Engaging and accessible, Life on the Tenure Track will delight and enlighten faculty, graduate students, and administrators alike.

    Several of the data display strategies from previous editions are now presented in re-envisioned and reorganized formats to enhance reader accessibility and comprehension. The Third Edition's presentation of the fundamentals of research design and data management is followed by five distinct methods of analysis: exploring, describing, ordering, explaining, and predicting. Miles and Huberman′s original research studies are profiled and accompanied with new examples from Salda�a′s recent qualitative work. The book′s most celebrated chapter, Drawing and Verifying Conclusions, is retained and revised, and the chapter on report writing has been greatly expanded, and is now called Writing About Qualitative Research. Comprehensive and authoritative, Qualitative Data Analysis has been elegantly revised for a new generation of qualitative researchers.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    Lorraine Monroe founded the Frederick Douglass Academy, a public school in Harlem, in the belief that caring instructors, a disciplined but creative environment, and a refusal to accept mediocrity could transform the lives of inner-city kids. Her experiment was a huge success. Today the Academy is one of the finest schools in the country, sending graduates to Ivy League colleges and registering the third highest SAT scores in New York City. The key to its success: a unique leadership method Monroe calls the "Monroe Doctrine," which she developed through decades as a teacher and principal in some of America's toughest schools. In this book Monroe tells her own remarkable story and explains her "Doctrine" through pithy, memorable rules and observations and a host of wonderful true stories. This is an inspiring read for both new and experienced educators—and for anyone who wants to succeed in the face of seemingly impossible odds.

    But linguist Daniel Everett argues that, like other tools, language was invented by humans and can be reinvented or lost. He shows how the evolution of different language forms—that is, different grammar—reflects how language is influenced by human societies and experiences, and how it expresses their great variety.  For example, the Amazonian Pirahã put words together in ways that violate our long-held under-standing of how language works, and Pirahã grammar expresses complex ideas very differently than English grammar does. Drawing on the Wari’ language of Brazil, Everett explains that speakers of all languages, in constructing their stories, omit things that all members of the culture understand. In addition, Everett discusses how some cultures can get by without words for numbers or counting, without verbs for “to say” or “to give,” illustrating how the very nature of what’s important in a language is culturally determined. Combining anthropology, primatology, computer science, philosophy, linguistics, psychology, and his own pioneering—and adventurous—research with the Amazonian Pirahã, and using insights from many different languages and cultures, Everett gives us an unprecedented elucidation of this society-defined nature of language. In doing so, he also gives us a new understanding of how we think and who we are.

    

    A timeless introduction to the field and a landmark in symbolic logic, showing that classical logic can be treated algebraically.

    Robert Claiborne then continues with the Anglo-Saxon invaders of England whose language developed into Old English, which in turn slowly developed into Middle English after the Norman Invasion. He also gives an overview of the various dialects of English and slang.

    No statistical knowledge is required, and your programming skills can range from hobbyist to pro.Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: Create artful graphs to visualize complex data sets and functions Write more efficient code using parallel R and vectorization Interface R with C/C++ and Python for increased speed or functionality Find new R packages for text analysis, image manipulation, and more Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.

    Readers profit from its verbal-conceptual exposition and frequent use of examples.The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying website with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT, at www.psypress.com/9780805822236 .Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.

    Although the occasional newspaper headline alerts us to genocide, AIDS, malaria, or civil war in Africa, most of us know very little about the continent. However we still carry strong mental images of Africa, which are reflected in American advertising, movies, amusement parks, cartoons, and many other corners of our society.Few think to question these perceptions or how they came to be so deeply lodged in the collective American consciousness. Curtis Keim’s Mistaking Africa looks at the historical evolution of this mindset and examines the role that popular media play in the creation of our mental images of Africa. Keim addresses the most prevalent myths and preconceptions and demonstrates how these prevent a true understanding of the enormously diverse peoples and cultures of Africa. Updated throughout, the second edition includes an entirely revised chapter on Africa in images, which analyzes portrayals of Africa in popular media, including print advertising by corporations such as Dow Chemical, ExxonMobil, IBM, Vogue magazine, Honda, and Snapple. New to the second edition as well is an appendix on learning more about Africa.ContentsPart One: Introduction1. Changing Our Mind about Africa2. How We LearnPart Two: Evolutionism3. The Origins of “Darkest Africa”4. “Our Living Ancestors”: Twentieth-Century Evolutionism5. Real Africa, Wise Africa6. We Should Help ThemPart Three: Further Misperceptions7. Cannibalism: No Accounting for Taste8. Africans Live in Tribes, Don’t They?9. Safari: Beyond Our Wildest Dreams10. Africa in ImagesPart Four: New Directions11. Race and Culture: The Same and the Other12. From Imagination to DialogueAppendix: Learning More

    That's what Ben Lindbergh and Sam Miller got to do when the Sonoma Stompers, an independent minor-league team in California, offered them the chance to run the team's baseball operations according to the most advanced statistics. The Only Rule Is It Has to Work is unlike any other baseball tale you've ever read.We tag along as Lindbergh and Miller apply their number-crunching insights to all aspects of assembling and running a team. We meet colorful figures like general manager Theo Fightmaster and boundary-breakers like the first openly gay professional player and the first Japanese manager in American professional baseball. Even José Canseco makes a cameo appearance.Will sabermetrics bring the Stompers a championship, or will they fall on their face? Will the team have a competitive advantage or is the old folk wisdom really true after all? Will the players be able to maximize their talents and attract the attention of big-league scouts, or will this be a fast track to oblivion?It's a wild ride, as the authors' infectious enthusiasm and feel for the absurd make the Stompers' story one that will speak to numbers geeks and traditionalists alike. And it proves that you don't need a bat or a glove to make a genuine contribution to the game.

    This book gives you a solid understanding of statistics without being too simple, yet without the numbing complexity of most college texts. You get a firm grasp of the fundamentals and a hands-on understanding of how to apply them before moving on to the more advanced material that follows. Each chapter presents you with easy-to-follow descriptions illustrated by graphics, formulas, and plenty of solved examples. Before you know it, you'll learn to apply statistical reasoning and statistical techniques, from basic concepts of probability and hypothesis testing to multivariate analysis. Organized into four distinct sections, Statistics in a Nutshell offers you:Introductory material: Different ways to think about statistics Basic concepts of measurement and probability theoryData management for statistical analysis Research design and experimental design How to critique statistics presented by others Basic inferential statistics: Basic concepts of inferential statistics The concept of correlation, when it is and is not an appropriate measure of association Dichotomous and categorical data The distinction between parametric and nonparametric statistics Advanced inferential techniques: The General Linear Model Analysis of Variance (ANOVA) and MANOVA Multiple linear regression Specialized techniques: Business and quality improvement statistics Medical and public health statistics Educational and psychological statistics Unlike many introductory books on the subject, Statistics in a Nutshell doesn't omit important material in an effort to dumb it down. And this book is far more practical than most college texts, which tend to over-emphasize calculation without teaching you when and how to apply different statistical tests. With Statistics in a Nutshell, you learn how to perform most common statistical analyses, and understand statistical techniques presented in research articles. If you need to know how to use a wide range of statistical techniques without getting in over your head, this is the book you want.

    Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business dataUpdates that incorporate input from readers, changes in the field, and more material on statistics and machine learningDozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projectsComplete classroom support for instructors at www.mkp.com/datamining2e companion site