It also discusses 'mixed methods research', that is, the various combinations of qualitative and quantitative methodologies.

    Bigger, stronger and more cunning than any other predator in the woods, this murderer has only existed in legends and the nightmares of the few to survive an encounter … until now. Dr. Jake Sanders, world-renowned expert on predators and predatory behavior, is on a quest to find the monster that did far worse than wound him. Deputy Julie Reed, a strong, beautiful and lethal enforcer of justice, fearlessly pursues a violent adversary unaware of the twisted path destiny will lay before her. Simon Standing Elk, a recovering alcoholic well on the road to atoning for past sins until a terrifying encounter that now threatens not only his life, but his soul as well. Three different people. Three very different backgrounds. One secret organization. This is the story of events that brought them together and, if they’re not careful, could destroy them all – or worse.

    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.

    Unlike traditional empirical psychology, it is more oriented toward the how than the what. An experiment in mind design is more likely to be an attempt to build something and make it work--as in artificial intelligence--than to observe or analyze what already exists. Mind design is psychology by reverse engineering.When Mind Design was first published in 1981, it became a classic in the then-nascent fields of cognitive science and AI. This second edition retains four landmark essays from the first, adding to them one earlier milestone (Turing's Computing Machinery and Intelligence) and eleven more recent articles about connectionism, dynamical systems, and symbolic versus nonsymbolic models. The contributors are divided about evenly between philosophers and scientists. Yet all are philosophical in that they address fundamental issues and concepts; and all are scientific in that they are technically sophisticated and concerned with concrete empirical research.ContributorsRodney A. Brooks, Paul M. Churchland, Andy Clark, Daniel C. Dennett, Hubert L. Dreyfus, Jerry A. Fodor, Joseph Garon, John Haugeland, Marvin Minsky, Allen Newell, Zenon W. Pylyshyn, William Ramsey, Jay F. Rosenberg, David E. Rumelhart, John R. Searle, Herbert A. Simon, Paul Smolensky, Stephen Stich, A.M. Turing, Timothy van Gelder

    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

     Second edition of a popular introductory textbook, combining breadth of coverage, practical examples, and student-friendly features Includes new sections on metaphor, framing, stance and style, multimodal discourse, and Gricean pragmatics Considers a variety of approaches to the subject, including critical discourse analysis, conversation analysis, interactional and variationist sociolinguistics, ethnography, corpus linguistics, and other qualitative and quantitative methods Features detailed descriptions of the results of discourse analysts' work Retains and expands the useful student features, including discussion questions, exercises, and ideas for small research projects.

    Barbara Crafton writes with exquisite nakedness about the futile search for meaning in the meaninglessness of despair. Her own salvation is a beacon to those who believe God means them to suffer in order to understand." --Gail Sheehy, author, Passages; Understanding Men's Passages"Writing well about depression is not nearly as challenging as surviving the beast, but it is still a hard thing to do. Having written about my own depression, I can say with some authority that Barbara Crafton, a fellow sufferer, writes wonderfully well on this difficult topic.... This book offers truth about the devastating darkness of this disease and about the hope that makes it possible to find one's way back to the light. Barbara Crafton offers up her truth with humor and gritty stories as well as candor and care.... May the many who suffer?and those who care for them, read this book, shed the shame, and find the new life that awaits them on the other side." --Parker J. Palmer, author, A Hidden Wholeness, Let Your Life Speak, and The Courage to Teach"Having known the tension of faith and depression in her own life, Barbara Crafton offers us wisdom that comes from years of reflection, of faithful practice, of knowing 'dark is not dark to you, O Lord.' (Psalms 139:11) She has no truck with pablum Christianity; she knows that faith that does not meet our darkest days is no faith at all?. Crafton offers sound insight and speaks the truth in love, offering hope and acceptance to those of us who struggle with depression." --Mary C. Earle, author, Broken Body, Healing Spirit: Lectio Divina and Living with Illness and Beginning Again: Benedictine Wisdom for Living with Illness

    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.

    

    The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

    Looking at the experiences of students and teachers in three LA public high schools, this volume investigates why so few African American and Latino high school students are studying computer science.

    Cast.It is 50 pages of reprinted Wikipedia and other public domain online articles.Hephaestus Books represents a new publishing paradigm, allowing disparate content sources to be curated into cohesive, relevant, and informative books. To date, this content has been curated from Wikipedia articles and images under Creative Commons licensing, although as Hephaestus Books continues to increase in scope and dimension, more licensed and public domain content is being added. We believe books such as this represent a new and exciting lexicon in the sharing of human knowledge. This particular book is a collaboration focused on House of Night series.

    The science of complexity studies how single elements, such as a species or a stock, spontaneously organize into complicated structures like ecosystems and economies; stars become galaxies, and snowflakes avalanches almost as if these systems were obeying a hidden yearning for order. Drawing from diverse fields, scientific luminaries such as Nobel Laureates Murray Gell-Mann and Kenneth Arrow are studying complexity at a think tank called The Santa Fe Institute. The revolutionary new discoveries researchers have made there could change the face of every science from biology to cosmology to economics. M. Mitchell Waldrop's groundbreaking bestseller takes readers into the hearts and minds of these scientists to tell the story behind this scientific revolution as it unfolds.

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.