The guide includes information on aspects of the search that are common to all levels, with invaluable tips for those seeking their first or second faculty position. This new edition provides updated advice and addresses hot topics in the competitive job market of today, including the challenges faced by dual-career couples, job search issues for pregnant candidates, and advice on how to deal with gaps in a CV. The chapter on alternatives to academic jobs has been expanded, and sample resumes from individuals seeking nonfaculty positions are included.The book begins with an overview of the hiring process and a timetable for applying for academic positions. It then gives detailed information on application materials, interviewing, negotiating job offers, and starting the new job. Guidance throughout is aimed at all candidates, with frequent reference to the specifics of job searches in scientific and technical fields as well as those in the humanities and social sciences. Advice on seeking postdoctoral opportunities is also included.Perhaps the most significant contribution is the inclusion of sample vitas. "The Academic Job Search Handbook" describes the organization and content of the vita and includes samples from a variety of fields. In addition to CVs and research statements, new in this edition are a sample interview itinerary, a teaching portfolio, and a sample offer letter. The job search correspondence section has also been updated, and there is current information on Internet search methods and useful websites.

    Five actual journal articles are reproduced in the appendix as examples of the different research designs.

    The 11th edition of this text continues to build on four major marketing themes: building and managing profitable customer relationships, building and managing strong brands to create brand equity, harnessing new marketing technologies in the digital age, and marketing in a socially responsible way around the globe.

    Groundbreaking in every way when first published, this book is a simple, straightforward, direct calculus text. It's popularity is directly due to its broad use of applications, the easy-to-understand writing style, and the wealth of examples and exercises which reinforce conceptualization of the subject matter. The author wrote this text with three objectives in mind. The first was to make the book more student-oriented by expanding discussions and providing more examples and figures to help clarify concepts. To further aid students, guidelines for solving problems were added in many sections of the text. The second objective was to stress the usefulness of calculus by means of modern applications of derivatives and integrals. The third objective, to make the text as accurate and error-free as possible, was accomplished by a careful examination of the exposition, combined with a thorough checking of each example and exercise.

    Using real-life case studies and written with a student-centered approach, this new edition provides students with the necessary knowledge and skills to enable them to undertake a piece of business research making the best use of IT where appropriate.

    Dan Harris's Quantitative Chemical Analysis continues to be the most widely used  textbook for analytical chemistry.  It offers consistently modern portrait of the tools and techniques of chemical analysis, incorporating real data, spreadsheets, and a wealth of applications, all presented in a witty, personable style that engages students without compromising the  principles and depth necessary for a thorough and practical understanding.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information. Recently published, this new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.

    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.

    The authors review their earlier findings and then synthesize what has been learned since 1990 about college's influences on students' learning. The book also discusses the implications of the findings for research, practice, and public policy. This authoritative and comprehensive analysis of the literature on college-impact is required reading for anyone interested in higher education practice, policy, and promise3/4faculty, administrators, researchers, policy analysts, and decision-makers at every level.

    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.

    With a logical, body-systems organization and engaging terminology exercises throughout, it's your key to communicating confidently and effectively with other health care professionals.Systematic approach to terminology prepares you to recognize and define new words as you encounter them and build the medical vocabulary you'll need in the health care setting.Pronunciation key provides quick access to frequently referenced material.Complimentary and Alternative Medicine terms boxes highlight words and phrases associated with this increasingly popular discipline.Case studies encourage critical thinking and demonstrate how to apply the information you've learned.Terminology flash cards, included with every book, give you valuable review and self-assessment tools you can take anywhere for study on the go.Evolve resources enhance your learning and reinforcement opportunities with additional exercises, a Spanish/English glossary, and the Body Spectrum Electronic Anatomy Coloring Book.Medical Terminology Online, available at an additional charge, gives you access to a complete online course for the most advanced learning and understanding.New terms and abbreviations familiarize you with the latest terminology in use in health care.New images and illustrations visually acquaint you with pathologic information and procedures you'll encounter in the clinical setting.Enhanced chapter features highlight important concepts and provide guidance for more effective learning and study.CD references within the text direct you to expanded learning opportunities on the companion CD.More than 20 new medical records let you practice medical terminology using the forms you'll encounter in the clinical setting.New icons make it easy to distinguish a variety of helpful boxes and reference the material you need quickly.Answers to review exercises help you gauge your strengths and weaknesses and configure the most effective study plan for you.Website boxes refer you to valuable content you can access online for further learning.Revised pharmacy appendix helps you easily reference key pharmaceutical terms.The vastly updated companion CD provides fun alternatives for reinforcing what you've learned with new learning games, including Medical Millionaire and Termbusters.Enhanced audio companion, available on CD or as iTerms downloads for portable media players, helps you perfect your pronunciation skills and confidently use the terms you've learned in practice.

    I also hope that it will serve as a useful reference too for the many workers engaged in one type of solid state research activity or another, who may be without formal training in the subject.

    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

    Yin's bestselling text provides a complete portal to the world of case study research. With the integration of 11 applications in this edition, the book gives readers access to exemplary case studies drawn from a wide variety of academic and applied fields. Ultimately, Case Study Research and Applications will guide students in the successful design and use of the case study research method. New to this Edition Includes 11 in-depth applications that show how researchers have implemented case study methods successfully. Increases reference to relativist and constructivist approaches to case study research, as well as how case studies can be part of mixed methods projects. Places greater emphasis on using plausible rival explanations to bolster case study quality. Discusses synthesizing findings across case studies in a multiple-case study in more detail Adds an expanded list of 15 fields that have text or texts devoted to case study research. Sharpens discussion of distinguishing research from non-research case studies. The author brings to light at least three remaining gaps to be filled in the future: how rival explanations can become more routinely integrated into all case study research; the difference between case-based and variable-based approaches to designing and analyzing case studies; and the relationship between case study research and qualitative research.