Unlike so many education 'reforms, ' this is not small-bore stuff. For that reason alone, it's likely to be resisted by defenders of the status quo, even though it's necessary and right for our kids. We owe it to them to make sure this book isn't merely a terrific read; it must become a blueprint for educational transformation." --Joel Klein, Chancellor of the New York City Department of Education"A brilliant teacher, Christensen brings clarity to a muddled and chaotic world of education."--Jim Collins, bestselling author of "Good to Great"According to recent studies in neuroscience, the way we learn doesn't always match up with the way we are taught. If we hope to stay competitive-academically, economically, and technologically-we need to rethink our understanding of intelligence, reevaluate our educational system, and reinvigorate our commitment to learning. In other words, we need "disruptive innovation."Now, in his long-awaited new book, Clayton M. Christensen and coauthors Michael B. Horn and Curtis W. Johnson take one of the most important issues of our time-education-and apply Christensen's now-famous theories of "disruptive" change using a wide range of real-life examples. Whether you're a school administrator, government official, business leader, parent, teacher, or entrepreneur, you'll discover surprising new ideas, outside-the-box strategies, and straight-A success stories.You'll learn how Customized learning will help many more students succeed in school Student-centric classrooms will increase the demand for new technology Computers must be disruptively deployed to every student Disruptive innovation can circumvent roadblocks that have prevented other attempts at school reform We can compete in the global classroom-and get ahead in the global marketFilled with fascinating case studies, scientific findings, and unprecedented insights on how innovation must be managed, "Disrupting Class" will open your eyes to new possibilities, unlock hidden potential, and get you to think differently. Professor Christensen and his coauthors provide a bold new lesson in innovation that will help you make the grade for years to come.The future is now. Class is in session.

    In 1967, they said the USSR would have one of the fastest-growing economies in the year 2000; in 2000, the USSR did not exist. In 1911, it was pronounced that there would be no more wars in Europe; we all know how that turned out. Face it, experts are about as accurate as dart-throwing monkeys. And yet every day we ask them to predict the future — everything from the weather to the likelihood of a catastrophic terrorist attack. Future Babble is the first book to examine this phenomenon, showing why our brains yearn for certainty about the future, why we are attracted to those who predict it confidently, and why it’s so easy for us to ignore the trail of outrageously wrong forecasts.In this fast-paced, example-packed, sometimes darkly hilarious book, journalist Dan Gardner shows how seminal research by UC Berkeley professor Philip Tetlock proved that pundits who are more famous are less accurate — and the average expert is no more accurate than a flipped coin. Gardner also draws on current research in cognitive psychology, political science, and behavioral economics to discover something quite reassuring: The future is always uncertain, but the end is not always near.

    Hardcover published by Quadragle/The New York Times Book Co., third printing, August 1977, copyright 1975.

    While the mathematics is there, the effort is not all concentrated on proofs. Strang's emphasis is on understanding. He explains concepts, rather than deduces. This book is written in an informal and personal style and teaches real mathematics. The gears change in Chapter 2 as students reach the introduction of vector spaces. Throughout the book, the theory is motivated and reinforced by genuine applications, allowing pure mathematicians to teach applied mathematics.

    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.

    'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

    Essentially, you make an initial guess, and then get more data to improve it. Bayes Theorem, or Bayes Rule, has a ton of real world applications, from estimating your risk of a heart attack to making recommendations on Netflix But It Isn't That Complicated This book is a short introduction to Bayes Theorem. It is only 15 pages long, and is intended to show you how Bayes Theorem works as quickly as possible. The examples are intentionally kept simple to focus solely on Bayes Theorem without requiring that the reader know complicated probability distributions. If you want to learn the basics of Bayes Theorem as quickly as possible, with some easy to duplicate examples, this is a good book for you.

    The novel approach taken here banishes determinants to the end of the book and focuses on the central goal of linear algebra: understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space (or an odd-dimensional real vector space) has an eigenvalue. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition includes a new section on orthogonal projections and minimization problems. The sections on self-adjoint operators, normal operators, and the spectral theorem have been rewritten. New examples and new exercises have been added, several proofs have been simplified, and hundreds of minor improvements have been made throughout the text.

    Reveals how to run an online business profitably in spite of the Internet's inherently free culture, disseminating the principles of a ''priceless economy'' in six categories that pertain to advertising, labor exchange, and advanced-version fees.

    . . a most valuable contribution." — Douglas R. Hofstadter, author of Gödel, Escher, BachThe foundations of game theory were laid by John von Neumann, who in 1928 proved the basic minimax theorem, and with the 1944 publication of the Theory of Games and Economic Behavior, the field was established. Since then, game theory has become an enormously important discipline because of its novel mathematical properties and its many applications to social, economic, and political problems.Game theory has been used to make investment decisions, pick jurors, commit tanks to battle, allocate business expenses equitably — even to measure a senator's power, among many other uses. In this revised edition of his highly regarded work, Morton Davis begins with an overview of game theory, then discusses the two-person zero-sum game with equilibrium points; the general, two-person zero-sum game; utility theory; the two-person, non-zero-sum game; and the n-person game.A number of problems are posed at the start of each chapter and readers are given a chance to solve them before moving on. (Unlike most mathematical problems, many problems in game theory are easily understood by the lay reader.) At the end of the chapter, where solutions are discussed, readers can compare their "common sense" solutions with those of the author. Brimming with applications to an enormous variety of everyday situations, this book offers readers a fascinating, accessible introduction to one of the most fruitful and interesting intellectual systems of our time.

    But by knowing what they are and how to spot them, we can avoid them and make better choices-whether dealing with a personal problem or a business negotiation; trying to save money or make money; working out what we do or don't want in life: and how best to get it.Simple, clear and always surprising, this indispensable book will change the way you think and transform your decision-making-work, at home, every day. It reveals, in 99 short chapters, the most common errors of judgment, and how to avoid them.

    It givessyntax and output for accomplishing many analyses through the mostrecent releases of SAS, SPSS, and SYSTAT, some not available insoftware manuals. The book maintains its practical approach, stillfocusing on the benefits and limitations of applications of a techniqueto a data set -- when, why, and how to do it. Overall, it providesadvanced students with a timely and comprehensive introduction totoday's most commonly encountered statistical and multivariatetechniques, while assuming only a limited knowledge of higher-levelmathematics.

    The four principles that can help us to overcome our brains' natural biases to make better, more informed decisions -- in our lives, careers, families and organizations.In Decisive, Chip Heath and Dan Heath, the bestselling authors of Made to Stick and Switch, tackle the thorny problem of how to overcome our natural biases and irrational thinking to make better decisions, about our work, lives, companies and careers.    When it comes to decision making, our brains are flawed instruments.  But given that we are biologically hard-wired to act foolishly and behave irrationally at times, how can we do better?  A number of recent bestsellers have identified how irrational our decision making can be.  But being aware of a bias doesn't correct it, just as knowing that you are nearsighted doesn't help you to see better.  In Decisive, the Heath brothers, drawing on extensive studies, stories and research, offer specific, practical tools that can help us to think more clearly about our options, and get out of our heads, to improve our decision making, at work and at home.

    Widely praised and much-discussed, this classic book explores how cooperation can emerge in a world of self-seeking egoists—whether superpowers, businesses, or individuals—when there is no central authority to police their actions. The problem of cooperation is central to many different fields. Robert Axelrod recounts the famous computer tournaments in which the “cooperative” program Tit for Tat recorded its stunning victories, explains its application to a broad spectrum of subjects, and suggests how readers can both apply cooperative principles to their own lives and teach cooperative principles to others.

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples