Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information. Recently published, this new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.

    It has sold 30,000 copies in each edition and will continue to reach a huge academic audience.In this renowned book, Everett M. Rogers, professor and chair of the Department of Communication & Journalism at the University of New Mexico, explains how new ideas spread via communication channels over time. Such innovations are initially perceived as uncertain and even risky. To overcome this uncertainty, most people seek out others like themselves who have already adopted the new idea. Thus the diffusion process consists of a few individuals who first adopt an innovation, then spread the word among their circle of acquaintances--a process which typically takes months or years. But there are exceptions: use of the Internet in the 1990s, for example, may have spread more rapidly than any other innovation in the history of humankind. Furthermore, the Internet is changing the very nature of diffusion by decreasing the importance of physical distance between people. The fifth edition addresses the spread of the Internet, and how it has transformed the way human beings communicate and adopt new ideas.

    It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.”Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

    Louis glassblowing artist and recovering computer scientist named Jim McKelvey lost a sale because he couldn't accept American Express cards. Frustrated by the high costs and difficulty of accepting credit card payments, McKelvey joined his friend Jack Dorsey (the cofounder of Twitter) to launch Square, a startup that would enable small merchants to accept credit card payments on their mobile phones. With no expertise or experience in the world of payments, they approached the problem of credit cards with a new perspective, questioning the industry's assumptions, experimenting and innovating their way through early challenges, and achieving widespread adoption from merchants small and large.But just as Square was taking off, Amazon launched a similar product, marketed it aggressively, and undercut Square on price. For most ordinary startups, this would have spelled the end. Instead, less than a year later, Amazon was in retreat and soon discontinued its service. How did Square beat the most dangerous company on the planet? Was it just luck? These questions motivated McKelvey to study what Square had done differently from all the other companies Amazon had killed. He eventually found the key: a strategy he calls the Innovation Stack.McKelvey's fascinating and humorous stories of Square's early days are blended with historical examples of other world-changing companies built on the Innovation Stack to reveal a pattern of ground-breaking, competition-proof entrepreneurship that is rare but repeatable.The Innovation Stack is a thrilling business narrative that's much bigger than the story of Square. It is an irreverent first-person look inside the world of entrepreneurship, and a call to action for all of us to find the entrepreneur within ourselves and identify and fix unsolved problems--one crazy idea at a time.

    Bill Bryson’s A Walk in the Woods is one example. Richard Feynman’s “Surely You’re Joking, Mr. Feynman!” is another. Now comes Youngme Moon’s Different, a book for “people who don’t read business books.” Actually, it’s more like a personal conversation with a friend who has thought deeply about how the world works … and who gets you to see that world in a completely new light.  If there is one strain of conventional wisdom pervading every company in every industry, it’s the absolute importance of “competing like crazy.” Youngme Moon’s message is simply “Get off this treadmill that’s taking you nowhere. Going tit for tat and adding features, augmentations, and gimmicks to beat the competition has the perverse result of making you like everyone else.” Different provides a highly original perspective on what it means to offer something that is meaningfully different—different in a manner that is both fundamental and comprehensive.  Youngme Moon identifies the outliers, the mavericks, the iconoclasts—the players who have thoughtfully rejected orthodoxy in favor of an approach that is more adventurous. Some are even “hostile,” almost daring you to buy what they are selling. The MINI Cooper was launched with fearless abandon: “Worried that this car is too small? Look here. It’s even smaller than you think.”  These are players that strike a genuine chord with even the most jaded consumers. In fact, almost every success story of the past two decades has been an exception to the rule. Simply go to your computer and compare AOL and Yahoo! with Google. The former pile on feature upon feature to their home pages, while Google is like an austere boutique, dominating a category filled with “extras.” Different shows how to succeed in a world where conformity reigns…but exceptions rule.

    Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

    Edited by the Sustainability Institute’s Diana Wright, this essential primer brings systems thinking out of the realm of computers and equations and into the tangible world, showing readers how to develop the systems-thinking skills that thought leaders across the globe consider critical for 21st-century life.Some of the biggest problems facing the world—war, hunger, poverty, and environmental degradation—are essentially system failures. They cannot be solved by fixing one piece in isolation from the others, because even seemingly minor details have enormous power to undermine the best efforts of too-narrow thinking.While readers will learn the conceptual tools and methods of systems thinking, the heart of the book is grander than methodology. Donella Meadows was known as much for nurturing positive outcomes as she was for delving into the science behind global dilemmas. She reminds readers to pay attention to what is important, not just what is quantifiable, to stay humble, and to stay a learner.In a world growing ever more complicated, crowded, and interdependent, Thinking in Systems helps readers avoid confusion and helplessness, the first step toward finding proactive and effective solutions.

    But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In The Model Thinker, social scientist Scott E. Page shows us the mathematical, statistical, and computational models—from linear regression to random walks and far beyond—that can turn anyone into a genius. At the core of the book is Page's "many-model paradigm," which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs. The Model Thinker provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    Everyone who wants to make the world a better place becomes possessed by a grand idea.But what does it take to turn your idea into action?  Whether you are an entrepreneur, intrapreneur, or not-for-profit crusader, there’s no shortage of advice available on issues such as writing a business plan, recruiting, raising capital, and branding. In fact, there are so many books, articles, and Web sites that many startups get bogged down to the point of paralysis. Or else they focus on the wrong priorities and go broke before they discover their mistakes. In The Art of the Start, Guy Kawasaki brings two decades of experience as one of business’s most original and irreverent strategists to offer the essential guide for anyone starting anything, from a multinational corporation to a church group. At Apple in the 1980s, he helped lead one of the great companies of the century, turning ordinary consumers into evangelists. As founder and CEO of Garage Technology Ventures, a venture capital firm, he has field-tested his ideas with dozens of newly hatched companies. And as the author of bestselling business books and articles, he has advised thousands of people who are making their startup dreams real. From raising money to hiring the right people, from defining your positioning to creating a brand, from creating buzz to buzzing the competition, from managing a board to fostering a community, this book will guide you through an adventure that’s more art than science—the art of the start.

    Today’s "machine-learning" systems, trained by data, are so effective that we’ve invited them to see and hear for us?and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem.Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole?and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands.The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software.In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story.The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.

    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!

    How has Google done it? Veteran technology reporter Steven Levy was granted unprecedented access to the company, and in this revelatory book he takes readers inside Google headquarters—the Googleplex—to show how Google works.While they were still students at Stanford, Google cofounders Larry Page and Sergey Brin revolutionized Internet search. They followed this brilliant innovation with another, as two of Google’s earliest employees found a way to do what no one else had: make billions of dollars from Internet advertising. With this cash cow, Google was able to expand dramatically and take on other transformative projects: more efficient data centers, open-source cell phones, free Internet video (YouTube), cloud computing, digitizing books, and much more.The key to Google’s success in all these businesses, Levy reveals, is its engineering mind-set and adoption of such Internet values as speed, openness, experimentation, and risk taking. After its unapologetically elitist approach to hiring, Google pampers its engineers—free food and dry cleaning, on-site doctors and masseuses—and gives them all the resources they need to succeed. Even today, with a workforce of more than 23,000, Larry Page signs off on every hire.But has Google lost its innovative edge? With its newest initiative, social networking, Google is chasing a successful competitor for the first time. Some employees are leaving the company for smaller, nimbler start-ups. Can the company that famously decided not to be evil still compete?No other book has ever turned Google inside out as Levy does with In the Plex.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    Then, in Bold, they chronicled the use of exponential technologies that allowed the emergence of powerful new entrepreneurs. Now the bestselling authors are back with The Future Is Faster Than You Think, a blueprint for how our world will change in response to the next ten years of rapid technological disruption. Technology is accelerating far more quickly than anyone could have imagined. During the next decade, we will experience more upheaval and create more wealth than we have in the past hundred years. In this gripping and insightful roadmap to our near future, Diamandis and Kotler investigate how wave after wave of exponentially accelerating technologies will impact both our daily lives and society as a whole. What happens as AI, robotics, virtual reality, digital biology, and sensors crash into 3D printing, blockchain, and global gigabit networks? How will these convergences transform today’s legacy industries? What will happen to the way we raise our kids, govern our nations, and care for our planet? Diamandis, a space-entrepreneur-turned-innovation-pioneer, and Kotler, bestselling author and peak performance expert, probe the science of technological convergence and how it will reinvent every part of our lives—transportation, retail, advertising, education, health, entertainment, food, and finance—taking humanity into uncharted territories and reimagining the world as we know it. As indispensable as it is gripping, The Future Is Faster Than You Think provides a prescient look at our impending future.