It is a book that models the types of issues that best suit different approaches and allows students to understand when to use mixed methods. Furthermore, its focus on theory and paradigms is done in a way that helps students decode their meaning." --MARTHA MONTERO-SIEBURTH, University of Massachusetts, BostonNew to the Second Edition:Because mixed methods research has come into its own since the publication of the first edition, every chapter now shows how to implement a mixed methods design in your proposal or plan as well as showing how to implement qualitative and quantitative approaches Ethical issues that may arise in quantitative, qualitative and mixed methods designs have been added to a new section in Chapter 3 Writing tips and considerations have been expanded and moved to the first part of the book to get your research plan started in the right direction The latest developments in qualitative inquiry, including advocacy, participatory, and emancipatory approaches have been added to Chapter 10 Mixed methods procedures (Chapter 11) show readers how to identify types of mixed methods strategy, select data collection and analysis approaches, and plan the overall structure of the study

    The text offers a flexible organization, enabling instructors to adapt the book to their particular courses. The book is both complete and careful, and it continues to maintain its emphasis on algorithms and applications. Excellent exercise sets allow students to perfect skills as they practice. This new edition continues to feature numerous computer science applications-making this the ideal text for preparing students for advanced study.

    This book integrates theory with facts and covers the research.

    The new edition maintains the authors' innovative solutions to two central problems of the course: first, the text continues to focus on about 80 sites, giving students less encyclopedic detail but essential coverage of the discoveries that have produced the major insights into prehistory; second, it continues to be organized into essays on sites and concepts, allowing professors complete flexibility in organizing their courses.

    Your guide to a higher math score on standardized tests*SATACT(R)ASVABGMAT(R)GRE(R)CBEST(R)PRAXIS I(R)GED(R) And More!Why CliffsNotes?Go with the name you know and trustGet the information you need-fast!About the Contents:IntroductionHow to use this bookOverview of the examsPart I: Basic Skills ReviewArithmetic and Data AnalysisAlgebraPart II: Strategies and PracticeMathematical AbilityQuantitative ComparisonData SufficiencyEach section includes a diagnostic test, explanations of rules, concepts withexamples, practice problems with complete explanations, a review test, and aglossary!Test-Prep Essentials from the Experts at CliffsNotes(R)For more test-prep help, visit CliffsNotes.com(R)*SAT is a registered trademark of the College Board, which was not involved inthe production of, and does not endorse, this product.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    This book will offer students a broad outline of essential mathematics and will help to fill in the gaps in their knowledge. The author explains the basic points and a few key results of all the most important undergraduate topics in mathematics, emphasizing the intuitions behind the subject. The topics include linear algebra, vector calculus, differential and analytical geometry, real analysis, point-set topology, probability, complex analysis, set theory, algorithms, and more. An annotated bibliography offers a guide to further reading and to more rigorous foundations.