It bridges the gap between the mechanics of econometrics and modern applications of econometrics by employing a systematic approach motivated by the major problems facing applied researchers today. Throughout the text, the emphasis on examples gives a concrete reality to economic relationships and allows treatment of interesting policy questions in a realistic and accessible framework.

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    Stake uses and annotates an actual case study to answer such questions as: How is the case selected? How do you select the case which will maximize what can be learned? How can what is learned from one case be applied to another? How can what is learned from a case be interpreted? In addition, the book covers: the differences between quantitative and qualitative approaches; data-gathering including document review; coding, sorting and pattern analysis; the roles of the researcher; triangulation; and reporting.

    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.

    Offers students with the tools necessary to do ethical research in psychology and to understand the research they learn about in psychology courses and in the media.

     Douglas Montgomery arms readers with the most effective approach for learning how to design, conduct, and analyze experiments that optimize performance in products and processes. He shows how to use statistically designed experiments to obtain information for characterization and optimization of systems, improve manufacturing processes, and design and develop new processes and products. You will also learn how to evaluate material alternatives in product design, improve the field performance, reliability, and manufacturing aspects of products, and conduct experiments effectively and efficiently. Discover how to improve the quality and efficiency of working systems with this highly-acclaimed book. This 6th Edition: Places a strong focus on the use of the computer, providing output from two software products: Minitab and DesignExpert. Presents timely, new examples as well as expanded coverage on adding runs to a fractional factorial to de-alias effects. Includes detailed discussions on how computers are currently used in the analysis and design of experiments. Offers new material on a number of important topics, including follow-up experimentation and split-plot design. Focuses even more sharply on factorial and fractional factorial design.

    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.

    Using actual unfinished, "working" notes as examples, they illustrate options for composing, reviewing, and working fieldnotes into finished texts. They discuss different organizational and descriptive strategies, including evocation of sensory detail, synthesis of complete scenes, the value of partial versus omniscient perspectives, and of first person versus third person accounts. Of particular interest is the author's discussion of notetaking as a mindset. They show how transforming direct observations into vivid descriptions results not simply from good memory but more crucially from learning to envision scenes as written. A good ethnographer, they demonstrate, must learn to remember dialogue and movement like an actor, to see colors and shapes like a painter, and to sense moods and rhythms like a poet.The authors also emphasize the ethnographer's core interest in presenting the perceptions and meanings which the people studied attach to their own actions. They demonstrate the subtle ways that writers can make the voices of people heard in the texts they produce. Finally, they analyze the "processing" of fieldnotes—the practice of coding notes to identify themes and methods for selecting and weaving together fieldnote excerpts to write a polished ethnography.This book, however, is more than a "how-to" manual. The authors examine writing fieldnotes as an interactive and interpretive process in which the researcher's own commitments and relationships with those in the field inevitably shape the character and content of those fieldnotes. They explore the conscious and unconscious writing choices that produce fieldnote accounts. And they show how the character and content of these fieldnotes inevitably influence the arguments and analyses the ethnographer can make in the final ethnographic tale.This book shows that note-taking is a craft that can be taught. Along with Tales of the Field and George Marcus and Michael Fisher's Anthropology as Cultural Criticism, Writing Ethnographic Fieldnotes is an essential tool for students and social scientists alike.

    Topics covered include matrix multiplication, row reduction, matrix inverse, orthogonality and computation. The self-teaching book is loaded with examples and graphics and provides a wide array of probing problems, accompanying solutions, and a glossary. Chapter 1: Introduction to Vectors; Chapter 2: Solving Linear Equations; Chapter 3: Vector Spaces and Subspaces; Chapter 4: Orthogonality; Chapter 5: Determinants; Chapter 6: Eigenvalues and Eigenvectors; Chapter 7: Linear Transformations; Chapter 8: Applications; Chapter 9: Numerical Linear Algebra; Chapter 10: Complex Vectors and Matrices; Solutions to Selected Exercises; Final Exam. Matrix Factorizations. Conceptual Questions for Review. Glossary: A Dictionary for Linear Algebra Index Teaching Codes Linear Algebra in a Nutshell.

    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.

    The study of type systems--and of programming languages from a type-theoretic perspective--has important applications in software engineering, language design, high-performance compilers, and security.This text provides a comprehensive introduction both to type systems in computer science and to the basic theory of programming languages. The approach is pragmatic and operational; each new concept is motivated by programming examples and the more theoretical sections are driven by the needs of implementations. Each chapter is accompanied by numerous exercises and solutions, as well as a running implementation, available via the Web. Dependencies between chapters are explicitly identified, allowing readers to choose a variety of paths through the material.The core topics include the untyped lambda-calculus, simple type systems, type reconstruction, universal and existential polymorphism, subtyping, bounded quantification, recursive types, kinds, and type operators. Extended case studies develop a variety of approaches to modeling the features of object-oriented languages.

    Ware's updated review of empirical research and interface design examples will do much to accelerate innovation and adoption of information visualization." —Ben Shneiderman, University of Maryland"Colin Ware is the perfect person to write this book, with a long history of prominent contributions to the visual interaction with machines and to information visualization directly. It goes a long way towards joining science to the practical design of information visualization systems." —from the foreword by Stuart Card, PARCMost designers know that yellow text presented against a blue background reads clearly and easily, but how many can explain why, and what really are the best ways to help others and ourselves clearly see key patterns in a bunch of data? When we use software, access a web site, or view graphics, our understanding is greatly enhanced or impeded by the way information is presented. By explaining in detail how we think visually, this book provides guidance on how to construct effective interactive information displays.This book combines a strictly scientific approach to human perception with a practical concern for the rules governing the effective visual presentation of information. Surveying the research of leading psychologists and neurophysiologists, author Colin Ware isolates key principles at work in vision and perception, and from them derives specific and effective visualization techniques suitable for a wide range of scenarios. Information Visualization offers practical guidelines that can be applied by anyone, and covers all facets of visual perception: color, organization, space perception, motion, and texture.* Major revision of this classic work, with a new chapter on visual thinking, new sections on face perception and flow visualization, an appendix on how to evaluate visualizations,and a greatly expanded chapter on color and color sequences. *New to this edition is the full-color treatment throughout, to better display over 400 illustrations.*From a leading researcher in the field of human perception who has brought together, in a single resource, all current scientific insight into the question of data visualization.

    Social Network Analysis: Methods and Applications reviews and discusses methods for the analysis of social networks with a focus on applications of these methods to many substantive examples. As the first book to provide a comprehensive coverage of the methodology and applications of the field, this study is both a reference book and a textbook.

    Premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid foundation in statistical theory while conveying the relevance and importance of the theory in solving practical problems in the real world. The authors' use of practical applications and excellent exercises helps readers discover the nature of statistics and understand its essential role in scientific research.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.