So while the focus is on design, the approach is holistic. Hence, the book speaks to designers, usability specialists, the HCI community, product managers, and business executives. There is an emphasis on balancing the back-end concern with usability and engineering excellence (getting the design right) with an up-front investment in sketching and ideation (getting the right design). Overall, the objective is to build the notion of informed design: molding emerging technology into a form that serves our society and reflects its values.Grounded in both practice and scientific research, Bill Buxton s engaging work aims to spark the imagination while encouraging the use of new techniques, breathing new life into user experience design. Covers sketching and early prototyping design methods suitable for dynamic product capabilities: cell phones that communicate with each other and other embedded systems, "smart" appliances, and things you only imagine in your dreamsThorough coverage of the design sketching method which helps easily build experience prototypes-without the effort of engineering prototypes which are difficult to abandonReaches out to a range of designers, including user interface designers, industrial designers, software engineers, usability engineers, product managers, and othersFull of case studies, examples, exercises, and projects, and access to video clips that demonstrate the principles and methods"

    They explore each metric, considering best methods for collecting, analyzing, and presenting the data. They provide step-by-step guidance for measuring the usability of any type of product using any type of technology.This book is recommended for usability professionals, developers, programmers, information architects, interaction designers, market researchers, and students in an HCI or HFE program.

    It should be labelled 'start here'." --Pieter Jan Stappers, ID-StudioLab, Delft University of Technology

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    Using a downloadable programming environment developed by the author, Visualizing Data demonstrates methods for representing data accurately on the Web and elsewhere, complete with user interaction, animation, and more. How do the 3.1 billion A, C, G and T letters of the human genome compare to those of a chimp or a mouse? What do the paths that millions of visitors take through a web site look like? With Visualizing Data, you learn how to answer complex questions like these with thoroughly interactive displays. We're not talking about cookie-cutter charts and graphs. This book teaches you how to design entire interfaces around large, complex data sets with the help of a powerful new design and prototyping tool called "Processing". Used by many researchers and companies to convey specific data in a clear and understandable manner, the Processing beta is available free. With this tool and Visualizing Data as a guide, you'll learn basic visualization principles, how to choose the right kind of display for your purposes, and how to provide interactive features that will bring users to your site over and over. This book teaches you:The seven stages of visualizing data -- acquire, parse, filter, mine, represent, refine, and interact How all data problems begin with a question and end with a narrative construct that provides a clear answer without extraneous details Several example projects with the code to make them work Positive and negative points of each representation discussed. The focus is on customization so that each one best suits what you want to convey about your data set The book does not provide ready-made "visualizations" that can be plugged into any data set. Instead, with chapters divided by types of data rather than types of display, you'll learn how each visualization conveys the unique properties of the data it represents -- why the data was collected, what's interesting about it, and what stories it can tell. Visualizing Data teaches you how to answer questions, not simply display information.

    Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt.In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: -What type of mortgage risk Chase Bank predicted before the recession. -Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. -Why early retirement decreases life expectancy and vegetarians miss fewer flights. -Five reasons why organizations predict death, including one health insurance company. -How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual. -How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! -How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. -How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free. -What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward — but that can be predicted in advance?Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

    This is a practical, hands-on guide for anyone trying to design systems that reflect the way customers want to do their work. The authors developed Contextual Design, the method discussed here, through their work with teams struggling to design products and internal systems. In this book, you'll find the underlying principles of the method and how to apply them to different problems, constraints, and organizational situations.Contextual Design enables you to+ gather detailed data about how people work and use systems + develop a coherent picture of a whole customer population + generate systems designs from a knowledge of customer work+ diagram a set of existing systems, showing their relationships, inconsistencies, redundancies, and omissions

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    Whether you want to cross over into user experience or you're a seasoned practitioner trying to drag your organization forward, this book gives you tools and insight for doing more with less.

    Five actual journal articles are reproduced in the appendix as examples of the different research designs.

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

    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.

    In Web Form Design, Luke Wroblewski draws on original research, his considerable experience at Yahoo! and eBay, and the perspectives of many of the field's leading designers to show you everything you need to know about designing effective and engaging Web forms.

     It is easy to be overwhelmed by the amount of information we encounter each day. Whether at work, at school, or in our personal endeavors, there’s a deepening (and inescapable) need for people to work with and understand information. Information architecture is the way that we arrange the parts of something to make it understandable as a whole. When we make things for others to use, the architecture of information that we choose greatly affects our ability to deliver our intended message to our users. We all face messes made of information and people. I define the word “mess” the same way that most dictionaries do: “A situation where the interactions between people and information are confusing or full of difficulties.” — Who doesn’t bump up against messes made of information and people every day? This book provides a seven step process for making sense of any mess. Each chapter contains a set of lessons as well as workbook exercises architected to help you to work through your own mess.

    Many of us are also teaching, even when it's not in our job descriptions. Whether it's giving a presentation, writing documentation, or creating a website or blog, we need and want to share our knowledge with other people. But if you've ever fallen asleep over a boring textbook, or fast-forwarded through a tedious e-learning exercise, you know that creating a great learning experience is harder than it seems.In Design For How People Learn, you'll discover how to use the key principles behind learning, memory, and attention to create materials that enable your audience to both gain and retain the knowledge and skills you're sharing. Using accessible visual metaphors and concrete methods and examples, Design For How People Learn will teach you how to leverage the fundamental concepts of instructional design both to improve your own learning and to engage your audience.