Karen Sawrey shows you the bible as you never seen it before, using powerful infographics, with data sourced by experts in their fields, to communicate the key biblical themes and narratives. The Info Graphic Bible focuses on the meta narrative, the key points of the major stories and themes, to give the reader insight into the bigger story and help you understand the complex or abstract themes in a relevant way.Through revealing the ancient traditions, showing connections between Old and New Testament themes, and profiling important characters, you are left with a deeper and more memorable interpretation of the scriptures. Covering themes such as the First Family Trade ReviewSHORTLISTED FOR THE CHRISTIAN RETAILERS TOGETHER AWARD FOR BEST BIBLE AND BEST BOOK OF 2019 ‘Awesome and ambitious … an elegant revisioning of the most read book in the world’ David McCandless [Author of Information is Beautiful] ‘This is an exciting and innovative project which will open many people’s eyes to hitherto unsuspected dimensions of the biblical story’ NT Wright [Professor of New Testament and Early Christianity, University of St Andrews]

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Book SynopsisIn this mind-blowing follow-up to the bestselling Information is Beautiful, the king of infographics David McCandless uses spectacular visuals to reveal unexpected insights into how the world really works. We are living in the Information Age, in which we are constantly bombarded with data - on television, in print and online. How can we relate to this mind-numbing overload? Enter David McCandless and his amazing infographics: simple, elegant ways to understand information too complex or abstract to grasp any way but visually. In his unique signature style, he creates dazzling displays that blend facts with their connections, contexts and relationships, making information meaningful, entertaining - and beautiful. Taking infographics to the next level, his new book Knowledge is Beautiful is an endlessly fascinating spin through the world of visualized data, which offers a deeper, more wide-ranging look at the world and its history. Covering everything from dog breeds anTrade ReviewPRAISE FOR INFORMATION IS BEAUTIFUL: ‘Unbelievably brilliant’ Vogue ‘(A) terrific compendium of visual information’ Shortlist ‘In this intriguing book, David McCandless presents a cavalcade of compelling and colourful graphics, each one innovative in its attempt to offer a new perspective on some of our most pervasive twenty-first century obsessions’ Time Out ‘Stunning’ The Sunday Times ‘Thought-provoking, lovingly-crafted and informative; a handsome book that anyone would be grateful to receive’ Picked as one of the best science books of the year in Arts & Books, Independent on Sunday ‘What David McCandless has done is genius… dry data is transformed into small pieces of pop art that engage so much you end up learning more, without realising it. The ideal encyclopaedia for the information age.' Red Handed

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Book SynopsisGet productive quickly with Pentaho Data IntegrationKey Features Take away the pain of starting with a complex and powerful system Simplify your data transformation and integration work Explore, transform, and validate your data with Pentaho Data Integration Book DescriptionPentaho Data Integration(PDI) is an intuitive and graphical environment packed with drag and drop design and powerful Extract-Transform-Load (ETL) capabilities. Given its power and flexibility, initial attempts to use the Pentaho Data Integration tool can be difficult or confusing. This book is the ideal solution.This book reduces your learning curve with PDI. It provides the guidance needed to make you productive, covering the main features of Pentaho Data Integration. It demonstrates the interactive features of the graphical designer, and takes you through the main ETL capabilities that the tool offers.By the end of the book, you will be able to use PDI for extracting, transforming, and loading the types of data you encounter on a daily basis.What you will learn Design, preview and run transformations in Spoon Run transformations using the Pan utility Understand how to obtain data from different types of files Connect to a database and explore it using the database explorer Understand how to transform data in a variety of ways Understand how to insert data into database tables Design and run jobs for sequencing tasks and sending emails Combine the execution of jobs and transformations Who this book is forThis book is for software developers, business intelligence analysts, and others involved or interested in developing ETL solutions, or more generally, doing any kind of data manipulation.Table of ContentsTable of Contents Getting Started with PDI Getting Familiar with Spoon Extracting Data Tranforming Data Loading Data Orchestrating your Work

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Book SynopsisUse these strategies to warm your heart and open your mind to the power of happiness. Happiness is like the answer to a riddle. The more we want it and the more we seek it, the more elusive it becomes. And yet, when we are least conscious of looking for it, it can envelop us in a warm sense of contentment and belonging, making a single moment precious and valuable beyond measure. Scientists will tell us that happiness has the power to heal and to extend life. It is the ingredient we all seek to make our lives complete. Happiness makes us feel glad to be alive and this book offers you a more joyous approach to living and thinking; a shift in approach that may reframe your view of the world. Simple things you can do to reconsider your life – consciously – so that you can decide whether now is the time when you can be happier. It shares the experiences of others and provides the closest we might have to a happiness 'formula'. The good news is, happiness is within everyone’s grasp.

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Book SynopsisTop 20 Essential Skills for ArcGIS® Pro introduces the most important skills you need to get up and running with geographic information systems (GIS).With its location data advantage, geographic information system (GIS) software is a tool to help with key projects, decisions, and problems. But how do you start using GIS in your projects? If you need a fast track to using this valuable tool, learn and get experience with Top 20 Essential Skills for ArcGIS Pro.With this book, you can start using ArcGIS Pro quickly and successfully. Easy to understand, step-by-step exercises Conversational, upbeat language with lots of illustrations and tips Interesting user stories covering how GIS is applied in many situations Exercises for working with spatial data, creating maps, and doing basic analysis This handy resource with easy to follow, how-to steps will help you build your skill set to become adept at understanding and using ArcGIS Pro.Table of ContentsChapter 1: Exploring the ArcGIS Pro interfaceChapter 2: Creating reference maps and layoutsChapter 3: Preparing your dataChapter 4: Joining tables to GIS dataChapter 5: Creating thematic mapsChapter 6: GeocodingChapter 7: Creating categorical mapsChapter 8: Working with data tablesChapter 9: Enriching your dataChapter 10: Mapping x,y coordinate dataChapter 11: Editing feature dataChapter 12: Performing data queriesChapter 13: Performing location queriesChapter 14: Using geoprocessing toolsChapter 15: Creating geodatabasesChapter 16: Joining featuresChapter 17: Working with imageryChapter 18: Using 3D dataChapter 19: Adding a table and chart to a layoutChapter 20: Sharing your workChapter 21: Publishing your work (bonus skill)

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Book SynopsisHave you ever wondered? How it feels to be bitten by a bullet ant? Which literary masterpiece was eaten by its author''s dog? When town planning ceased to be an Olympic event? Where you should put a grockle box? What the fastest sporting projectile is? Whether it would be nice to live on Mars? Why football, beards and lip-synching at weddings have all been bannedThen you need Misc.Misc. is a playful modern take on the classic miscellany, a delightfully random collection of facts discovered by the creators of Delayed Gratification magazine. Inside you''ll find a joyful abundance of things you didn''t know you needed to know.In an era when it feels like you need to adopt the brace position before reading the morning''s headlines, this is a fun and frivolous book in which no one dies (except for Rasputin).Designed to be dipped into and revisited time and time again, M

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Book Synopsis“If you can’t explain it simply, you don’t understand it well enough.” —Albert Einstein Our everyday lives are filled with a massive flow of information that we must interpret in order to understand the world we live in. Considering the complex variety of data floating around us, sometimes the best—or even only—way to communicate is visually. This unique book presents a fascinating perspective on the subject, highlighting the work of the masters of the profession, creators of breakthroughs that have changed the way we communicate. Information Graphics has been conceived and designed not just for graphics professionals, but for anyone interested in the history and practice of communicating visually. The in-depth introductory section, illustrated with over 60 images (each accompanied by an explanatory caption), features essays by Sandra Rendgen, Paolo Ciuccarelli, Richard Saul Wurman, and Simon Rogers. Looking back all the way to primitive cave paintings as a means of communication, this section gives readers an excellent overview of the subject. The second part of the book is entirely dedicated to contemporary works by today’s most renowned professionals, presenting 200 graphics projects, with over 400 examples—each with a fact sheet and an explanation of methods and objectives—divided into chapters by the topics Location, Time, Category, and Hierarchy. Includes: 200 projects and over 400 examples of contemporary information graphics from all over the world—ranging from journalism to art, government, education, business and much more Four essays about the development of information graphics since its beginnings

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Book SynopsisWhat’s the best book ever written? What would happen if we all stopped eating meat? What's the secret to living past 110? And what actually is the best thing since sliced bread? In An Answer For Everything, 200 of the world’s most intriguing questions are settled once and for all through beautiful and brilliant infographics. The results will leave you shocked, informed and thoroughly entertained. Created by the team behind the award-winning Delayed Gratification magazine, these compelling, darkly funny data visualisations will change the way you think about ... everythingTrade ReviewFresh, fascinating, and fun – a visual and verbal treat * Ian Hislop *A chunky, colourful book packed with fascinating facts and figures in infographic form * Daily Mirror, Books of the Year *Data just got a lot more fun * It's Nice That *Achieves the ultimate factual book goal; makes you cleverer, with minimal effort on your part. You’ll learn from it but somehow it makes that pleasurable * Rachel Parris, The Mash Report *A fabulous compendium of a thousand new things you didn’t know you didn’t know. And it looks very nice too * Andrew Hunter Murray, No Such Thing as a Fish, QI *Delayed Gratification is one of my favourite magazines, and one of the best things about it is the infographics. They've now published a book of them and it's glorious! * Alex Bellos *

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Book SynopsisThis book provides users with cutting edge methods and technologies in the area of big data and visual analytics, as well as an insight to the big data and data analytics research conducted by world-renowned researchers in this field. The authors present comprehensive educational resources on big data and visual analytics covering state-of-the art techniques on data analytics, data and information visualization, and visual analytics. Each chapter covers specific topics related to big data and data analytics as virtual data machine, security of big data, big data applications, high performance computing cluster, and big data implementation techniques. Every chapter includes a description of an unique contribution to the area of big data and visual analytics. This book is a valuable resource for researchers and professionals working in the area of big data, data analytics, and information visualization. Advanced-level students studying computer science will also find this book helpful as a secondary textbook or reference.Table of ContentsInformation visualization.- Data analytics.- Visual analytics.- Intelligent information systems.- Business analytics,- Virtual data machine.- Big data architecture.- Security of big data.- Big data applications.- Tensor-based computation and modeling.- High performance computing cluster.- Big data technologies.

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Book SynopsisTrade Review"[Healy’s] prose is engaging and chatty, and the style of instruction is unpretentious and practical . . . This single volume represents an excellent entry point for those wishing to upskill their abilities in data visualization."---Paul Cuffe, IEEE Transactions"Undoubtedly, this book is an excellent introduction to an essential tool for anyone who needs to collect and present data." * Conservation Biology *

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Book SynopsisThis book closes the gap between what people think Excel can do and what they can achieve in the tool. Over the past few years, recognition of the importance of effectively visualizing data has led to an explosion of data analysis and visualization software tools. But for many people, Microsoft Excel continues to be the workhorse for their data visualization needs, not to mention the only tool that many data workers have access to. Although Excel is not a specialist data visualization platform, it does have strong capabilities. The default chart types do not need to be the limit of the toolâs data visualization capabilities, and users can extend its features by understanding some key elements and strategies. Data Visualization in Excel provides a step-by-step guide to creating more advanced and often more effective data visualizations in Excel and is the perfect guide for anyone who wants to create better, more effective, and more engaging data visualizations.Trade Review"Microsoft Excel is already installed on 750 million computers. But creating modern visualizations using Excel charts seems impossible. In this book, Jonathan Schwabish gives you the step-by-step instructions to make Excel charts do what most people (including me) previously thought was impossible in Excel. Every chapter, you will learn how to make visualizations that most people assume require Python or R."--Bill Jelen, Excel MVP and publisher of MrExcel.com “Those who expanded their graphicacy with Schwabish's Better Data Visualizations are going to love the follow up, in which Jon outlines how to make graphs using the ubiquitous Microsoft Excel. Readers will appreciate Jon's pragmatic examples, easy-to-follow instructions and entertaining writing. If you need to communicate with data and lack confidence making graphs in Excel, I urge you to add Data Visualization in Excel to your library!”--Cole Nussbaumer Knaflic, founder and CEO of storytelling with data (SWD)“Wow, just wow. One book that explains how to make all the fancy, powerful charts you see in good ol' Excel. If you want to impress your boss with Excel charting wizardry, this is THE BOOK for you. Not only does the book explain the chart construction process, it also provides guidance on designing great outputs. I wish I had this book when I started my data analyst journey. A must buy. Highly recommended.”--Chandoo (Purna Duggirala), Microsoft MVP, Chandoo.org"This book takes you well beyond the defaults of Excel charting. It shows you how to apply principles and best practices of data visualization in an accessible way. In the process it streamlines the use of Excel while enabling the creation of much more advanced graphics."--Jon Peltier, Peltier Technical Services “Thank you Jonathan Schwabish for giving us such a clear how-to book! Data Visualization in Excel shows us how far we can go with Excel to make data visualization insights accessible to a much broader audience! I am making this my go-to textbook for teaching myself and my students to create better, more effective, and different graphs in Excel to operationalize the data visualization concepts and strategies the earlier books have inspired in us.”-- Kosali Simon, Distinguished Professor, Indiana University, O’Neill School of Public and Environmental Affairs"There aren’t many places where the cliché “think outside the box” can be applied with such propriety (and benefits) than when making charts in Excel. If you go beyond its poor chart library and defaults, there is a whole world to discover, and Jon’s new book is a great journey companion. This detailed step-by-step tutorial will show you how to take advantage of Excel’s flexibility, how to use design and formatting options far from their original intent and how to visualize data directly on the spreadsheet, going even further away from the chart library. On top of this, Jon is a communicator and a data visualization expert, so his many examples are relevant (many analytical tasks can benefit from them) and designed for effectiveness applying sound data visualization guidelines."-- Jorge Camões, data visualization consultant, founder of Excelcharts.com"For data visualisation practitioners looking to hone their charting craft there are three core questions: what, when, and how. What different options exist. When should I use. How do I make them. Jon’s bestseller "Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks” addresses the first two of those enquiries, and much more besides, but this excellent new text gives explicit solutions for the matter of how. For Excel users, beginners or advanced, this book will become an essential practical companion. You’ll learn how to elegantly master the standard charting functions in the most effective and efficient way, but you’ll also discover how to truly make Excel sing, finding novel approaches and clever workarounds to expand your visual vocabulary even further."--Andy Kirk, independent consultant, educator, and author, founder of visualisingdata.com"I found this book 20 years too late! Seriously, I wish I had this book a long time ago, for its rich collection of tutorials, best practices, lessons learned, and principles of data visualization using Excel. From broken stacked bars to Gantt charts, heatmaps, tile grid maps, and waffle plots (and so much more from A to Z), the many detailed visualization examples in this book will provide tremendous benefit in the workplace whenever and wherever data storytelling, visual analytics, or data-driven decision insights are required. Excel users from beginners to experts will discover useful data visualization examples, corresponding detailed instructions, and perhaps some secrets of Excel here in this wonderful book."--Kirk Borne, Chief Science Officer, DataPrime Inc. "Jonathan Schwabish taught me what constitutes a good chart. In this incredibly useful volume, he teaches me how to make a good chart in Excel. A step-by-step guide – practical, easy to follow and (of course) well-illustrated."--David Wessel, Director, Hutchins Center on Fiscal & Monetary Policy, The Brookings Institution "Finally, a comprehensive data visualisation book for Excel that’s detailed enough that a relative beginner can use it, and extensive enough that an experienced user will also discover tips and learn valuable Excel techniques and data visualisation best practices."--Mynda Treacy, MyOnlineTrainingHub.com"Data visualization has an ever-growing toolbox of applications and programming languages for creating charts, but some may come with steep learning curves, some may require coding skills, and some resources may lack the full range of functionality we really need. The one, often overlooked tool familiar and often available to most of us is Microsoft Excel. In this companion book to Better Data Visualizations: A Guide for Scholars, Researchers and Wonks, Jon Schwabish systematically and expertly shows us how to create a wide variety of visualization types, from simple heatmaps right through to more complex examples such as raincloud plots and Marimekko charts, each with detailed tutorials and with all companion resources available via his website. This book first opens your eyes to the wide range of visualization possibilities that were most likely available to you all along in Excel, then promptly equips you with the skills to create them all."--Neil Richards, Lead Business Intelligence Analyst at JLL, former Knowledge Director for the Data Visualization SocietyTable of ContentsAcknowledgements. PART ONE: SETTING THE STAGE. 1. Introduction. 2. How to Use This Book. 3. The Philosophy of Data Visualization in Excel. 4. Introduction to Making Graphs in Excel. 5. Fundamental Formulas Used in Building Your Graphs. 6. Building a Custom Color Palette in Excel. PART TWO: MAKING GRAPHS IN EXCEL. 7. Sparklines. 8. Heatmap. 9. Stripe Chart. 10. Waffle Chart. 11. Gantt Chart. 12. Comparing Values with Two Graph Types. 13. Broken Stacked Bar Chart. 14. Diverging Bar Chart. 15. Block Shading (Same Frequency). 16. Block Shading (Different Frequencies). 17. Mark an Event with a Line. 18. Dot Plot. 19. Slope Chart. 20. Overlaid Gridlines. 21. Lollipop Chart. 22. Bullet Chart. 23. Tile Grid Map. 24. Histogram. 25. Marimekko Chart. 26. Cycle Plot. 27. Strip Chart. 28. Raincloud Plot. 29. Making Better Tables. PART THREE: MOVING VISUALS OUT OF EXCEL. 30. Exporting Graphs from Excel. 31. Redesigns and Examples. 32. Conclusion. Appendix 1. Color Tools. Appendix 2. Data Visualization Tools. Appendix 3. Additional Excel Resources. Appendix 4. Quick Instructions List. References. Index.

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Book SynopsisSpatial statistics empowers you to go beyond visual analysis to answer questions confidently and make data-driven decisions.Thanks to the data and computational power now at our fingertips, data science is in every aspect of our lives. But with so many algorithms and buzzwords floating around, where do you start to solve complex problems or figure out where to go next?There has never been a more exciting time to learn about spatial statistics. Spatial statistics uses an aspect of geography that helps you quantify patterns and relationships so that you can feel confident in your analysis.Spatial Statistics Illustrated is an introductory book for learning the concepts behind the powerful spatial statistics tools in ArcGIS.With approachable explanations and uncomplicated drawings, Spatial Statistics Illustrated gives readers an accessible understanding of some of the most widely used spatial statistics methods, including how they work and when to use them. In a friendly, conversational tone, the authors share techniques that can help you explore your data in meaningful ways; quantify patterns and relationships; understand trends, and make informed, impactful decisions.This book has something for everyone analyzing data, including: seasoned data scientists looking to explore the value that spatial analysis offers GIS analysts looking to expand their spatial statistics skill set new GIS users discovering the value of spatial statistics Spatial Statistics Illustrated is a perfect complement to more traditional, technical statistics and spatial statistics texts and is also ideal as supplemental reading for academic courses.Based on the popular series of Spatial Statistics workshops presented by the authors at the annual Esri User Conference, Spatial Statistics Illustrated welcomes readers into the unparalleled world of spatial statistics.

