Data science and analysis Books

Book SynopsisFoundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated

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Book SynopsisThe Third Edition ofAn R Companion to Political Analysisby Philip H. Pollock III and Barry C. Edwards teaches your students to conduct political research with R, the open-source programming language and software environment for statistical computing and graphics. This workbookoffers the same easy-to-use and effective style as the other software companions to theEssentials of Political Analysis, tailored for R.With this comprehensive workbook, students analyze research-quality data to learn descriptive statistics, data transformations, bivariate analysis (such as cross-tabulations and mean comparisons), controlled comparisons, correlation and bivariate regression, interaction effects, and logistic regression. The clear explanations and instructions are aided by the use of many annotated and labeled screen shots, as well as QR codes linking to demonstration videos. The many end-of-chapter exercises allow students to apply their new skills. The ThirdTable of ContentsChapter 1: Using R for Data Analysis Chapter 2: Descriptive Statistics Chapter 3: Creating and Transforming Variables Chapter 4: Making Comparisons Chapter 5: Graphing Relationships and Describing Patterns Chapter 6: Random Assignment and Sampling Chapter 7: Making Controlled Comparisons Chapter 8: Foundations of Statistical Inference Chapter 9: Hypothesis Tests with One or Two Samples Chapter 10: Chi-Square Test and Analysis of Variance Chapter 11: Correlation and Bivariate Regression Chapter 12: Multiple Regression Chapter 13: Analyzing Regression Residuals Chapter 14: Logistic Regression Chapter 15: Doing Your Own Political Analysis

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Book SynopsisStatistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography, pattern recognition for satellite images from remote sensing, management of agriculture and forests, assessment of climate change, and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics are covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms, and deep learning, as well as the recent merging of machine learning and physics. End-of-chapter exercises allow readers to develop tTrade Review'As a new wave of machine learning becomes part of our toolbox for environmental science, this book is both a guide to the latest developments and a comprehensive textbook on statistics and data science. Almost everything is covered, from hypothesis testing to convolutional neural networks. The book is enjoyable to read, well explained and economically written, so it will probably become the first place I'll go to read up on any of these topics.' Alan Geer, European Centre for Medium-Range Weather Forecasts (ECMWF)'William Hsieh has been one of the 'founding fathers' of an exciting new field of using machine learning (ML) in the environmental sciences. His new book provides readers with a solid introduction to the statistical foundation of ML and various ML techniques, as well as with the fundamentals of data science. The unique combination of solid mathematical and statistical backgrounds with modern applications of ML tools in the environmental sciences … is an important distinguishing feature of this book. The broad range of topics covered in this book makes it an invaluable reference and guide for researchers and graduate students working in this and related fields.' Vladimir Krasnopolsky, Center for Weather and Climate Prediction, NOAA'Dr. Hsieh is one of the pioneers of the development of machine learning for the environmental sciences including the development of methods such as nonlinear principal component analysis to provide insights into the ENSO dynamic. Dr. Hsieh has a deep understanding of the foundations of statistics, machine learning, and environmental processes that he is sharing in this timely and comprehensive work with many recent references. It will no doubt become an indispensable reference for our field. I plan to use the book for my graduate environmental forecasting class and recommend the book for a self-guided progression or as a comprehensive reference.' Philippe Tissot, Texas A&M University-Corpus Christi'There is a need for a forward-looking text on environmental data science and William Hsieh's text succeeds in filling the gap. This comprehensive text covers basic to advanced material ranging from timeless statistical techniques to some of the latest machine learning approaches. His refreshingly engaging style is written to be understood and complemented by a plethora of expressive visuals. Hsieh's treatment of nonlinearity is cutting-edge and the final chapter examines ways to combine machine learning with physics. This text is destined to become a modern classic.' Sue Ellen Haupt, National Center for Atmospheric ResearchTable of Contents1. Introduction; 2. Basics; 3. Probability distributions; 4. Statistical inference; 5. Linear regression; 6. Neural networks; 7. Nonlinear optimization; 8. Learning and generalization; 9. Principal components and canonical correlation; 10. Unsupervised learning; 11. Time series; 12. Classification; 13. Kernel methods; 14. Decision trees, random forests and boosting; 15. Deep learning; 16. Forecast verification and post-processing; 17. Merging of machine learning and physics; Appendices; References; Index.

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Book SynopsisClassical statistical tools that handled real-life data have become inadequate upon the emergence of Big Data. Random matrix theory and free calculus introduced here present valuable solutions to the complex challenges posed by large datasets. Real world applications make it an essential tool for physicists, engineers, data analysts and economists.Table of ContentsPreface; Part I. Classical Random Matrix Theory: 1. Deterministic Matrices; 2. Wigner Ensemble and Semi-circle Law; 3. More on Gaussian Matrices; 4. Wishart Ensemble and Marcenko-Pastur Distribution; 5. Joint Distribution of Eigenvalues; 7. The Jacobi Ensemble; Part II. Sums and Products of Random Matrices: 8. Addition of Random Variables and Brownian Motion; 9. Dyson Brownian Motion; 10. Addition of Large Random Matrices; 11. Free Probabilities; 12. Free Random Matrices; 13. The Replica Method; 14. Edge Eigenvalues and Outliers; Part III. Applications: 15. Addition and Multiplication: Recipes and Examples; 16. Products of Many Random Matrices; 17. Sample Covariance Matrices; 18. Bayesian Estimation; 19. Eigenvector Overlaps and Rotationally Invariant Estimators; 20. Applications to Finance; Appendix A. Appendices: Mathematical Tools; List of Symbols; Index.

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Book SynopsisRecent years have seen an explosion in the volume and variety of data collected in scientific disciplines from astronomy to genetics and industrial settings ranging from Amazon to Uber. This graduate text equips readers in statistics, machine learning, and related fields to understand, apply, and adapt modern methods suited to large-scale data.Trade Review'Non-asymptotic, high-dimensional theory is critical for modern statistics and machine learning. This book is unique in providing a crystal clear, complete and unified treatment of the area. With topics ranging from concentration of measure to graphical models, the author weaves together probability theory and its applications to statistics. Ideal for graduate students and researchers. This will surely be the standard reference on the topic for many years.' Larry Wasserman, Carnegie Mellon University, Pennsylvania'Martin J. Wainwright brings his large box of analytical power tools to bear on the problems of the day - the analysis of models for wide data. A broad knowledge of this new area combines with his powerful analytical skills to deliver this impressive and intimidating work - bound to be an essential reference for all the brave souls that try their hand.' Trevor Hastie, Stanford University, California'This book provides an excellent treatment of perhaps the fastest growing area within high-dimensional theoretical statistics - non-asymptotic theory that seeks to provide probabilistic bounds on estimators as a function of sample size and dimension. It offers the most thorough, clear, and engaging coverage of this area to date, and is thus poised to become the definitive reference and textbook on this topic.' Genevera Allen, William Marsh Rice University, Texas'Statistical theory and practice have undergone a renaissance in the past two decades, with intensive study of high-dimensional data analysis. No researcher has deepened our understanding of high-dimensional statistics more than Martin Wainwright. This book brings the signature clarity and incisiveness of his published research into book form. It will be a fantastic resource for both beginning students and seasoned researchers, as the field continues to make exciting breakthroughs.' John Lafferty, Yale University, Connecticut'This is an outstanding book on high-dimensional statistics, written by a creative and celebrated researcher in the field. It gives comprehensive treatments on many important topics in statistical machine learning and, furthermore, is self-contained, from introductory materials to most updated results on various research frontiers. This book is a must-read for those who wish to learn and to develop modern statistical machine theory, methods and algorithms.' Jianqing Fan, Princeton University, New Jersey'This book provides an in-depth mathematical treatment and methodological intuition of high-dimensional statistics. The main technical tools from probability theory are carefully developed and the construction and analysis of statistical methods and algorithms for high-dimensional problems is presented in an outstandingly clear way. Martin J. Wainwright has written a truly exceptional, inspiring and beautiful masterpiece!' Peter Bühlmann, Eidgenössische Technische Hochschule Zürich'This new book by Martin J. Wainwright covers modern topics in high-dimensional statistical inference, and focuses primarily on explicit non-asymptotic results related to sparsity and non-parametric estimation. This is a must-read for all graduate students in mathematical statistics and theoretical machine learning, both for the breadth of recent advances it covers and the depth of results which are presented. The exposition is outstandingly clear, starting from the first introductory chapters on the necessary probabilistic tools. Then, the book covers state-of-the-art advances in high-dimensional statistics, with always a clever choice of results which have the perfect mix of significance and mathematical depth.' Francis Bach, INRIA Paris'Wainwright's book on those parts of probability theory and mathematical statistics critical to understanding of the new phenomena encountered in high dimensions is marked by the clarity of its presentation and the depth to which it travels. In every chapter he starts with intuitive examples and simulations which are systematically developed either into powerful mathematical tools or complete answers to fundamental questions of inference. It is not easy, but elegant and rewarding whether read systematically or dipped into as a reference.' Peter Bickel, University of California, Berkeley'… this is a very valuable book, covering a variety of important topics, self-contained and nicely written.' Fabio Mainardi, MAA Reviews'This is an excellent book. It provides a lucid, accessible and in-depth treatment of nonasymptotic high-dimensional statistical theory, which is critical as the underpinning of modern statistics and machine learning. It succeeds brilliantly in providing a self-contained overview of high-dimensional statistics, suitable for use in formal courses or for self-study by graduate-level students or researchers. The treatment is outstandingly clear and engaging, and the production is first-rate. It will quickly become essential reading and the key reference text in the field.' G. Alastair Young, International Statistical Review'Martin Wainwright takes great care to polish every sentence of each part of the book. He introduces state-of-the-art theory in every chapter, as should probably be expected from an acknowledged specialist of the field. But it is certainly an enormous amount of work to organize all these results in a complete, coherent, rigorous yet easy-to-follow theory. I am simply amazed by the quality of the writing. The explanations on the motivations (Chapter 1) and on the core of the theory are extremely pedagogical. The proofs of the main results are rigorous and complete, but most of them are also built in a way that makes them seem easier to the reader than they actually are. This is the kind of magic only a few authors are capable of.' Pierre Alquier, MatSciNet'... provides a masterful exposition of various mathematical tools that are becoming increasingly common in the analysis of contemporary statistical problems. In addition to providing a rigorous and comprehensive overview of these tools, the author delves into the details of many illustrative examples to provide a convincing case for the general usefulness of the methods that are introduced.' Po-Ling Lo, Bulletin of the American Mathematical Society'An excellent statistical masterpiece is in the hands of the reader, which is a must read book for all graduate students in both mathematical statistics and mathematical machine learning.' Rózsa Horváth-Bokor, ZB Math ReviewsTable of Contents1. Introduction; 2. Basic tail and concentration bounds; 3. Concentration of measure; 4. Uniform laws of large numbers; 5. Metric entropy and its uses; 6. Random matrices and covariance estimation; 7. Sparse linear models in high dimensions; 8. Principal component analysis in high dimensions; 9. Decomposability and restricted strong convexity; 10. Matrix estimation with rank constraints; 11. Graphical models for high-dimensional data; 12. Reproducing kernel Hilbert spaces; 13. Nonparametric least squares; 14. Localization and uniform laws; 15. Minimax lower bounds; References; Author index; Subject index.

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Book SynopsisLearn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book''s companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplinesTrade Review'This book provides an excellent introduction to time series modelling and forecasting which are increasingly important tools in the domain of official statistics. The clear descriptions and real-life examples provided in this text make it easy to digest for those not already familiar with the topic. In addition, the exercises allow readers to develop their understanding in more depth through hands-on applications of the methods to real data using open-source tools. The inclusion of modern topics such as machine learning and artificial intelligence are a valuable addition to make the text relevant and comprehensive.' Steve Matthews, Statistics Canada'This book is a great introduction to the ideas and methods of time series data analysis. Chapter by chapter, it will show you its most valuable features, like the wealth of real examples as well as practical uses of R and graphical visualization. You will certainly enjoy this text, as it is suitable for a wide range of statistical courses.' Vera Ioudina, Texas State University'Lots of good real world examples together with the use of R helps a lot as do the nice set of exercises. In time series, it is a tricky balance between overdoing theory or just hand waving and here the author does very well. This would make a lovely course text!' Gareth Janacek, University of East Anglia'Time Series for Data Scientists' develops your intuition before walking through classical and modern time series methods in easy-to-understand terms. With each algorithm Dr. Sanchez first helps you understand the motivation behind the approach; then walks you through the formulas step-by-step, outlining what we're doing and why; she also includes R code to help you apply the techniques learned to solve real-world business problems using real-world data sets; and takes the time to show you how to interpret the output, and discuss what to try next when an initial approach doesn't quite match the trends in the data. Whether you're an undergraduate or graduate student, are curious about time series methods, are looking for a self-paced book, or a reference guide, this is a must-have.' Irina Kukuyeva, Fractional Chief Data Officer'A fine textbook for an introductory time series course aimed at undergraduates in Statistics or Data Science. The author did an excellent work in the choice of topics, covering from classical exploratory techniques to modern machine learning approaches, while keeping the level of the exposition accessible to readers with a modicum of mathematical background. To be recommended!' Giovanni Petris, University of Arkansas'This book should be a serious contender if you are looking for an introductory text for an undergraduate course in time series. It is especially suited for a course populated with students having varying degrees of mathematical skill levels. Its conversational approach to introducing time series concepts and the use of insightful examples throughout the book makes it very accessible to students who are not highly trained in abstract mathematical reasoning. Nevertheless, it does not shy away from providing the theoretical underpinnings of various time series models but does so in a manner very accessible to students. The availability of R code throughout the book is an added plus. Even if I am teaching an upper-level graduate course in time series, I would use this book as a supplement simply because of the plethora of examples and data sources it provides.' V. A. Samaranayake, Missouri University of Science and TechnologyTable of ContentsPart I. Descriptive Features of Time Series Data: 1. Introduction to time series data; 2. Smoothing and decomposing a time series; 3. Summary statistics of stationary time series; Part II. Univariate Models of Temporal Dependence: 4. The algebra of differencing and backshifting; 5. Stationary stochastic processes; 6. ARIMA(p,d,q)(P,D,Q)$_F$ modeling and forecasting; Part III. Multivariate Modeling and Forecasting: 7. Latent process models for time series; 8. Vector autoregression; 9. Classical regression with ARMA residuals; 10. Machine learning methods for time series; References; Index.

