Yet the corporatisation of the contemporary university has sped up the clock, demanding increased speed and efficiency from faculty regardless of the consequences for education and scholarship.In The Slow Professor, Maggie Berg and Barbara K. Seeber discuss how adopting the principles of the Slow movement in academic life can counter this erosion of humanistic education. Focusing on the individual faculty member and his or her own professional practice, Berg and Seeber present both an analysis of the culture of speed in the academy and ways of alleviating stress while improving teaching, research, and collegiality. The Slow Professor will be a must-read for anyone in academia concerned about the frantic pace of contemporary university life.

    With this accessible step-by-step guide, anyone--from students developing a research poster for a school project to faculty and researchers presenting data at a conference--can learn how to present and communicate their research findings in more interesting and effective ways.Author Stephanie Evergreen draws on her extensive experience in the study of research reporting, interdisciplinary evaluation, and data visualization, as well as from diverse interdisciplinary fields, including cognitive psychology, communications, and graphic design, to extract tangible and practical data-reporting communication lessons and insights. She then demonstrates how to apply those principles to the design of data presentations to make it easier for the audience to understand, remember, and use the data.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    A finished book—or even steady journal articles—may seem like an impossible dream. But, as Joli Jensen proves, it really is possible to write happily and productively in academe. Jensen begins by busting the myth that universities are supportive writing environments.  She points out that academia, an arena dedicated to scholarship, offers pressures that actually prevent scholarly writing. She shows how to acknowledge these less-than-ideal conditions, and how to keep these circumstances from draining writing time and energy. Jensen introduces tools and techniques that encourage frequent, low-stress writing. She points out common ways writers stall and offers workarounds that maintain productivity. Her focus is not on content, but on how to overcome whatever stands in the way of academic writing.Write No Matter What draws on popular and scholarly insights into the writing process and stems from Jensen’s experience designing and directing a faculty writing program. With more than three decades as an academic writer, Jensen knows what really helps and hinders the scholarly writing process for scholars in the humanities, social sciences,and sciences. Cut down the academic sword of Damocles, Jensen advises. Learn how to write often and effectively, without pressure or shame. With her encouragement, writers of all levels will find ways to create the writing support they need and deserve.

    This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks. It is based in part on the authors blog posts, lecture materials, and tutorials. The author is one of the co-developers of the Johns Hopkins Specialization in Data Science the largest data science program in the world that has enrolled more than 1.76 million people. The book is useful as a companion to introductory courses in data science or data analysis. It is also a useful reference tool for people tasked with reading and critiquing data analyses. It is based on the authors popular open-source guides available through his Github account (https://github.com/jtleek). The paper is also available through Leanpub (https://leanpub.com/datastyle), if the book is purchased on that platform you are entitled to lifetime free updates.

    The book begins with an introduction to the language of statistics and then covers descriptive statistics and inferential statistics. Throughout, the author offers readers:- Difficulty Rating Index for each chapter′s material- Tips for doing and thinking about a statistical technique- Top tens for everything from the best ways to create a graph to the most effective techniques for data collection- Steps that break techniques down into a clear sequence of procedures- SPSS tips for executing each major statistical technique- Practice exercises at the end of each chapter, followed by worked out solutions.The book concludes with a statistical software sampler and a description of the best Internet sites for statistical information and data resources. Readers also have access to a website for downloading data that they can use to practice additional exercises from the book. Students and researchers will appreciate the book′s unhurried pace and thorough, friendly presentation.

    Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt.In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: -What type of mortgage risk Chase Bank predicted before the recession. -Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. -Why early retirement decreases life expectancy and vegetarians miss fewer flights. -Five reasons why organizations predict death, including one health insurance company. -How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual. -How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! -How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. -How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free. -What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward — but that can be predicted in advance?Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

    

    This text helps develop in readers the skills, knowledge and strategies needed to read and interpret research reports and to evaluate the quality of such reports.

    But what if science itself can’t be relied on?Medicine, education, psychology, health, parenting – wherever it really matters, we look to science for advice. Science Fictions reveals the disturbing flaws that undermine our understanding of all of these fields and more.While the scientific method will always be our best and only way of knowing about the world, in reality the current system of funding and publishing science not only fails to safeguard against scientists’ inescapable biases and foibles, it actively encourages them. From widely accepted theories about ‘priming’ and ‘growth mindset’ to claims about genetics, sleep, microbiotics, as well as a host of drugs, allergies and therapies, we can trace the effects of unreliable, overhyped and even fraudulent papers in austerity economics, the anti-vaccination movement and dozens of bestselling books – and occasionally count the cost in human lives.Stuart Ritchie was among the first people to help expose these problems. In this vital investigation, he gathers together the evidence of their full and shocking extent – and how a new reform movement within science is fighting back. Often witty yet deadly serious, Science Fictions is at the vanguard of the insurgency, proposing a host of remedies to save and protect this most valuable of human endeavours from itself.

    Guptill's classic Rendering in Pen and Ink has long been regarded as the most comprehensive book ever published on the subject of ink drawing. This is a book designed to delight and instruct anyone who draws with pen and ink, from the professional artist to the amateur and hobbyist. It is of particular interest to architects, interior designers, landscape architects, industrial designers, illustrators, and renderers. Contents include a review of materials and tools of rendering; handling the pen and building tones; value studies; kinds of outline and their uses; drawing objects in light and shade; handling groups of objects; basic principles of composition; using photographs, study of the work of well-known artists; on-the-spot sketching; representing trees and other landscape features; drawing architectural details; methods of architectural rendering; examination of outstanding examples of architectural rendering; solving perspective and other rendering problems; handling interiors and their accessories; and finally, special methods of working with pen including its use in combination with other media. The book is profusely illustrated with over 300 drawings that include the work of famous illustrators and renderers of architectural subjects such as Rockwell Kent, Charles Dana Gibson, James Montgomery Flagg, Willy Pogany, Reginald Birch, Harry Clarke, Edward Penfield, Joseph Clement Coll, F.L. Griggs, Samuel V. Chamberlain, Louis C. Rosenberg, John Floyd Yewell, Chester B. Price, Robert Lockwood, Ernest C. Peixotto, Harry C. Wilkinson, Bertram Grosvenor Goodhue, and Birch Burdette Long. Best of all, Arthur Guptill enriches the text with drawings of his own.

    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.

    It provides dozens of new examples from actual research studies are used to provide illustrations of concepts and methods. Key terms are now called out and defined in boxes at the bottom of the pages where they appear, for easier study and review. In chapter 1, there are now separate descriptions and examples of the steps in the research process for quantitative and qualitative approaches, to underscore some of the fundamental differences. Chapter 2 has new discussions of participatory action research, instrumental and reflexive knowledge, the various audiences for social research findings, and researcher autonomy when research is commissioned. The discussion of social theories in Chapter 3 now covers levels of abstraction, and relationships among concepts

    The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http: //www.stat.columbia.edu/ gelman/arm/

    But for some reason they choose to ignore those guidelines and churn out turgid, pompous, and obscure prose. Distinguished sociologist Howard S. Becker, true to his calling, looks for an explanation for this bizarre behavior not in the psyches of his colleagues but in the structure of his profession. In this highly personal and inspirational volume he considers academic writing as a social activity.Both the means and the reasons for writing a thesis or article or book are socially structured by the organization of graduate study, the requirements for publication, and the conditions for promotion, and the pressures arising from these situations create the writing style so often lampooned and lamented. Drawing on his thirty-five years' experience as a researcher, writer, and teacher, Becker exposes the foibles of the academic profession to the light of sociological analysis and gentle humor. He also offers eminently useful suggestions for ways to make social scientists better and more productive writers. Among the topics discussed are how to overcome the paralyzing fears of chaos and ridicule that lead to writer's block; how to rewrite and revise, again and again; how to adopt a persona compatible with lucid prose; how to deal with that academic bugaboo, "the literature." There is also a chapter by Pamela Richards on the personal and professional risks involved in scholarly writing.In recounting his own trials and errors Becker offers his readers not a model to be slavishly imitated but an example to inspire. Throughout, his focus is on the elusive work habits that contribute to good writing, not the more easily learned rules of grammar and punctuation. Although his examples are drawn from sociological literature, his conclusions apply to all fields of social science, and indeed to all areas of scholarly endeavor. The message is clear: you don't have to write like a social scientist to be one.