Wilson has distilled sixty years of teaching into a book for students, young and old. Reflecting on his coming-of-age in the South as a Boy Scout and a lover of ants and butterflies, Wilson threads these twenty-one letters, each richly illustrated, with autobiographical anecdotes that illuminate his career--both his successes and his failures--and his motivations for becoming a biologist. At a time in human history when our survival is more than ever linked to our understanding of science, Wilson insists that success in the sciences does not depend on mathematical skill, but rather a passion for finding a problem and solving it. From the collapse of stars to the exploration of rain forests and the oceans' depths, Wilson instills a love of the innate creativity of science and a respect for the human being's modest place in the planet's ecosystem in his readers.

    This text, organized with a traditional functional-group approach, applies the most modern teaching and pedagogical techniques to the study of organic chemistry. In a highly accessible fashion, this top-selling text bridges the gap between conceptual understanding and actual application - while strongly emphasizing the development of problem-solving skills. Additionally, it provides up-to-date aspects of spectroscopy, relevant photographs, and many applications to polymer chemistry integrated throughout the text.

    He draws on interviews with some of the world’s highest performing researchers, the literature and his own experience to identify a small number of important insights that can transform how researchers work. The book is based on an unparalleled breadth of interdisciplinary evidence that speaks directly to researchers of all disciplines and career stages. The lessons in this book will make you more productive, more satisfied with what you produce, and enable you to be happy working less, and being more. The hardback edition has the title and design imprinted on a fabric cover, hand crafted by a book maker in Yorkshire. It contains spectacular colour photography throughout. Chapters are accompanied by close-up images of trees that build up to the forest metaphor that concludes the book. These are bookended by wide perspective canopy images that accompany the front matter (from which the cover design is derived) and concluding chapter. The overall effect is a touch and feel that makes this a book to savour. Mark Reed is Professor of Socio-Technical Innovation at Newcastle University and Visiting Professor at Birmingham City University and the University of Leeds. He has over 140 publications that have been cited more than 10,000 times. He is author of The Research Impact Handbook, which he has used to train over 4000 researchers from more than 200 institutions in 55 countries.

    Written in uncommonly engaging, lucid, and elegant prose, this text is an understand-it-yourself, do-it-yourself manual designed to help research students in any discipline understand the fundamental structure of quality research and the methodical process that leads to genuinely significant results. It guides the reader, step-by-step, from the selection of a problem to study, through the process of conducting authentic research, to the preparation of a completed report, with practical suggestions based on a solid theoretical framework and sound pedagogy. Suited for the core text in any introductory research course or even for self-instruction, this text will show students two things: 1) that quality research demands planning and design; and, 2) how their own research projects can be executed effectively and professionally.

    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.

    If it takes a book to get it across, I hope this book will do it. It ought to.”—Thomas Schelling, Distinguished University Professor, School of Public Policy, University of Maryland, and 2005 Nobel Prize Laureate in Economics “With humor, insight, piercing logic and a nod to history, Ziliak and McCloskey show how economists—and other scientists—suffer from a mass delusion about statistical analysis. The quest for statistical significance that pervades science today is a deeply flawed substitute for thoughtful analysis. . . . Yet few participants in the scientific bureaucracy have been willing to admit what Ziliak and McCloskey make clear: the emperor has no clothes.”—Kenneth Rothman, Professor of Epidemiology, Boston University School of Health The Cult of Statistical Significance shows, field by field, how “statistical significance,” a technique that dominates many sciences, has been a huge mistake. The authors find that researchers in a broad spectrum of fields, from agronomy to zoology, employ “testing” that doesn’t test and “estimating” that doesn’t estimate. The facts will startle the outside reader: how could a group of brilliant scientists wander so far from scientific magnitudes? This study will encourage scientists who want to know how to get the statistical sciences back on track and fulfill their quantitative promise. The book shows for the first time how wide the disaster is, and how bad for science, and it traces the problem to its historical, sociological, and philosophical roots. Stephen T. Ziliak is the author or editor of many articles and two books. He currently lives in Chicago, where he is Professor of Economics at Roosevelt University. Deirdre N. McCloskey, Distinguished Professor of Economics, History, English, and Communication at the University of Illinois at Chicago, is the author of twenty books and three hundred scholarly articles. She has held Guggenheim and National Humanities Fellowships. She is best known for How to Be Human* Though an Economist (University of Michigan Press, 2000) and her most recent book, The Bourgeois Virtues: Ethics for an Age of Commerce (2006).