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Book SynopsisAs a result of the COVID-19 pandemic, medical statistics and public health data have become staples of newsfeeds worldwide, with infection rates, deaths, case fatality and the mysterious R figure featuring regularly. However, we don''t all have the statistical background needed to translate this information into knowledge. In this lively account, Stephen Senn explains these statistical phenomena and demonstrates how statistics is essential to making rational decisions about medical care. The second edition has been thoroughly updated to cover developments of the last two decades and includes a new chapter on medical statistical challenges of COVID-19, along with additional material on infectious disease modelling and representation of women in clinical trials. Senn entertains with anecdotes, puzzles and paradoxes, while tackling big themes including: clinical trials and the development of medicines, life tables, vaccines and their risks or lack of them, smoking and lung cancer, and even the power of prayer.Trade Review'The COVID pandemic has shown the power of statistics to save millions of lives by revealing 'what works'. Yet statistical methods have a deeply controversial history, and provoke sometimes bitter debate to this day. Professor Stephen Senn is renowned for his brilliant insights on the subject, and in Dicing with Death he offers us a series of fascinating journeys through its vast and varied landscape.' Robert Matthews, Visiting Professor Aston University and author of Chancing It: The Laws of Chance and How They Can Work for YouTable of Contents1. Circling the square; 2. The diceman cometh; 3. Trials of life; 4. Of dice and men; 5. Sex and the single patient; 6. A hale view of pills (and other matters); 7. Time's tables; 8. A dip in the pool; 9. The things that bug us; 10. The law is a ass; 11. The empire of the sum; 12. Going viral; Notes; Index.

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Book SynopsisThis concise book for scientists and students interested in bioinformatics and data science covers all aspects of predictive modeling for biomarker discovery based on high-dimensional data, as well as modern data science methods for identification of parsimonious and robust multivariate biomarkers for medical diagnosis and personalized medicine.

