“Big data” refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it. This emerging science can translate myriad phenomena—from the price of airline tickets to the text of millions of books—into searchable form, and uses our increasing computing power to unearth epiphanies that we never could have seen before. A revolution on par with the Internet or perhaps even the printing press, big data will change the way we think about business, health, politics, education, and innovation in the years to come. It also poses fresh threats, from the inevitable end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior.In this brilliantly clear, often surprising work, two leading experts explain what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data is the first big book about the next big thing.www.big-data-book.com

    The text shows you how to use statistical methods to design and develop new products, and new manufacturing systems and processes. You'll gain a better understanding of how these methods are used in everyday work, and get a taste of practical engineering experience through real-world, engineering-based examples and exercises. Now revised, this Fourth Edition of "Applied Statistics and Probability for Engineers" features many new homework exercises, including a greater variation of problems and more computer problems.

    It shows how the components of design interact with each other, and provides a strategy for creating coherent and workable relationships among these design components, highlighting key design issues. Written in an informal, jargon-free style, the new Third Edition incorporates examples and hands-on exercises.

    Cottrell introduces us to Jeff, a successful corporate manager who has hit a major wall. Jeff has been leading his team, quarter after quarter, to great sales and better profits for several years -- until now. The tricks that used to work wonders have lost their magic; Jeff is in a slump and is at a loss to find his way out of it.Overworked, stressed, and feeling that his personal and professional lives are at risk, Jeff reaches out to the father of a college buddy, a retired and tremendously accomplished former executive named Tony. Tony and Jeff agree to meet every Monday for ten weeks to work through Jeff's problems and get his career back on track.In the course of these intimate sessions, Jeff discovers the secrets of real leadership: "Until I accept total responsibility -- no matter what -- I will not be able to put plans in place to accomplish my goals." And, "My success is the result of making better choices and recovering quickly from poor choices."Tony leads Jeff through tough lessons in how to manage his people, how to manage his own time, how to manage his superiors, and how to escape from "management land." Most of all, Jeff learns that his success is intimately bound with the success of his people and that tolerating lackluster performance in himself and others on the team only leads to discontent from his most prized and productive employees.Through Jeff's mentoring sessions, the reader meets a character of integrity who dispenses homespun but effective wisdom. Spend time with Tony and Jeff at their Monday morning meetings, and you will find yourself on the road to becoming a better leader and being more successful at work.

    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.

    Unfortunately, people tend to be terrible forecasters. As Wharton professor Philip Tetlock showed in a landmark 2005 study, even experts’ predictions are only slightly better than chance. However, an important and underreported conclusion of that study was that some experts do have real foresight, and Tetlock has spent the past decade trying to figure out why. What makes some people so good? And can this talent be taught?   In Superforecasting, Tetlock and coauthor Dan Gardner offer a masterwork on prediction, drawing on decades of research and the results of a massive, government-funded forecasting tournament. The Good Judgment Project involves tens of thousands of ordinary people—including a Brooklyn filmmaker, a retired pipe installer, and a former ballroom dancer—who set out to forecast global events. Some of the volunteers have turned out to be astonishingly good. They’ve beaten other benchmarks, competitors, and prediction markets. They’ve even beaten the collective judgment of intelligence analysts with access to classified information. They are "superforecasters."   In this groundbreaking and accessible book, Tetlock and Gardner show us how we can learn from this elite group. Weaving together stories of forecasting successes (the raid on Osama bin Laden’s compound) and failures (the Bay of Pigs) and interviews with a range of high-level decision makers, from David Petraeus to Robert Rubin, they show that good forecasting doesn’t require powerful computers or arcane methods. It involves gathering evidence from a variety of sources, thinking probabilistically, working in teams, keeping score, and being willing to admit error and change course. Superforecasting offers the first demonstrably effective way to improve our ability to predict the future—whether in business, finance, politics, international affairs, or daily life—and is destined to become a modern classic.

    A finished book—or even steady journal articles—may seem like an impossible dream. But, as Joli Jensen proves, it really is possible to write happily and productively in academe. Jensen begins by busting the myth that universities are supportive writing environments.  She points out that academia, an arena dedicated to scholarship, offers pressures that actually prevent scholarly writing. She shows how to acknowledge these less-than-ideal conditions, and how to keep these circumstances from draining writing time and energy. Jensen introduces tools and techniques that encourage frequent, low-stress writing. She points out common ways writers stall and offers workarounds that maintain productivity. Her focus is not on content, but on how to overcome whatever stands in the way of academic writing.Write No Matter What draws on popular and scholarly insights into the writing process and stems from Jensen’s experience designing and directing a faculty writing program. With more than three decades as an academic writer, Jensen knows what really helps and hinders the scholarly writing process for scholars in the humanities, social sciences,and sciences. Cut down the academic sword of Damocles, Jensen advises. Learn how to write often and effectively, without pressure or shame. With her encouragement, writers of all levels will find ways to create the writing support they need and deserve.

    In that short time, many doctors decide on the likely diagnosis and best treatment. Often, decisions made this way are correct, but at crucial moments they can also be wrong -- with catastrophic consequences. In this myth-shattering book, Jerome Groopman pinpoints the forces and thought processes behind the decisions doctors make. Groopman explores why doctors err and shows when and how they can -- with our help -- avoid snap judgments, embrace uncertainty, communicate effectively, and deploy other skills that can profoundly impact our health. This book is the first to describe in detail the warning signs of erroneous medical thinking and reveal how new technologies may actually hinder accurate diagnoses. How Doctors Think offers direct, intelligent questions patients can ask their doctors to help them get back on track.Groopman draws on a wealth of research, extensive interviews with some of the country’s best doctors, and his own experiences as a doctor and as a patient. He has learned many of the lessons in this book the hard way, from his own mistakes and from errors his doctors made in treating his own debilitating medical problems.How Doctors Think reveals a profound new view of twenty-first-century medical practice, giving doctors and patients the vital information they need to make better judgments together.

    Many designers and researchers view usability and design as qualitative activities, which do not require attention to formulas and numbers. However, usability practitioners and user researchers are increasingly expected to quantify the benefits of their efforts. The impact of good and bad designs can be quantified in terms of conversions, completion rates, completion times, perceived satisfaction, recommendations, and sales.The book discusses ways to quantify user research; summarize data and compute margins of error; determine appropriate samples sizes; standardize usability questionnaires; and settle controversies in measurement and statistics. Each chapter concludes with a list of key points and references. Most chapters also include a set of problems and answers that enable readers to test their understanding of the material. This book is a valuable resource for those engaged in measuring the behavior and attitudes of people during their interaction with interfaces.

    These include teachers, social workers and health service professionals, managers and specialists in business, architects, designers, criminologists and accountants among many others.Real World Research provides a clear route-map of the various steps needed to carry out a piece of applied research to a high professional standard. It is accessible to those without a social science background while providing rigorous and fully up-to-date coverage of contemporary issues and debates. It brings together materials and approaches from different social science disciplines, seeing value in both quantitative and qualitative approaches, as well as their combination in mixed-method designs.

    

    The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    It helps in the process of choosing the right statistical technique and includes a detailed guide to interpreting SPSS ouput.

    of Wisconsin-Madison) and Wichern (Texas A&M U.) present the newest edition of this college text on the statistical methods for describing and analyzing multivariate data, designed for students who have taken two or more statistics courses. The fifth edition includes the addition of seve