    Intuitive Biostatistics covers all the topics typically found in an introductory statistics text, but with the emphasis on confidence intervals rather than P values, making it easier for students to understand both. Additionally, it introduces a broad range of topics left out of most other introductory texts but used frequently in biomedical publications, including survival curves. multiple comparisons, sensitivity and specificity of lab tests, Bayesian thinking, lod scores, and logistic, proportional hazards and nonlinear regression. By emphasizing interpretation rather than calculation, this text provides a clear and virtually painless introduction to statistical principles for those students who will need to use statistics constantly in their work. In addition, its practical approach enables readers to understand the statistical results published in biological and medical journals.

    Based on the author’s 27 years of teaching experience, Genetics: A Conceptual Approach, Third Edition, builds upon features that have made past editions so successful:  an engaging writing style; relevant applications; an accessible and instructive art program; an emphasis on problem-solving; and, most importantly, a strong focus on concepts and connections.  To bring these key concepts into sharper focus, Ben Pierce has selectively reduced the amount of detail and has streamlined coverage to focus on essential concepts.

    This book contains student-friendly Study Tips, Clinical View boxes, and progressive question sets to motivate students to internalize and apply what they've learned.

    Sipser's candid, crystal-clear style allows students at every level to understand and enjoy this field. His innovative "proof idea" sections explain profound concepts in plain English. The new edition incorporates many improvements students and professors have suggested over the years, and offers updated, classroom-tested problem sets at the end of each chapter.

    All test questions are answered and explained. It also provides extensive subject review covering all test topics. Topics reviewed include research methods, the biological basis of behavior, sensation and perception, states of consciousness, learning, cognition, personality, abnormal psychology, and treatment of disorders. This manual also presents an overview of the test, extra multiple-choice practice questions, test-taking tips, and an analysis of the test’s essay question with a sample essay.

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    Increasingly, the decisions that affect our lives--where we go to school, whether we can get a job or a loan, how much we pay for health insurance--are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules.But as mathematician and data scientist Cathy O'Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination--propping up the lucky, punishing the downtrodden, and undermining our democracy in the process.

    There are equally many advanced textbooks which delve into the far reaches of statistical theory, while bypassing practical applications. But between these two approaches is an unfilled gap, in which theory and practice merge at an intermediate level. Professor M. G. Bulmer's Principles of Statistics, originally published in 1965, was created to fill that need. The new, corrected Dover edition of Principles of Statistics makes this invaluable mid-level text available once again for the classroom or for self-study.Principles of Statistics was created primarily for the student of natural sciences, the social scientist, the undergraduate mathematics student, or anyone familiar with the basics of mathematical language. It assumes no previous knowledge of statistics or probability; nor is extensive mathematical knowledge necessary beyond a familiarity with the fundamentals of differential and integral calculus. (The calculus is used primarily for ease of notation; skill in the techniques of integration is not necessary in order to understand the text.)Professor Bulmer devotes the first chapters to a concise, admirably clear description of basic terminology and fundamental statistical theory: abstract concepts of probability and their applications in dice games, Mendelian heredity, etc.; definitions and examples of discrete and continuous random variables; multivariate distributions and the descriptive tools used to delineate them; expected values; etc. The book then moves quickly to more advanced levels, as Professor Bulmer describes important distributions (binomial, Poisson, exponential, normal, etc.), tests of significance, statistical inference, point estimation, regression, and correlation. Dozens of exercises and problems appear at the end of various chapters, with answers provided at the back of the book. Also included are a number of statistical tables and selected references.