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Book SynopsisPipelines can be challenging to manage, especially when your data has to flow through a collection of application components, servers, and cloud services. Airflow lets you schedule, restart, and backfill pipelines, and its easy-to-use UI and workflows with Python scripting has users praising its incredible flexibility. Data Pipelines with Apache Airflow takes you through best practices for creating pipelines for multiple tasks, including data lakes, cloud deployments, and data science. Data Pipelines with Apache Airflow teaches you the ins-and-outs of the Directed Acyclic Graphs (DAGs) that power Airflow, and how to write your own DAGs to meet the needs of your projects. With complete coverage of both foundational and lesser-known features, when you’re done you’ll be set to start using Airflow for seamless data pipeline development and management. Key Features Framework foundation and best practices Airflow's execution and dependency system Testing Airflow DAGs Running Airflow in production For data-savvy developers, DevOps and data engineers, and system administrators with intermediate Python skills. About the technology Data pipelines are used to extract, transform and load data to and from multiple sources, routing it wherever it’s needed -- whether that’s visualisation tools, business intelligence dashboards, or machine learning models. Airflow streamlines the whole process, giving you one tool for programmatically developing and monitoring batch data pipelines, and integrating all the pieces you use in your data stack. Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies including Heineken, Unilever, and Booking.com. Bas is a committer, and both Bas and Julian are active contributors to Apache Airflow.

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Book SynopsisThis practical book shows you how to master all areas of Alteryx Designer quickly. Author and Alteryx ACE Joshua Burkhow starts with the basics of building a workflow, then introduces more than 200 tools for working with intermediate and advanced analytics functionality.

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Book SynopsisSheila Pontis is lecturer at Princeton University (USA), honorary research associate at UCL (UK), and partner at Sense Information Design. Since 2002, she teaches and facilitates workshops on information design, design research, ethnography, and design thinking in Argentina, Spain, the UK and the US. She has produced work for diverse organisations in the US, South America and Europe including Pfizer, TfL, Elsevier, Unilever and NHS. Michael Babwahsingh is an information designer and partner at Sense Information Design. His experience spans strategic design and innovation, branding, and communication design for corporate and non-profit clients. He has taught design thinking at NYU Wagner Graduate School of Public Service. Michael received a BA in Art and Graphic Design from Moravian College.Table of ContentsIntroduction PART I. Context 1. Understanding information design 1.1 Finding our way every day 1.2 Building a working definition 1.3 Mapping out the landscape 1.4 Seeing the bigger picture 1.5 What is effective information design? 1.6 Tracing the historical roots 1.7 Information design comes of age Exercises 2. How information designers work 2.1 What makes an information designer? 2.2 A collaborative, cross-disciplinary practice 2.3 Ethics and professional conduct 2.4 Working with the information design process 2.5 The scale of information design challenges Exercises 3. How we process visual information 3.1 The visual information processing system 3.2 Vision 3.3 Perception 3.4 Learning and retrieval 3.5 Thinking and problem-solving Exercises 4. How we create meaning 4.1 Understanding culture 4.2 Signs, the basic units of meaning 4.3 Rhetoric and communication 4.4 Society, identity, and representation Exercises PART II. Skills 5. Thinking and working visually 5.1 Giving shape to thought 5.2 Assembling your visual toolkit 5.3 Learning to sketch 5.4 Using diagrams to think 5.5 Making use of space and form Exercises 6. Learning through research 6.1 A structured search for knowledge 6.2 Understanding the subject matter 6.3 Understanding the audience 6.4 Planning out your research 6.5 Collecting data Exercises 7. Sensemaking for communication 7.1 Planning before designing 7.2 Constructing knowledge 7.3 Organizing information Exercises 8. Designing with clarity 8.1 Focusing on effectiveness 8.2 Content 8.3 Structure 8.4 Presentation 8.5 Engagement 8.6 Performance Exercises PART III. Practice 9. Communications 9.1 Working with communications 9.2 Project Re:form 9.3 GAIA Amazonas icon system 9.4 Emergency ventilator instructions 9.5 We Are Here: An Atlas of Aotearoa 10. Experiences 10.1 Working with experiences 10.2 Nemus Futurum interactive visitor experience 10.3 SEPTA wayfinding master plan 10.4 Skicircus wayfinding system 10.5 Seat at the Table interactive exhibit 11. Organizations 11.1 Working with organizations 11.2 Oregon Museum of Science and Industry vision and strategy 11.3 Integrated journey maps 12. Systems 12.1 Working with systems 12.2 Understanding elderly isolation in Pittsburgh 12.3 Systems approach to youth employment in Bhutan 12.4 Navigating the complexity of cancer diagnosis Resources Bibliography Index Acknowledgments Image credits

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Book SynopsisInformation may be beautiful, but our decisions about the data we choose to represent and how we represent it are never neutral. This insightful history traces how data visualization accompanied modern technologies of war, colonialism and the management of social issues of poverty, health and crime. Discussion is based around examples of visualization, from the ancient Andean information technology of the quipu to contemporary projects that show the fate of our rubbish and take a participatory approach to visualizing cities. This analysis places visualization in its theoretical and cultural contexts, and provides a critical framework for understanding the history of information design with new directions for contemporary practice.Trade ReviewDebunking the idea that data is ever ‘raw’ or unbiased, this book brings information anxiety to a new level as it goes deep into the underlying power structures at play in the assemblage of data and the motivations of those who amass it. Hall and Dávila explain how design’s focus on clarity and statistical accuracy can serve to enhance dominant narratives inherent in the data and challenge designers to activate their agency to visualize the kind of world in which we want to live. This should be required reading in any data visualization or information design curriculum. -- Thomas Starr, Professor of Graphic and Information Design, Northeastern University, USAHall and Dávila make a compelling argument for a critical approach to data visualization. Through a comprehensive survey of extant literature, a rereading of canonical images through decolonizing frameworks, and discussion of highly topical debates, they arrive at a rich examination of current projects drawn from a wide array of activities. They address self-quantification, smart cities, emotional cartography, and a whole host of specific and activist interventions in conventional data practices. Ultimately, they argue for visualizations that might create alternatives to dominant conventions and the oppressive power asymmetries of the status quo. -- Johanna Drucker, Distinguished Professor of Information Studies, UCLA, USAWith acuity and depth, Hall and Dávila demonstrate just how much history, culture and context matter for the design and interpretation of data visualization. Their book is timely and important, and will usher in a new era of critical data practice. -- Lauren Klein, Winship Distinguished Research Professor, Departments of English and Quantitative Theory and Methods, Emory University, USATable of Contents1. An Introduction to Critical Visualization Defining the field Looking at Visualization beyond Western Paradigms Alternative Western perspectives: Distributed Cognition and Humanistic Approaches 2. Disruptive Histories Positivism and Objectivity A History of Progress Critical Cartography: a 'Defining Moment' A Few Examples: Not a Canon - Haptic Visualization: the Quipu (1200-1532) - Plan and Sections of a Slave Ship (1789) - Polar Area Diagram (1859) - Great Trigonometrical Survey of India (1802-1875) - Data Visualization at the Paris Exposition, W.E.B. Du Bois (1900) - Community-building with Isotype: Otto and Marie Neurath Conclusion Focus: Anna Ridler, Myriad (Tulips) 2018 3. Making Data Qualitative and Quantitative Data The Role of Categorization Focus: Data4Change - Keepiton - Hear the Blind Spot - Perceiving Yemen 4. Data and the Self Taylorism Within? Comic Critique What is Normal? Biometrics and Risk-Profiling Challenging Norms The Examined Life Focus: Margaret Pearce and Michael Hermann, They Would Not Take Me There: People, Places, and Stories from Champlain’s Travels in Canada, 1603-1616 5. Data and the City Participatory planning: HECTOR Focus: Heath Bunting: Status Project 6. Aesthetics and Representation Aesthetics and Representation Representation as Translation 7. Beyond Critical Visualization Practice

