” —George Lipsitz“The text is of great value to anyone working on issues pertaining to the fantastic and the uncanny.”  —American Studies International“Ghostly Matters immediately establishes Avery Gordon as a leader among her generation of social and cultural theorists in all fields. The sheer beauty of her language enhances an intellectual brilliance so daunting that some readers will mark the day they first read this book. One must go back many more years than most of us can remember to find a more important book.” —Charles LemertDrawing on a range of sources, including the fiction of Toni Morrison and Luisa Valenzuela (He Who Searches), Avery Gordon demonstrates that past or haunting social forces control present life in different and more complicated ways than most social analysts presume. Written with a power to match its subject, Ghostly Matters has advanced the way we look at the complex intersections of race, gender, and class as they traverse our lives in sharp relief and shadowy manifestations.Avery F. Gordon is professor of sociology at the University of California, Santa Barbara.Janice Radway is professor of literature at Duke University.

    The text also teaches students how to analyze the data they collect, and how to write an ethnography. The appendices include research questions and writing tasks.

    The implications of this argument are highly significant. If this is the case, methods are always political, and it raises the question of what kinds of social realities we want to create.Most current methods look for clarity and precision. It is usually said that only poor research produces messy findings, and the idea that things in the world might be fluid, elusive, or multiple is unthinkable. Law's startling argument is that this is wrong and it is time for a new approach. Many realities, he says, are vague and ephemeral. If methods want to know and help to shape the world, then they need to reinvent themselves and their politics to deal with mess. That is the challenge. Nothing less will do.

    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

    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

    This is a rare firsthand expression of native religion, without the filters imposed by translators or anthropologists. Rather than a scientific treatise, Eastman has written a book, "as true as I can make it to my childhood teaching and ancestral ideals, but from the human, not the ethnological standpoint." His discussions of the forms of ceremonial and symbolic worship, the unwritten scriptures, and the spirit world emphasize the universal quality and personal appeal of Native American religion.Excerpt:Thus spoke the great Seneca ora tor, Red Jacket, in his superb reply to Missionary Cram more than a century ago, and I have often heard the same thought expressed by my countrymen.

    The rise of DNA testing has further complicated the issues and raised the stakes.In Native American DNA, Kim TallBear shows how DNA testing is a powerful—and problematic—scientific process that is useful in determining close biological relatives. But tribal membership is a legal category that has developed in dependence on certain social understandings and historical contexts, a set of concepts that entangles genetic information in a web of family relations, reservation histories, tribal rules, and government regulations. At a larger level, TallBear asserts, the “markers” that are identified and applied to specific groups such as Native American tribes bear the imprints of the cultural, racial, ethnic, national, and even tribal misinterpretations of the humans who study them.TallBear notes that ideas about racial science, which informed white definitions of tribes in the nineteenth century, are unfortunately being revived in twenty-first-century laboratories. Because today’s science seems so compelling, increasing numbers of Native Americans have begun to believe their own metaphors: “in our blood” is giving way to “in our DNA.” This rhetorical drift, she argues, has significant consequences, and ultimately she shows how Native American claims to land, resources, and sovereignty that have taken generations to ratify may be seriously—and permanently—undermined.

    Defined as an argument about why the topic of a study matters, and why the methods proposed to study it are appropriate and rigorous, the book explores the conceptual framework as both a process and a framework that helps to direct and ground researchers as they work through common research challenges.

    In " An Introduction to the Event-Related Potential Technique," Steve Luck offers the first comprehensive guide to the practicalities of conducting ERP experiments in cognitive neuroscience and related fields, including affective neuroscience and experimental psychopathology. The book can serve as a guide for the classroom or the laboratory and as a reference for researchers who do not conduct ERP studies themselves but need to understand and evaluate ERP experiments in the literature. It summarizes the accumulated body of ERP theory and practice, providing detailed, practical advice about how to design, conduct, and interpret ERP experiments, and presents the theoretical background needed to understand why an experiment is carried out in a particular way. Luck focuses on the most fundamental techniques, describing them as they are used in many of the world's leading ERP laboratories. These techniques reflect a long history of electrophysiological recordings and provide an excellent foundation for more advanced approaches.The book also provides advice on the key topic of how to design ERP experiments so that they will be useful in answering questions of broad scientific interest. This reflects the increasing proportion of ERP research that focuses on these broader questions rather than the "ERPology" of early studies, which concentrated primarily on ERP components and methods. Topics covered include the neural origins of ERPs, signal averaging, artifact rejection and correction, filtering, measurement and analysis, localization, and the practicalities of setting up the lab.

    This Very Short Introduction discusses both the history and key debates of postcolonialism, and considers its importance as a means of changing the way we think about the world.Robert J. C. Young examines the key strategies that postcolonial thought has developed to engage with the impact of sometimes centuries of western political and cultural domination. Situating the discussion in a wide cultural and geographical context, he draws on examples such as the status of indigenous peoples, of those dispossessed from their land, Algerian rai music, and global social and ecological movements. In this new edition he also includes updated material on race, slavery, and postcolonial gender politics. Above all, Young argues that postcolonialism offers a political philosophy of activism that contests the current situation of global inequality, which in a new way continues the anti-colonial struggles of the past and enables us to decolonize our own lives in the present.ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area These pocket-sized books are the perfect way to get ahead in a new subject quickly Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable

    The first chapter outlines the theoretical foundations for IPA. It discusses phenomenology, hermeneutics, and idiography and how they have been taken up by IPA. The next four chapters provide detailed, step by step guidelines to conducting IPA research: study design, data collection and interviewing, data analysis, and writing up. In the next section, the authors give extended worked examples from their own studies in health, sexuality, psychological distress, and identity to illustrate the breadth and depth of IPA research. The final section of the book considers how IPA connects with other contemporary qualitative approaches like discourse and narrative analysis and how it addresses issues to do with validity.

    In this important and essential new textbook, she introduces the reader to the craft of using grounded theory in social research, and provides a clear, step-by-step guide for those new to the field. Using worked examples throughout, this book also maps out an alternative vision of grounded theory put forward by its founding thinkers, Glaser and Strauss. To Charmaz, grounded theory must move on from its positivist origins and must incorporate many of the methods and questions posed by constructivists over the past twenty years to become a more nuanced and reflexive practice.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples--ready for immediate application to your next graph or presentation.Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data--Storytelling with Data will give you the skills and power to tell it!

    He also tackles the complicated issue of identity and details recent efforts and successes in cultural preservation and language revitalization.A personal account from the state’s first female Indian lawyer, Margaret Treuer, tells her firsthand experience of much change in the community and looks ahead with renewed cultural strength and hope for the first people of Minnesota.Anton Treuer is professor of Ojibwe at Bemidji State University and editor of Living Our Language: Ojibwe Tales and Oral Histories, Aaniin Ekidong: Ojibwe Vocabulary Project, Omaa Akiing, and Oshkaabewis Native Journal, the only academic journal of the Ojibwe language.