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Book SynopsisHow can you carry out discourse analysis using corpus linguistics? What research questions should I ask? Which methods should you use and when? What is a collocational network or a key cluster? Introducing the major techniques, methods and tools for corpus-assisted analysis of discourse, this book answers these questions and more, showing readers how to best use corpora in their analyses of discourse. Using carefully tailored case studies, each chapter is devoted to a central technique, including frequency, concordancing and keywords, going step by step through the process of applying different analytical procedures. Introducing a wide range of different corpora, from holiday brochures to political debates, the book considers the key debates and latest advances in the field. Fully revised and updated, this new edition includes:- A new chapter on how to conduct research projects in corpus-based discourse analysis- Completely rewritten chapters on collocation and advanced techniTrade ReviewBaker (Lancaster University, UK) looks at how corpora (computerized collections of naturally occurring language samples) can be used for discourse analysis. The book has four particular strengths. First, the author explains corpus methodologies thoroughly, including frequency and dispersion, concordances, collocates, and keyness. Second, the grounds his explanations in concrete analyses of discourse used in tourism brochures, fox-hunting debates, and news articles on refugees (among other texts), thereby offering exemplars of the methodology; included are several tabular examples of analysis. And fourth, he explores the strengths and limitations of corpus analysis, explaining the need for self-reflection with respect to methodological decision-making. An excellent guide to the scope and method of corpus linguistics as applied to discourse analysis, this book on research methods will be valuable to those in linguistics, rhetoric and communication, literary theory and other humanities fields. * Choice Reviews (of the first edition) *Is a necessity for any researcher, practitioner or student interested in the interplay of content, discourse and corpus linguistics. It is a practical, hands-on guide that articulately explores the complex workings of corpora building and analysis. It is a valuable contribution for both the novice exploring the field and the more experienced scholar aiming to refresh their understanding of this ever-growing, ever-evolving discipline. * Discourse Studies (of the first edition) *Corpus methodologies have a huge potential for use in discourse studies, and Paul Baker has written a superb introduction that combines common sense and academic expertise. As a practical 'how-to' advisor he provides an accessible explanation of the key technical and interpretative issues. As an advocate of innovation, he is sensitive to the priorities and the research paradigms of both the discourse analyst and the corpus linguist. This is a splendid book that will inspire a new generation of research. -- Professor Susan Hunston, Department of English, University of Birmingham (of the first edition)We are given examples of research which demonstrate the various techniques and these can be intriguing...Using Corpora in Discourse Analysis should indeed build bridges, for those who are not already using them, but it will also be useful to anyone interested in language as it is used in texts...the generative nature of the techniques should be stimulating for all those who monitor language use... -- Alison Duguid * Times Literary Supplement (of the first edition) *If you want to know what corpus linguistics can offer to sociolinguists interested in the relationship between language and gender, this book is the answer. I found it hard to put down. Written in a wonderfully accessible style, it provides detailed examples of the challenging questions, messy data, and satisfying, though often approximate, answers that corpus linguistics can provide. It confronts researchers with the real nitty-gritty of the challenges and rewards of each step of a corpus linguistics project. Researchers and students will both find it invaluable. -- Janet Holmes, Professor of Linguistics, Victoria University of Wellington, New Zealand (of the first edition)One of the best introductory texts on corpus assisted discourse analysis currently available. Baker expertly embeds concrete examples of critical data analysis within wider discussion of methodological choices, using a range of corpus tools. Readers will find the step by step guides particularly useful, along with Baker’s inimitable clear and engaging writing style. -- Valerie Hobbs, Senior Lecturer in Applied Linguistics, University of Sheffield, UKThis new edition combines clear explanations of key corpus concepts with significantly updated chapters. Highlighted throughout are the technological advancements in corpus tools as they are applied to contemporary research questions. Once again, Baker’s extensive expertise provides an invaluable resource for integrating discourse and corpus methodologies in linguistic research. -- Tammy Gales, Associate Professor of Linguistics, Hofstra University, USATable of Contents1. Introduction 2. The First Stages 3. Corpus Building and Annotation 4. Frequency and Dispersion 5. Concordances 6. Collocates 7. Keyness 8. Beyond Collocation 9. What Comes Next? Bibliography Index

