The authors use multiple sources, including biographies and dialogues, to increase student interest and involvement with the material, and encourage students to regard becoming a teacher a positive challenge.

    Portraying indigenous researchers as knowledge seekers who work to progress indigenous ways of being, knowing, and doing in a constantly evolving context, this examination shows how relationships both shape indigenous reality and are vital to reality itself. These same knowledge seekers develop relationships with ideas in order to achieve enlightenment in the ceremony of maintaining accountability. Envisioning researchers as accountable to all relations, this overview proves that careful choices should be made regarding selection of topics, methods of data collection, forms of analysis, and the way in which information is presented.

    Aimed to aid student writers through practical, logistical, and emotional stages of writing dissertations and theses, this book offers guidance to students through such important steps as defining topics, scheduling time to accommodate projects, and conducting, analyzing, writing, presenting, and publishing research.

    A full bibliography is included.

    Beginning with an introduction to array formulas, this manual examines topics such as how they differ from ordinary formulas, the benefits and drawbacks of their use, functions that can and cannot handle array calculations, and array constants and functions. Among the practical applications surveyed include how to extract data from tables and unique lists, how to get results that match any criteria, and how to utilize various methods for unique counts. This book contains 529 screen shots.

    Wink (California State University) describes an approach to pedagogy that requires teachers to name experiences in the classroom, reflect critically on their origins and implications, and act in response.

    Each self-contained chapter consists of three sections. The first describes the statistic, including how it is used and what information it provides. The second section reviews how it works, how to calculate the formula, the strengths and weaknesses of the technique, and the conditions needed for its use. The final section provides examples that use and interpret the statistic. A glossary of terms and symbols is also included.New features in the second edition include:an interactive CD with PowerPoint presentations and problems for each chapter including an overview of the problem's solution; new chapters on basic research concepts including sampling, definitions of different types of variables, and basic research designs and one on nonparametric statistics; more graphs and more precise descriptions of each statistic; and a discussion of confidence intervals.This brief paperback is an ideal supplement for statistics, research methods, courses that use statistics, or as a reference tool to refresh one's memory about key concepts. The actual research examples are from psychology, education, and other social and behavioral sciences.Materials formerly available with this book on CD-ROM are now available for download from our website www.psypress.com. Go to the book's page and look for the 'Download' link in the right-hand column.

    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.

    Bonilla-Silva documented how beneath the rhetorical maze of contemporary racial discourse lies a full-blown arsenal of arguments, phrases, and stories that whites use to account for and ultimately justify racial inequities.In the new edition Bonilla-Silva has added a chapter dealing with the future of racial stratification in America that goes beyond the white / black dichotomy. He argues that the U.S. is developing a more complex and apparently "plural" racial order that will mimic Latin American patterns of racial stratification. Another new chapter addresses a variety of questions from readers of the first edition. And he has updated the book throughout with new information, data, and references where appropriate. The book ends with a new Postscript, "What is to be Done (For Real?)". As in the highly acclaimed first edition, Bonilla-Silva continues to challenge color-blind thinking.

    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

    Fully updated and revised to deal with recent changes to the GMAT, they were designed with a content-based approach.The Critical Reasoning Guide demystifies critical reasoning by teaching a clear, consistent, and effective approach to understanding an argument’s logic and choosing the best answer to the given question. Unlike other guides that attempt to convey everything in a single tome, the Critical Reasoning Strategy Guide is designed to provide deep, focused coverage of one specialized area tested on the GMAT. As a result, students benefit from thorough and comprehensive subject material, clear explanations of fundamental principles, and step-by-step instructions of important techniques. In-action practice problems and detailed answer explanations challenge the student, while topical sets of Official Guide problems provide the opportunity for further growth. Used by itself or with other Manhattan GMAT Strategy Guides, the Critical Reasoning Guide will help students develop all the knowledge, skills, and strategic thinking necessary for success on the GMAT. Purchase of this book includes one year of access to Manhattan GMAT’s online computer-adaptive practice exams and Critical Reasoning Question Bank. All of Manhattan Prep’s GMAT Strategy Guides are aligned with both the 2015 Edition and 13th Edition GMAC Official Guide.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    He was a below-average student with a lackluster work ethic and a bad attitude, who hung with the wrong crowd and made a lot of bad choices. He was a kid whose life was headed for disaster—until a local coach took interest in him, suggested that he take up amateur wrestling, and offered to work with him if he promised to stay out of trouble.It was in North Dakota that Bob Backlund had the first of several chance encounters that would shape his destiny. While working out at the YMCA gymnasium in Fargo, North Dakota, where he wrestled for North Dakota State, Backlund met a well-known professional wrestler, “Superstar” Billy Graham. The men talked, and at Graham’s suggestion, Backlund was inspired to pursue a career in professional wrestling.Less than five years from that day, on February 20, 1978, Backlund would find himself halfway across the country, standing in the middle of the ring at Madison Square Garden with his hand raised in victory as the newly crowned World Wide Wrestling Federation Heavyweight Champion. The man Backlund pinned for the championship that night was none other than Superstar Billy Graham.Featuring contributions from Bruno Sammartino, Harley Race, Terry Funk, Pat Patterson, Ken Patera, Sergeant Slaughter, The Magnificent Muraco, George “The Animal” Steele, “Mr. USA” Tony Atlas, The Iron Sheik, and many others, this book tells the incredible story of the life and nearly forty-year career of one of the most famous men to ever grace the squared circle.Skyhorse Publishing, as well as our Sports Publishing imprint, is proud to publish a broad range of books for readers interested in sports—books about baseball, pro football, college football, pro and college basketball, hockey, or soccer, we have a book about your sport or your team.In addition to books on popular team sports, we also publish books for a wide variety of athletes and sports enthusiasts, including books on running, cycling, horseback riding, swimming, tennis, martial arts, golf, camping, hiking, aviation, boating, and so much more. While not every title we publish becomes a New York Times bestseller or a national bestseller, we are committed to publishing books on subjects that are sometimes overlooked by other publishers and to authors whose work might not otherwise find a home.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases