They were preparing to compete in a poker tournament with million-dollar stakes, but those numbers meant nothing to them.  They were accustomed to risking billions.     At the card table that night was Peter Muller, an eccentric, whip-smart whiz kid who’d studied theoretical mathematics at Princeton and now managed a fabulously successful hedge fund called PDT…when he wasn’t playing his keyboard for morning commuters on the New York subway.  With him was Ken Griffin, who as an undergraduate trading convertible bonds out of his Harvard dorm room had outsmarted the Wall Street pros and made money in one of the worst bear markets of all time.  Now he was the tough-as-nails head of Citadel Investment Group, one of the most powerful money machines on earth. There too were Cliff Asness, the sharp-tongued, mercurial founder of the hedge fund AQR, a man as famous for his computer-smashing rages as for his brilliance, and Boaz Weinstein, chess life-master and king of the credit default swap, who while juggling $30 billion worth of positions for Deutsche Bank found time for frequent visits to Las Vegas with the famed MIT card-counting team.     On that night in 2006, these four men and their cohorts were the new kings of Wall Street.  Muller, Griffin, Asness, and Weinstein were among the best and brightest of a  new breed, the quants.  Over the prior twenty years, this species of math whiz --technocrats who make billions not with gut calls or fundamental analysis but with formulas and high-speed computers-- had usurped the testosterone-fueled, kill-or-be-killed risk-takers who’d long been the alpha males the world’s largest casino.  The quants believed that a dizzying, indecipherable-to-mere-mortals cocktail of differential calculus, quantum physics, and advanced geometry held the key to reaping riches from the financial markets.  And they helped create a digitized money-trading machine that could shift billions around the globe with the click of a mouse.     Few realized that night, though, that in creating this unprecedented machine, men like Muller, Griffin, Asness and Weinstein had sowed the seeds for history’s greatest financial disaster.     Drawing on unprecedented access to these four number-crunching titans, The Quants tells the inside story of what they thought and felt in the days and weeks when they helplessly watched much of their net worth vaporize – and wondered just how their mind-bending formulas and genius-level IQ’s had led them so wrong, so fast.  Had their years of success been dumb luck, fool’s gold, a good run that could come to an end on any given day?  What if The Truth they sought -- the secret of the markets -- wasn’t knowable? Worse, what if there wasn’t any Truth?   In The Quants, Scott Patterson tells the story not just of these men, but of Jim Simons, the reclusive founder of the most successful hedge fund in history; Aaron Brown, the quant who used his math skills to humiliate Wall Street’s old guard at their trademark game of Liar’s Poker, and years later found himself with a front-row seat to the rapid emergence of mortgage-backed securities; and gadflies and dissenters such as Paul Wilmott, Nassim Taleb, and Benoit Mandelbrot.     With the immediacy of today’s NASDAQ close and the timeless power of a Greek tragedy, The Quants is at once a masterpiece of explanatory journalism, a gripping tale of ambition and hubris…and an ominous warning about Wall Street’s future.

    A wealth of accompanying figures and exercises illustrate and develop the material in more depth. They describe what a quantum computer is, how it can be used to solve problems faster than familiar "classical" computers, and the real-world implementation of quantum computers. Their book concludes with an explanation of how quantum states can be used to perform remarkable feats of communication, and of how it is possible to protect quantum states against the effects of noise.

    

    Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

    Yet psychologists and philosophers agree that these commonplace approaches to creating happiness, success, and fulfillment make critical mistakes. Stop Chasing Carrots reveals how these mistakes begin with misconceptions about happiness and then presents a philosophy of life to achieve better results.Here is the rare book that creates a philosophy of life based on scientific evidence. Replacing self-help materialism, Eastern spiritualism/minimalism, and complex but insightful psychological studies with an accessible, balanced, and realistic concept, Stop Chasing Carrots empowers readers to lead lives based on proven ideas.

      Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.

    Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, the new edition remains the most practical, comprehensive handbook of scientific computing available today.

    The Art of Problem Solving, Volume 1, is the classic problem solving textbook used by many successful MATHCOUNTS programs, and have been an important building block for students who, like the authors, performed well enough on the American Mathematics Contest series to qualify for the Math Olympiad Summer Program which trains students for the United States International Math Olympiad team.Volume 1 is appropriate for students just beginning in math contests. MATHCOUNTS and novice high school students particularly have found it invaluable. Although the Art of Problem Solving is widely used by students preparing for mathematics competitions, the book is not just a collection of tricks. The emphasis on learning and understanding methods rather than memorizing formulas enables students to solve large classes of problems beyond those presented in the book.Speaking of problems, the Art of Problem Solving, Volume 1, contains over 500 examples and exercises culled from such contests as MATHCOUNTS, the Mandelbrot Competition, the AMC tests, and ARML. Full solutions (not just answers!) are available for all the problems in the solution manual.

    Hadoop allowed small and medium sized companies to store huge amounts of data on cheap commodity servers in racks. The introduction of Big Data has allowed businesses to make decisions based on quantifiable analysis. Hadoop is now implemented in major organizations such as Amazon, IBM, Cloudera, and Dell to name a few. This book introduces you to Hadoop and to concepts such as ‘MapReduce’, ‘Rack Awareness’, ‘Yarn’ and ‘HDFS Federation’, which will help you get acquainted with the technology.

    A new foreword by Kip Thorne, the current Richard Feynman Professor of Theoretical Physics at Caltech, discusses the relevance of the new edition to today's readers. This boxed set also includes Feynman's new Tips on Physics—the four previously unpublished lectures that Feynman gave to students preparing for exams at the end of his course. Thus, this 4-volume set is the complete and definitive edition of The Feynman Lectures on Physics. Packaged in a specially designed slipcase, this 4-volume set provides the ultimate legacy of Feynman's extraordinary contribution to students, teachers, researches, and lay readers around the world.

    Over years of groundbreaking experiments, he has distilled remarkable discoveries significant enough to become the bedrock of a whole new scientific field: social physics. Humans have more in common with bees than we like to admit: We’re social creatures first and foremost. Our most important habits of action—and most basic notions of common sense—are wired into us through our coordination in social groups. Social physics is about idea flow, the way human social networks spread ideas and transform those ideas into behaviors. Thanks to the millions of digital bread crumbs people leave behind via smartphones, GPS devices, and the Internet, the amount of new information we have about human activity is truly profound. Until now, sociologists have depended on limited data sets and surveys that tell us how people say they think and behave, rather than what they actually do. As a result, we’ve been stuck with the same stale social structures—classes, markets—and a focus on individual actors, data snapshots, and steady states. Pentland shows that, in fact, humans respond much more powerfully to social incentives that involve rewarding others and strengthening the ties that bind than incentives that involve only their own economic self-interest. Pentland and his teams have found that they can study patterns of information exchange in a social network without any knowledge of the actual content of the information and predict with stunning accuracy how productive and effective that network is, whether it’s a business or an entire city. We can maximize a group’s collective intelligence to improve performance and use social incentives to create new organizations and guide them through disruptive change in a way that maximizes the good. At every level of interaction, from small groups to large cities, social networks can be tuned to increase exploration and engagement, thus vastly improving idea flow.  Social Physics will change the way we think about how we learn and how our social groups work—and can be made to work better, at every level of society. Pentland leads readers to the edge of the most important revolution in the study of social behavior in a generation, an entirely new way to look at life itself.

    This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results.The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.-- "Journal of Economics"

    The R language is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets.