“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

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    Because of the way the book is organized, it may be used at a variety of levels of rigor. For example, if matrix algebra is used, theoretical exercises may be omitted. A CD of data sets is provided with the text.

    Never again will you order the Poisson Distribution in a French restaurant!This updated version features all new material.

     For decades, technology encouraged people to squander their time and intellect as passive consumers. Today, tech has finally caught up with human potential. In Cognitive Surplus, Internet guru Clay Shirky forecasts the thrilling changes we will all enjoy as new digital technology puts our untapped resources of talent and goodwill to use at last. Since we Americans were suburbanized and educated by the postwar boom, we've had a surfeit of intellect, energy, and time-what Shirky calls a cognitive surplus. But this abundance had little impact on the common good because television consumed the lion's share of it-and we consume TV passively, in isolation from one another. Now, for the first time, people are embracing new media that allow us to pool our efforts at vanishingly low cost. The results of this aggregated effort range from mind expanding-reference tools like Wikipedia-to lifesaving-such as Ushahidi.com, which has allowed Kenyans to sidestep government censorship and report on acts of violence in real time. Shirky argues persuasively that this cognitive surplus-rather than being some strange new departure from normal behavior-actually returns our society to forms of collaboration that were natural to us up through the early twentieth century. He also charts the vast effects that our cognitive surplus- aided by new technologies-will have on twenty-first-century society, and how we can best exploit those effects. Shirky envisions an era of lower creative quality on average but greater innovation, an increase in transparency in all areas of society, and a dramatic rise in productivity that will transform our civilization. The potential impact of cognitive surplus is enormous. As Shirky points out, Wikipedia was built out of roughly 1 percent of the man-hours that Americans spend watching TV every year. Wikipedia and other current products of cognitive surplus are only the iceberg's tip. Shirky shows how society and our daily lives will be improved dramatically as we learn to exploit our goodwill and free time like never before.

    The addition of Angela Eaton of Texas Tech University brings a fresh tone to her updates of content and pedagogy while retaining the authoritative voice of Carolyn Rude. Some of the text's changes include an update to Chapter 6, "Electronic Editing," and examples about editing Web sites are found throughout the text to support the increased role of online resources in every aspect of communication.

    Packed with practical recipes written and tested with Python 3.3, this unique cookbook is for experienced Python programmers who want to focus on modern tools and idioms.Inside, you’ll find complete recipes for more than a dozen topics, covering the core Python language as well as tasks common to a wide variety of application domains. Each recipe contains code samples you can use in your projects right away, along with a discussion about how and why the solution works.Topics include:Data Structures and AlgorithmsStrings and TextNumbers, Dates, and TimesIterators and GeneratorsFiles and I/OData Encoding and ProcessingFunctionsClasses and ObjectsMetaprogrammingModules and PackagesNetwork and Web ProgrammingConcurrencyUtility Scripting and System AdministrationTesting, Debugging, and ExceptionsC Extensions

    In addition to explaining the basic moves, this book provides writing templates that show students explicitly how to make these moves in their own writing.

    But for some reason they choose to ignore those guidelines and churn out turgid, pompous, and obscure prose. Distinguished sociologist Howard S. Becker, true to his calling, looks for an explanation for this bizarre behavior not in the psyches of his colleagues but in the structure of his profession. In this highly personal and inspirational volume he considers academic writing as a social activity.Both the means and the reasons for writing a thesis or article or book are socially structured by the organization of graduate study, the requirements for publication, and the conditions for promotion, and the pressures arising from these situations create the writing style so often lampooned and lamented. Drawing on his thirty-five years' experience as a researcher, writer, and teacher, Becker exposes the foibles of the academic profession to the light of sociological analysis and gentle humor. He also offers eminently useful suggestions for ways to make social scientists better and more productive writers. Among the topics discussed are how to overcome the paralyzing fears of chaos and ridicule that lead to writer's block; how to rewrite and revise, again and again; how to adopt a persona compatible with lucid prose; how to deal with that academic bugaboo, "the literature." There is also a chapter by Pamela Richards on the personal and professional risks involved in scholarly writing.In recounting his own trials and errors Becker offers his readers not a model to be slavishly imitated but an example to inspire. Throughout, his focus is on the elusive work habits that contribute to good writing, not the more easily learned rules of grammar and punctuation. Although his examples are drawn from sociological literature, his conclusions apply to all fields of social science, and indeed to all areas of scholarly endeavor. The message is clear: you don't have to write like a social scientist to be one.

    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.

    Bellow. Strauss. Friedman. The University of Chicago has been the home of some of the most important thinkers of the modern age. But perhaps no name has been spoken with more respect than Turabian. The dissertation secretary at Chicago for decades, Kate Turabian literally wrote the book on the successful completion and submission of the student paper. Her Manual for Writers of Research Papers, Theses, and Dissertations, created from her years of experience with research projects across all fields, has sold more than seven million copies since it was first published in 1937.Now, with this seventh edition, Turabian’s Manual has undergone its most extensive revision, ensuring that it will remain the most valuable handbook for writers at every level—from first-year undergraduates, to dissertation writers apprehensively submitting final manuscripts, to senior scholars who may be old hands at research and writing but less familiar with new media citation styles. Gregory G. Colomb, Joseph M. Williams, and the late Wayne C. Booth—the gifted team behind The Craft of Research—and the University of Chicago Press Editorial Staff combined their wide-ranging expertise to remake this classic resource. They preserve Turabian’s clear and practical advice while fully embracing the new modes of research, writing, and source citation brought about by the age of the Internet.Booth, Colomb, and Williams significantly expand the scope of previous editions by creating a guide, generous in length and tone, to the art of research and writing. Growing out of the authors’ best-selling Craft of Research, this new section provides students with an overview of every step of the research and writing process, from formulating the right questions to reading critically to building arguments and revising drafts. This leads naturally to the second part of the Manual for Writers, which offers an authoritative overview of citation practices in scholarly writing, as well as detailed information on the two main citation styles (“notes-bibliography” and “author-date”). This section has been fully revised to reflect the recommendations of the fifteenth edition of The Chicago Manual of Style and to present an expanded array of source types and updated examples, including guidance on citing electronic sources.The final section of the book treats issues of style—the details that go into making a strong paper. Here writers will find advice on a wide range of topics, including punctuation, table formatting, and use of quotations. The appendix draws together everything writers need to know about formatting research papers, theses, and dissertations and preparing them for submission. This material has been thoroughly vetted by dissertation officials at colleges and universities across the country.This seventh edition of Turabian’s Manual for Writers of Research Papers, Theses, and Dissertations is a classic reference revised for a new age. It is tailored to a new generation of writers using tools its original author could not have imagined—while retaining the clarity and authority that generations of scholars have come to associate with the name Turabian.

    The title says it is the hard way to learn to writecode but it’s actually not. It’s the “hard” way only in that it’s the way people used to teach things. In this book youwill do something incredibly simple that all programmers actually do to learn a language: 1. Go through each exercise. 2. Type in each sample exactly. 3. Make it run.That’s it. This will be very difficult at first, but stick with it. If you go through this book, and do each exercise for1-2 hours a night, then you’ll have a good foundation for moving on to another book. You might not really learn“programming” from this book, but you will learn the foundation skills you need to start learning the language.This book’s job is to teach you the three most basic essential skills that a beginning programmer needs to know:Reading And Writing, Attention To Detail, Spotting Differences.

    This book develops statistical thinking over rote drill and practice. The Nature of Statistics; Organizing Data; Descriptive Measures; Probability Concepts; Discrete Random Variables; The Normal Distribution; The Sampling Distribution of the Sample Menu; Confidence Intervals for One Population Mean; Hypothesis Tests for One Population Mean; Inferences for Two Population Means; Inferences for Population Standard Deviations; Inferences for Population Proportions; Chi-Square Procedures; Descriptive Methods in Regression and Correlation; Inferential Methods in Regression and Correlation; Analysis of Variance (ANOVA) For all readers interested in Introductory Statistics.

    The complexity of nature's shapes differs in kind, not merely degree, from that of the shapes of ordinary geometry, the geometry of fractal shapes.Now that the field has expanded greatly with many active researchers, Mandelbrot presents the definitive overview of the origins of his ideas and their new applications. The Fractal Geometry of Nature is based on his highly acclaimed earlier work, but has much broader and deeper coverage and more extensive illustrations.

    Updated information is included about the associated features, culture, age & gender features, prevalence, course & familial pattern of mental disorders. The DSM-IV-TR(R) brings this essential diagnostic tool to-date, to promote effective diagnosis, treatment & quality of care. One can get all the essential diagnostic information from the DSM-IV(R) along with important updates not in the '94 edition. Benefit from new research into Schizophrenia, Asperger's Disorder & other conditions. Utilize additional information about the epidemiology & other facets of DSM conditions. Update ICD-9-CM codes implemented since 1994 including Conduct Disorder, Dementia, Somatoform Disorders.Use of the manual DSM-IV-TR classification Multiaxial assessment Disorders usually 1st diagnosed in infancy, childhood or adolescenceDelirium, dementia & amnestic & other cognitive disordersMental disorders due to a general medical condition Substance-related disorders Schizophrenia & other psychotic disordersMood disorders Anxiety disordersSomatoform disordersFactitious disordersDissociative disordersSexual & gender identity disordersEating disorders Sleep disorders Impulse-control disorders not elsewhere classifiedAdjustment disordersPersonality disordersOther conditions that may be a focus of clinical attentionAdditional codes Appendix A: Decision trees for differential diagnosis Appendix B: Criteria sets & axes provided for further study Appendix C: Glossary of technical terms Appendix D: Highlights of changes in DSM-IV text revisionAppendix E: Alphabetical listing of DSM-IV-TR diagnoses & codesAppendix F: Numerical listing of DSM-IV-TR diagnoses & codesAppendix G: ICD-9-CM codes for selected general medical conditions & medication-induced disordersAppendix H: DSM-IV classification with ICD-10 codesAppendix I: Outline for cultural formulation & glossary of culture-bound syndromesAppendix J: DSM-IV contributorsAppendix K: DSM-IV text revision advisers