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Book Synopsis'Terrifying yet funny, surprising yet predictable, simple yet poignant' Chris PackhamA shocking but informative, eye-catching and witty book of maps that illustrate the perilous state of our planet.The maps in this book are often shocking, sometimes amusing, and packed with essential information:· Did you know that just 67 companies worldwide are responsible for 67 per cent of global greenhouse emissions? · Or that keeping a horse has the same carbon footprint as a 23,500-kilometre road trip? · Did you know how many countries use less energy than is consumed globally by downloading porn from the internet?· Do you know how much of the earth's surface has been concreted over?· Or how many trees would we have to plant to make our planet carbon-neutral?Presenting a wealth of innovative scientific research and data in stunning, beautiful infographics, 99 Maps to Save the Planet provides us with instant snapshots of the destruction of our environment. At one glance, we can see the precarious state of our planet - but also realise how easy it would be to improve it Enlightening, a bit frightening, but definitely inspiring, 99 Maps to Save the Planet doesn't provide practical tips on how to save our planet: it just presents the facts. And the facts speak for themselves. Once we know them, what excuse do we have for failing to act?Trade ReviewYou'll never look at the fight for our common home in the same way again after seeing the images in this remarkable book * Big Issue *Impressively imaginative and effectively alarming * Wanderlust *Terrifying yet funny, surprising yet predictable, simple yet poignant -- Chris Packham

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Book SynopsisD3 allows developers to create scalable graphs for any modern browser. They start with a structure, dataset, or algorithm and programmatically generate static, interactive, or animated images that responsively scale to any screen. D3.js in Action, Second Edition is completely revised and updated for D3 v4 and ES6. It's a practical tutorial for creating interactive graphics and data-driven applications using D3. Readers will start with in-depth explanations of D3's out-of-the-box layouts, along with dozens of realworld use cases that align with different types of visualizations. By the end, readers will be ready to integrate D3.js into their web development process and add data visualization to transform any site or internal application. Key Features: · Completely revised and updated · Practical tutorial · In-depth explanations Readers need basic HTML, CSS, and JavaScript skills. No experience with D3 or SVG is required. About the Technology: D3.js is a JavaScript library that allows data to be represented graphically.

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Book SynopsisTable of ContentsIntroduction 1 Part 1: Tackling Tableau Basics 5 Chapter 1: Learning Tableau Lingo 7 Chapter 2: Venturing into Tableau Versions 25 Chapter 3: Mapping Out the Data Journey 39 Part 2: Prepping Data in Tableau 55 Chapter 4: Connecting Your Data 57 Chapter 5: Ready, Set, Data Prep: Readying Your Data for Prime Time 85 Chapter 6: Structuring Data for Liftoff 115 Part 3: Telling the Data Story in Tableau 129 Chapter 7: Touring Tableau Desktop 131 Chapter 8: Visualization Foundations in Tableau 161 Chapter 9: Analytic Practices in Action. 197 Chapter 10: Showcasing Data with Dashboards and Stories 213 Part 4: Showcasing Your Data in the Tableau Cloud 237 Chapter 11: Collaboration and Publishing with Tableau Cloud 239 Chapter 12: Administration in Tableau Cloud 257 Part 5: The Part of Tens 275 Chapter 13: Ten Tableau Must-Know Function Types 277 Chapter 14: Ten Helpful Tableau Resources 289 Index 295

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Book SynopsisHow can you transform a spreadsheet of numbers into a clear, compelling story that your audience will want to pass on? This book is a step-by-step guide (honed through the authors′ Guardian masterclasses, workshops and seminars) to bringing data to life through visualisations, from static charts and maps to interactive infographics and motion graphics. Introducing a four-step framework to creating engaging and innovative visualisations, it helps you to: · Find the human stories in your datasets · Design a visual story that will resonate with your audience · Make a clear, persuasive visual that represents your data truthfully · Refine your work to ensure your visual expresses your story in the best possible way. This book also includes a portfolio of best-practice examples and annotated templates to help you choose the right visual for the right audience, and repurpose your work for different contexts.Trade ReviewThis is an enjoyable and informative read from four premier data visualisation experts. Grounded in their award-winning practice and teaching, the authors offer concrete strategies and a wealth of examples for anyone wishing to create intuitive and engaging data visualisations. -- Penny Sheets ThibautThe book is a refreshingly witty guide to data visualisation that entertains while it educates. It offers a clear and easy to follow process illustrated through a dizzying range of examples. It’s a great combination of design principles and real-world lessons learnt through actual projects. Fun, accurate, erudite and teeming with useful nuggets of information - very much like some of their best data visualisations! -- Emma WhiteheadTable of ContentsIntroduction: Notes from the Underground Part 1: The Data Visualisation Process Chapter 1: Find, design, make, refine Chapter 2: A spectrum of right answers Chapter 3: Find Chapter 4: Designing Static Graphics Chapter 5: Making Static Graphics Chapter 6: An Introduction to Interactive Data Visualisation Chapter 7: Designing Motion Graphics Chapter 8: Making Motion Graphics Chapter 9: Designing Interactive Infographics Chapter 10: Making Interactive Infographics Chapter 11: Refine Chapter 12: Resources Part 2: Finding the Right Chart For Your Story

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Book SynopsisStreamline your workflow, transform raw data into actionable insights, and use Alteryx Designer to shape, design, and visualize data Key Features Acquire the skills necessary to perform analytics operations like an expert Discover hidden trends and insights in your data from various sources to make accurate predictions Reduce the time and effort required to derive insights from your data Purchase of the print or Kindle book includes a free eBook in the PDF format Book DescriptionAlteryx allows you to create data manipulation and analytic workflows with a simple, easy-to-use, code-free UI, and perform fast-executing workflows, offering multiple ways to achieve the same results. The Alteryx Designer Cookbook is a comprehensive guide to maximizing your Alteryx skills and determining the best ways to perform data operations. This book's recipes will guide you through an analyst's complete journey, covering all aspects of the data life cycle. The first set of chapters will teach you how to read data from various sources to obtain reports and pass it through the required adjustment operations for analysis. After an explanation of the Alteryx platform components with a particular focus on Alteryx Designer, you’ll be taken on a tour of what and how you can accomplish by using this tool. Along the way, you’ll learn best practices and design patterns. The book also covers real-world examples to help you apply your understanding of the features in Alteryx to practical scenarios. By the end of this book, you’ll have enhanced your proficiency with Alteryx Designer and an improved ability to execute tasks within the tool efficiently.What you will learn Speed up the cleansing, data preparing, and shaping process Perform operations and transformations on the data to suit your needs Blend different types of data sources for analysis Pivot and un-pivot the data for easy manipulation Perform aggregations and calculations on the data Encapsulate reusable logic into macros Develop high-quality, data-driven reports to improve consistency Who this book is forThis book is for data analysts, data professionals, and business intelligence professionals seeking to harness the full potential of the tool. A basic understanding of Alteryx Designer and Alteryx terminology, including macros, apps, and workflows, is all you need to get started with this book.Table of ContentsTable of Contents Input data from files Working with databases Data Preparation Data Transformations Data Parsing Grouping Data Blending and Merging data Aggregations Dynamic Operations/ Tools Macros and Apps Downloads, APIs & Web Services Developer options Reporting with Alteryx Outputting Data

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Book SynopsisGain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoostKey Features Think critically about data and use it to form and test a hypothesis Choose an appropriate machine learning model and train it on your data Communicate data-driven insights with confidence and clarity Book DescriptionIf data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable.In this book, you’ll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you’ll experience in real-world data science projects.You’ll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest.Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world.By the end of this data science book, you’ll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.What you will learn Load, explore, and process data using the pandas Python package Use Matplotlib to create compelling data visualizations Implement predictive machine learning models with scikit-learn Use lasso and ridge regression to reduce model overfitting Evaluate random forest and logistic regression model performance Deliver business insights by presenting clear, convincing conclusions Who this book is forData Science Projects with Python – Second Edition is for anyone who wants to get started with data science and machine learning. If you’re keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics.Table of ContentsTable of Contents Data Exploration and Cleaning Introduction to Scikit-Learn and Model Evaluation Details of Logistic Regression and Feature Exploration The Bias-Variance Trade-off Decision Trees and Random Forests Gradient Boosting, XGBoost, and SHAP (SHapley Additive exPlanations) Values Test Set Analysis, Financial Insights, and Delivery to the Client

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Book SynopsisThis new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to: produce handsome, publication-quality plots with automatic legends created from the plot specification superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.Trade Review“The versatility and efficiency of ggplot have led to the development of ggplot2 and this book which overviews the standard use and presentation secrets of functions developed in the last 5 years. … The book is written in an accessible manner and it is suitable for undergraduates, postgraduates and researchers with some R experience. All theoretical concepts are accompanied by code making it easy to learn by reproducing the examples.” (Irina Ioana Mohorianu, zbMATH 1397.62006, 2018)“The book is an excellent and very comprehensive manual of … one of the most popular R packages. It is currently the only book describing ggplot2 in such depth. The book contains many examples and is very nicely illustrated, demonstrating the strength of the package.” (Klaus Galensa, Computing Reviews, May, 2017)Table of ContentsIntroduction.- Getting Started with ggplot2.- Toolbox.- Mastering the Grammar.- Building a Plot Layer by Layer.- Scales, Axes and Legends.- Positioning.- Themes.- Data Analysis.- Data Transformation.- Modelling for Visualisation.- Programming with ggplot2.- Index.- R Code Index.

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Book SynopsisMaster the concepts and strategies underlying success and progress in data science. From the author of the bestsellers, Data Scientist and Julia for Data Science, this book covers four foundational areas of data science. The first area is the data science pipeline including methodologies and the data scientists toolbox. The second are essential practices needed in understanding the data including questions and hypotheses. The third are pitfalls to avoid in the data science process. The fourth is an awareness of future trends and how modern technologies like Artificial Intelligence (AI) fit into the data science framework. Targeted towards data science learners of all levels, this book aims to help the reader go beyond data science techniques and obtain a more holistic and deeper understanding of what data science entails. With a focus on the problems data science tries to solve, this book challenges the reader to become a self-sufficient player in the field.

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Trade ReviewFrom the reviews of the second edition: "This fascinating book deconstructs the process of producing graphics and in doing so raises many fascinating questions on the nature and representation of information...This second edition is almost twice the size of the original, with six new chapters and substantial revisions." Short Book Reviews of the International Statistical Institute, December 2005 "When the first edidtion of this book appeared in 2000 it was much praised. I called it a tour de force of the highest order. (Wainer, 2001), Edward Wegman (2000) argued that it was destined to become a classic. Now, six years later this very fine book has been much improved." Howard Wainer for Psychometrika "...The second edition is an impressive expansion beyond a quite remarkable first edition. The text remains dense and even more encyclopedic, but it is a pleasure to read, whether a novice or an expert in graphics...this book is a bargain...The second edition is a must-have volume for anyone interested in graphics." Thomas E. Bradstreet for the Journal of the American Statistical Association, December 2006 "I find myself still thinking about the book and its ideas, several weeks after I finished reading it. I love that kind of book." Mark Bailey for Techometrics, Vol. 49, No. 1, February 2007 "Warts and all, The Grammar of Graphics is a richly rewarding work, an outstanding achievement by one of the leaders of statistical graphics. Seek it out." Nicholas J. Cox for the Journal of Statistical Software, January 2007 "The second edition is a quite fascinating book as well, and it comes with many color graphics. Anyone working in this field can see how many hours the author (plus coworkers) has spent on such a volume. … Demands for good graphics are high and this book will help to wetten the appetite to create future computer packages that will meet this demand. An occasional reader will get insights into a modern world of computing … ." (Wolfgang Polasek, Statistical Papers, Vol. 48, 2007)Table of ContentsSyntax.- How To Make a Pie.- Data.- Variables.- Algebra.- Scales.- Statistics.- Geometry.- Coordinates.- Aesthetics.- Facets.- Guides.- Semantics.- Space.- Time.- Uncertainty.- Analysis.- Control.- Automation.- Reader.- Coda.

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Book SynopsisThis 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.Table of ContentsVector Quantisation and Topology-Based Graph RepresentationGraph-Based Clustering AlgorithmsGraph-Based Visualisation of High-Dimensional Data

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Book Synopsis1 Introduction.- 2 Historical Background.- 3 Time & Time-Oriented Data.- 4 Crafting Visualizations of Time-Oriented Data.- 5 Involving the Human via Interaction.- 6 Computational Analysis Support.- 7 Guiding the Selection of Visualization Techniques.- 8 Conclusion.Table of ContentsForeword.- Preface.- About the Authors.- Acknowledgements.- 1 Introduction.- 2 Historical Background.- 3 Time & Time-Oriented Data.- 4 Crafting Visualizations of Time-Oriented Data.- 5 Involving the Human via Interaction.- 6 Computational Analysis Support.- 7 Guiding the Selection of Visualization Techniques.- 8 Conclusion. Appendix A: Survey of Visualization Techniques.- Appendix B: Examples of Data Quality Issues.- References.- Index

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Book SynopsisLearn to view, edit and analyse geospatial data using QGIS and Python 3Key Features Leverage the power of QGIS to add professionalism to your maps Explore and work with the newly released features like Python 3, GeoPackage, 3D views, Print layouts in QGIS 3.4 Build your own plugins and customize maps using QT designer Book DescriptionQGIS 3.4 is the first LTR (long term release) of QGIS version 3. This is a giant leap forward for the project with tons of new features and impactful changes. Learn QGIS is fully updated for QGIS 3.4, covering its processing engine update, Python 3 de-facto coding environment, and the GeoPackage format.This book will help you get started on your QGIS journey, guiding you to develop your own processing pathway. You will explore the user interface, loading your data, editing, and then creating data. QGIS often surprises new users with its mapping capabilities; you will discover how easily you can style and create your first map. But that’s not all! In the final part of the book, you’ll learn about spatial analysis and the powerful tools in QGIS, and conclude by looking at Python processing options.By the end of the book, you will have become proficient in geospatial analysis using QGIS and Python.What you will learn Explore various ways to load data into QGIS Understand how to style data and present it in a map Create maps and explore ways to expand them Get acquainted with the new processing toolbox in QGIS 3.4 Manipulate your geospatial data and gain quality insights Understand how to customize QGIS 3.4 Work with QGIS 3.4 in 3D Who this book is forIf you are a developer or consultant familiar with the basic functions and processes of GIS and want to learn how to use QGIS to analyze geospatial data and create rich mapping applications, this book is for you. You’ll also find this book useful if you’re new to QGIS and wish to grasp its fundamentalsTable of ContentsTable of Contents Where do I start? Data Creation and Editing Visualizing data Creating Great Maps Running geospatial queries on data Extending and customizing QGIS 3

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Book SynopsisA practical guide to getting the most out of Excel, using it for data preparation, applying machine learning models (including cloud services) and understanding the outcome of the data analysis.Key Features Use Microsoft's product Excel to build advanced forecasting models using varied examples Cover range of machine learning tasks such as data mining, data analytics, smart visualization, and more Derive data-driven techniques using Excel plugins and APIs without much code required Book DescriptionWe have made huge progress in teaching computers to perform difficult tasks, especially those that are repetitive and time-consuming for humans. Excel users, of all levels, can feel left behind by this innovation wave. The truth is that a large amount of the work needed to develop and use a machine learning model can be done in Excel.The book starts by giving a general introduction to machine learning, making every concept clear and understandable. Then, it shows every step of a machine learning project, from data collection, reading from different data sources, developing models, and visualizing the results using Excel features and offerings. In every chapter, there are several examples and hands-on exercises that will show the reader how to combine Excel functions, add-ins, and connections to databases and to cloud services to reach the desired goal: building a full data analysis flow. Different machine learning models are shown, tailored to the type of data to be analyzed.At the end of the book, the reader is presented with some advanced use cases using Automated Machine Learning, and artificial neural network, which simplifies the analysis task and represents the future of machine learning.What you will learn Use Excel to preview and cleanse datasets Understand correlations between variables and optimize the input to machine learning models Use and evaluate different machine learning models from Excel Understand the use of different visualizations Learn the basic concepts and calculations to understand how artificial neural networks work Learn how to connect Excel to the Microsoft Azure cloud Get beyond proof of concepts and build fully functional data analysis flows Who this book is forThis book is for data analysis, machine learning enthusiasts, project managers, and someone who doesn't want to code much for performing core tasks of machine learning. Each example will help you perform end-to-end smart analytics. Working knowledge of Excel is required.Table of ContentsTable of Contents Implementing Machine Learning Algorithms Hands-on examples of machine learning models Importing Data into Excel from Different Data Sources Data cleansing and preliminary data analysis Correlations and the Importance of Variables Data Mining Models in Excel Hands-On Examples Implementing Time Series Visualizing data in diagrams, histograms, and maps Artificial Neural Networks Azure and Excel - Machine Learning in the Cloud The future of Machine Learning

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Book SynopsisBuild a continuously learning and adapting organization that can extract increasing levels of business, customer and operational value from the amalgamation of data and advanced analytics such as AI and Machine Learning Key Features Master the Big Data Business Model Maturity Index methodology to transition to a value-driven organizational mindset Acquire implementable knowledge on digital transformation through 8 practical laws Explore the economics behind digital assets (data and analytics) that appreciate in value when constructed and deployed correctly Book DescriptionIn today’s digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator. The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization’s data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise. The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company’s operations through AI and machine learning. By the end of the book, you will have the tools and techniques to drive your organization’s digital transformation. Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book: "Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon."What you will learn Train your organization to transition from being data-driven to being value-driven Navigate and master the big data business model maturity index Learn a methodology for determining the economic value of your data and analytics Understand how AI and machine learning can create analytics assets that appreciate in value the more that they are used Become aware of digital transformation misconceptions and pitfalls Create empowered and dynamic teams that fuel your organization’s digital transformation Who this book is forThis book is designed to benefit everyone from students who aspire to study the economic fundamentals behind data and digital transformation to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their business careers.Table of ContentsTable of Contents The CEO Mandate: Become Value-driven, Not Data-driven Value Engineering: The Secret Sauce for Data Science Success A Review of Basic Economic Concepts University of San Francisco Economic Value of Data Research Paper The Economic Value of Data Theorems The Economics of Artificial Intelligence The Schmarzo Economic Digital Asset Valuation Theorem The 8 Laws of Digital Transformation Creating a Culture of Innovation Through Empowerment Appendix A: My Most Popular Economics of Data, Analytics, and Digital Transformation Infographics Appendix B: The Economics of Data, Analytics, and Digital Transformation Cheat Sheet

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Book SynopsisGain the knowledge and skills needed to become a certified Microsoft Power BI data analyst and get the most out of Power BIKey Features Get the skills you need to pass the PL-300 certification exam with confidence Create and maintain robust reports and dashboards to enable a data-driven enterprise Test your new BI skills with the help of practice questions Book DescriptionMicrosoft Power BI enables organizations to create a data-driven culture with business intelligence for all. This guide to achieving the Microsoft Power BI Data Analyst Associate certification will help you take control of your organization's data and pass the exam with confidence.From getting started with Power BI to connecting to data sources, including files, databases, cloud services, and SaaS providers, to using Power BI's built-in tools to build data models and produce visualizations, this book will walk you through everything from setup to preparing for the certification exam. Throughout the chapters, you'll get detailed explanations and learn how to analyze your data, prepare it for consumption by business users, and maintain an enterprise environment in a secure and efficient way.By the end of this book, you'll be able to create and maintain robust reports and dashboards, enabling you to manage a data-driven enterprise, and be ready to take the PL-300 exam with confidence.What you will learn Connect to and prepare data from a variety of sources Clean, transform, and shape your data for analysis Create data models that enable insight creation Analyze data using Microsoft Power BI's capabilities Create visualizations to make analysis easier Discover how to deploy and manage Microsoft Power BI assets Who this book is forThis book is for data analysts and BI professionals who want to become more competent in Microsoft Power BI. Although the content in this book will help you pass the PL-300 exam, there are plenty of other practical applications beyond exam preparation in the chapters. No prior experience with Power BI is needed.Table of ContentsTable of Contents Overview of Power BI and the PL-300 Exam Connecting to Data Sources Profiling the Data Cleansing, Transforming, and Shaping Data Designing a Data Model Using Data Model Advanced Features Creating Measures Using DAX Optimizing Model Performance Creating Reports Creating Dashboards Enhancing Reports Exposing Insights from Data Performing Advanced Analytics Managing Workspaces Managing Datasets Practice Exams

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Book SynopsisDiscover over 80 recipes for modeling and handling real-life biological data using modern libraries from the R ecosystem Key Features Apply modern R packages to process biological data using real-world examples Represent biological data with advanced visualizations and workflows suitable for research and publications Solve real-world bioinformatics problems such as transcriptomics, genomics, and phylogenetics Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe updated second edition of R Bioinformatics Cookbook takes a recipe-based approach to show you how to conduct practical research and analysis in computational biology with R. You’ll learn how to create a useful and modular R working environment, along with loading, cleaning, and analyzing data using the most up-to-date Bioconductor, ggplot2, and tidyverse tools. This book will walk you through the Bioconductor tools necessary for you to understand and carry out protocols in RNA-seq and ChIP-seq, phylogenetics, genomics, gene search, gene annotation, statistical analysis, and sequence analysis. As you advance, you'll find out how to use Quarto to create data-rich reports, presentations, and websites, as well as get a clear understanding of how machine learning techniques can be applied in the bioinformatics domain. The concluding chapters will help you develop proficiency in key skills, such as gene annotation analysis and functional programming in purrr and base R. Finally, you'll discover how to use the latest AI tools, including ChatGPT, to generate, edit, and understand R code and draft workflows for complex analyses. By the end of this book, you'll have gained a solid understanding of the skills and techniques needed to become a bioinformatics specialist and efficiently work with large and complex bioinformatics datasets.What you will learn Set up a working environment for bioinformatics analysis with R Import, clean, and organize bioinformatics data using tidyr Create publication-quality plots, reports, and presentations using ggplot2 and Quarto Analyze RNA-seq, ChIP-seq, genomics, and next-generation genetics with Bioconductor Search for genes and proteins by performing phylogenetics and gene annotation Apply ML techniques to bioinformatics data using mlr3 Streamline programmatic work using iterators and functional tools in the base R and purrr packages Use ChatGPT to create, annotate, and debug code and workflows Who this book is forThis book is for bioinformaticians, data analysts, researchers, and R developers who want to address intermediate-to-advanced biological and bioinformatics problems by learning via a recipe-based approach. Working knowledge of the R programming language and basic knowledge of bioinformatics are prerequisites.Table of ContentsTable of Contents Setting Up Your R Bioinformatics Working Environment Loading, Tidying, and Cleaning Data in the tidyverse ggplot2 and Extensions for Publication Quality Plots Using Quarto to Make Data-Rich Reports, Presentations, and Websites Easily Performing Statistical Tests Using Linear Models Performing Quantitative RNA-seq Finding Genetic Variants with HTS Data Searching Gene and Protein Sequences for Domains and Motifs Phylogenetic Analysis and Visualization Analyzing Gene Annotations Machine Learning with mlr3 Functional Programming in puRRR and base R Turbo-Charging Development in R with ChatGPT

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Book SynopsisThe inverse scattering problem is central to many areas of science and technology such as radar, sonar, medical imaging, geophysical exploration and nondestructive testing. This book is devoted to the mathematical and numerical analysis of the inverse scattering problem for acoustic and electromagnetic waves. In this fourth edition, a number of significant additions have been made including a new chapter on transmission eigenvalues and a new section on the impedance boundary condition where particular attention has been made to the generalized impedance boundary condition and to nonlocal impedance boundary conditions. Brief discussions on the generalized linear sampling method, the method of recursive linearization, anisotropic media and the use of target signatures in inverse scattering theory have also been added.Table of ContentsIntroduction.- The Helmholtz Equation.- Direct Acoustic Obstacle Scattering.- Ill-Posed Problems.- Inverse Acoustic Obstacle Scattering.- The Maxwell Equations.- Inverse Electromagnetic Obstacle Scattering.- AcousticWaves in an Inhomogeneous Medium.- ElectromagneticWaves in an Inhomogeneous Medium.- Transmission Eigenvalues.- The Inverse Medium Problem.

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