This new edition gives expanded attention to the important symbolic roles of school leaders, including practical suggestions on how leaders can balance cultural goals and values against accountability demands, and features new and powerful case examples throughout. Most important, the authors show how school leaders can transform negative and toxic cultures so that trust, commitment, and sense of unity can prevail. Praise for Shaping School Culture "For those seeking enduring change that is measured in generations rather than months, and to create a legacy rather than a headline, then Shaping School Culture is your guide." —Dr. Douglas B. Reeves, founder, The Leadership and Learning Center, Englewood, CO "Deal and Peterson combine exquisite language, vibrant stories, and sage advice to support school leaders in embracing the paradoxical nature of their work. A 'must read' for all school leaders." —Pam Robbins, educational consultant and author "Once again, the authors have presented practitioners, researchers, professional developers, school coaches, and others with a tremendous resource for renovating and reinvigorating schools." —Karen M. Dyer, Ed.D., group director, Education and Nonprofit Sector Office, Center for CreativeLeadership, Greensboro, NC

    Premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid foundation in statistical theory while conveying the relevance and importance of the theory in solving practical problems in the real world. The authors' use of practical applications and excellent exercises helps readers discover the nature of statistics and understand its essential role in scientific research.

    Rossi, Mark W. Lipsey, and Howard E. Freeman first published Evaluation: A Systematic Approach, more than 90,000 readers have considered it the premier text on how to design, implement, and appraise social programs through evaluation. In this, the completely revised Seventh Edition, authors Rossi and Lipsey include the latest techniques and approaches to evaluation as well as guidelines to tailor evaluations to fit programs and social contexts.With decades of hands-on experience conducting evaluations, the authors provide scores of examples to help students understand how evaluators deal with various critical issues. They include a glossary of key terms and concepts, making this the most comprehensive and authoritative evaluation text available.Thoroughly revised, the Seventh Edition now includes* Substantially more attention to outcome measurement* Lengthy discussions of program theory, including a section about detecting program effects and interpreting their practical significance* An augmented and updated discussion of major evaluation designs* A detailed exposition of meta-analysis as an approach to the synthesis of evaluation studies* Alternative approaches to evaluation* Examples of successful evaluations* Discussions of the political and social contexts of evaluation

    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.

    But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options.This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization.Explore the basic concepts of color as a tool to highlight, distinguish, or represent a valueUnderstand the importance of redundant coding to ensure you provide key information in multiple waysUse the book's visualizations directory, a graphical guide to commonly used types of data visualizationsGet extensive examples of good and bad figuresLearn how to use figures in a document or report and how employ them effectively to tell a compelling story

    This text aims to motivate the need for tools with concrete applications, providing simple assumptions that match the application.

    Contains detailed discussions of how to open an interview, how to interpret nonverbal communication, how to make more natural transitions, and how to arrive at accurate diagnoses. Offers special techniques for eliciting information from depressed, psychotic, and personality-disordered patients. This edition presents updated DSM-IV criteria, new strategies in suicide assessment, and an annotated interview section accompanied by sample write-ups with tips in the appendix.Spanish version also available, ISBN: 84-8174-596-0

    The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks.The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks.

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

    Through statistics like these, we feel that we understand our fellow citizens. But remarkably, such data now woven into our social fabric became common currency only in the last century. Sarah Igo tells the story, for the first time, of how opinion polls, man-in-the-street interviews, sex surveys, community studies, and consumer research transformed the United States public.Igo argues that modern surveys, from the Middletown studies to the Gallup Poll and the Kinsey Reports, projected new visions of the nation: authoritative accounts of majorities and minorities, the mainstream and the marginal. They also infiltrated the lives of those who opened their doors to pollsters, or measured their habits and beliefs against statistics culled from strangers. Survey data underwrote categories as abstract as the average American and as intimate as the sexual self.With a bold and sophisticated analysis, Igo demonstrates the power of scientific surveys to shape Americans sense of themselves as individuals, members of communities, and citizens of a nation. Tracing how ordinary people argued about and adapted to a public awash in aggregate data, she reveals how survey techniques and findings became the vocabulary of mass society and essential to understanding who we, as modern Americans, think we are.

    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.

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    This is the currently used textbook for "Probabilistic Systems Analysis," an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains, a number of more advanced topics, from which an instructor can choose to match the goals of a particular course. These topics include transforms, sums of random variables, least squares estimation, the bivariate normal distribution, and a fairly detailed introduction to Bernoulli, Poisson, and Markov processes. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis has been just intuitively explained in the text, but is developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems. The book has been widely adopted for classroom use in introductory probability courses within the USA and abroad.

    In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect.Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems—rather than humans—control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor.In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile.The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values.This deeply researched and passionate book could not be more timely.Naomi Klein: "This book is downright scary."Ethan Zuckerman, MIT: "Should be required reading."Dorothy Roberts, author of Killing the Black Body: "A must-read for everyone concerned about modern tools of inequality in America."Astra Taylor, author of The People's Platform: "This is the single most important book about technology you will read this year."

    This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.