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Book SynopsisKevin Guyan is an equality, diversity and inclusion (EDI) researcher based in Edinburgh, Scotland. He is currently Head of Knowledge and Research at Advance HE, a higher education agency that works to improve EDI for staff and students in universities and colleges in the UK.Trade ReviewKevin Guyan’s Queer Data, though not a quick read, is very comprehensible to an average reader and is absolutely chockablock with ways to understand how research is conducted and how it systematically discounts queer people (or counts us incorrectly, or codes us incorrectly, or…). If you ever do research on anything involving people—even something as minor as a brand-preference survey—you must read this, absolutely. But even the lay reader with no research aspirations will find so many ways to prove that their homophobic cousin Karen is just plain wrong. * Xtra Magazine *[T]he book does an admirable job explaining the finer points behind the complicated constructs of sexual orientation and gender identity (SOGI) and drawing attention to nuances that make it difficult to precisely measure micro-minorities ... the book is a welcome addition on a topic that currently lacks wide attention. Guyan poses provocative questions that practitioners should consider before embarking on research that focuses on sexual and gender minorities. * Science Magazine *An accessible read, Queer Data is a must-read to understand why reliable data is necessary to ensure the improvement of everyday LGBTQ+ people, policies, and activist causes. -- One of Gay Times' 10 Most Anticipated Books of 2022A brilliant study on how [data is collected] within the LGBTQ community... enlightening reading. * Publishing Scotland *Each of Queer Data’s sections provides thought-provoking debates and relevant dilemmas grounded in rigorous academic concepts and rich evidence from practice. In this sense, one of the book’s core strengths is how it intertwines complex scholarly ideas with concrete problems that practitioners and activists wrestle within their day-to-day work. * Harvard Educational Review *...Very comprehensible to an average reader and absolutely chockablock with ways to understand how research is conducted and how it systematically discounts queer people...If you ever do research on anything involving people—even something as minor as a brand-preference survey—you must read this, absolutely. But even the lay reader with no research aspirations will find so many ways to prove that their homophobic cousin Karen is just plain wrong. * Xtra Magazine *A refreshingly clear and practical take which cuts through turbulent discourse and offers a new way of looking at fixing inequalities and responding to threats facing the LGBTQI+ community. * Emma Roddick, Member of the Scottish Parliament (MSP) *Committed to the project of changing the world for the better for Queer People, this book critically analyses the need to include LGBTQ people in policymaking. It’s enormously readable, theoretically informed and supported by evidence. * Julie Fish, Director of the Centre for LGBTQ Research, De Montfort University, UK *A unique, powerful call to action. Guyan boldly points out how queer data is ignored, ‘straightwashed’ or corrupted. It offers a way forward to engage with queer data to shape our own lived experiences. Highly recommended! * Drew Dalton, Senior Lecturer in Sociology and Programme Leader MSc Inequality and Society, University of Sunderland, UK *Zooming in on lesbian, gay, bisexual, trans, and queer (LGBTQ) rights, the book illuminates how increased knowledge about queer identities proves essential as a tool for action, which impacts decision making related to resource allocation, changes to legislation, access to services, representation, and visibility. * International Feminist Journal of Politics *This book undeniably deserves a place on your shelf and is a ‘must have’ for anyone in the academic field. * Journal of Cultural Analysis and Social Change *Table of Contents1. Introduction PART ONE - COLLECTING QUEER DATA 2. A history of queer data collection 3. Queer data in the Equality Act 4. Queer collection methods 5. Censuses 6. International approaches to queer data collection SECTION TWO - ANALYSING QUEER DATA 7. Making sense of queer data 8. Intersectional analysis SECTION THREE - USING QUEER DATA 9. Maintenance of the status quo 10. Your place to speak 11. For political action 12. Conclusion

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Book SynopsisJordan Morrow is known as the "Godfather of Data Literacy", having helped pioneer the field by building one of the world's first data literacy programs. He is the founder and CEO of Bodhi Data and the Senior Vice President of Data and AI Transformation for AgileOne, helping to utilize data and AI in the total talent management space. He served as the Chair of the Advisory Board for The Data Literacy Project and has helped companies and organizations around the world, including the United Nations, build and understand data literacy. Morrow is the author of four books: Be Data Literate, Be Data Driven, Be Data Analytical, and Business 101 for the Data Professional all published by Kogan Page. He is based near Salt Lake City, Utah.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisCrime research has grown substantially over the past decade, with a rise in evidence-informed approaches to criminal justice. The fuel that has driven this growth is data and one of its most pressing challenges is the lack of research on its use and interpretation. This accessible book closes that gap for researchers, practitioners and students.

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Book SynopsisMeasurement connects theoretical concepts to what is observable in the empirical world, and is fundamental to all social and behavioral research. In this volume, J. Micah Roos and Shawn Bauldry introduce a popular approach to measurement: Confirmatory Factor Analysis (CFA). As the authors explain, CFA is a theoretically informed statistical framework for linking multiple observed variables to latent variables that are not directly measurable. The authors begin by defining terms, introducing notation, and illustrating a wide variety of measurement models with different relationships between latent and observed variables. They proceed to a thorough treatment of model estimation, followed by a discussion of model fit. Most of the volume focuses on measures that approximate continuous variables, but the authors also devote a chapter to categorical indicators. Each chapter develops a different example (sometimes two) covering topics as diverse as racist attitudes, theological conservatism, leadership qualities, psychological distress, self-efficacy, beliefs about democracy, and Christian nationalism drawn mainly from national surveys. Data to replicate the examples are available on a companion website, along with code for R, Stata, and Mplus.Trade ReviewConfirmatory Factor Analysis is well written and easy to read, The book covers the essentials necessary for understanding and using CFA. It is appropriate for graduate students and professors new to this analysis approach. -- Jerry J. VaskeThe authors provide a masterful and fluid overview of confirmatory factor analysis that will guide readers to the best practices whether conducting their own research or evaluating the research of others. -- John HoffmannThis is a well-written and comprehensive text. -- Michael D. BidermanRoos and Bauldry lucidly set out foundations of confirmatory factor analysis (CFA) as applied in the assessment and construction of scales. Beginning with model specification, they discuss identification, estimation, and assessment of CFA models, before developing extensions to assessing measurement invariance and categorical (rather than quantitative) indicators. -- Peter V. MarsdenTable of ContentsChapter 1: Introduction Chapter 2: Model Specification Chapter 3: Identification and Estimation Chapter 4: Model Evaluation and Respecification Chapter 5: Measurement Invariance Chapter 6: Categorical Indicators Chapter 7: Conclusion Appendix: Reliability of Scales Glossary Bibliography

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Book SynopsisSpatial Data Science will show GIS scientists and practitioners how to add and use new analytical methods from data science in their existing GIS platforms. By explaining how the spatial domain can provide many of the building blocks, it''s critical for transforming data into information, knowledge, and solutions. This book is for those using or studying GIS and the computer scientists, engineers, statisticians, and information and library scientists leading the development and deployment of data science.

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Book SynopsisThis book offers an introduction and hands-on examples that demonstrate how Learning Analytics (LA) can be used to enhance digital learning, teaching and training at various levels. While the majority of existing literature on the subject focuses on its application at large corporations, this book develops and showcases approaches that bring LA closer to smaller organizations, and to educational institutions that lack sufficient resources to implement a full-fledged LA infrastructure. In closing, the book introduces a set of software tools for data analytics and visualization, and explains how they can be employed in several LA scenarios.

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Book Synopsis​This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization.This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics. Table of ContentsPART 1. INTRODUCTION AND ANALYTICS MODELS.- Retraining and Reskilling Financial Participators in the Digital Age.- Basics of Financial Data Analytics.- Predictive Analytics Techniques: Theory and Applications in Finance.- Prescriptive Analytics Techniques: Theory and Applications in Finance.- Forecasting Returns of Crypto Currency - Analyzing Robustness of Auto Regressive and Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNS).- PART 2. MACHINE LEARNING.- Machine Learning in Financial Markets: Dimension Reduction and Support Vector Machine.- Pruned Random Forests for Effective and Efficient Financial Data Analytics.- Foreign Currency Exchange Rate Prediction Using Long Short Term Memory.- Natural Language Processing (NLP) for Exploring Culture in Finance: Theory and Applications.- PART 3. TECHNOLOGY DRIVEN FINANCE.- Financial Networks: A Review of Models and the Use of Network Similarities.- Optimization of Regulatory Economic-Capital Structured Portfolios: Modeling Algorithms, Financial Data Analytics and Reinforcement Machine Learning in Emerging Markets.- Transforming Insurance Business with Data Science.- A General Cyber Hygiene Approach for Financial Analytical Environment.

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Book SynopsisTrade Review"A Choice Outstanding Academic Title of the Year""[An] intriguingly human collection of articles . . . [from] contributors, including programmers, technologists, historians, journalists and academics."---Andrew Robinson, Nature"A wonderful book. . . . The writing is clear, and you don’t need to know anything about computers to understand pretty much every line of this book. A must-read!"---Jonathan Shock, Mathemafrica"A highly relevant collection of short essays. . . . [You Are Not Expected to Understand This] is intended to develop readers' appreciation for the critical role of software in their lives." * Choice *

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Book SynopsisThe SPSS Survival Manual throws a lifeline to students and researchers grappling with this powerful data analysis software.In her bestselling guide, Julie Pallant takes you through the entire research process, helping you choose the right data analysis technique for your project. This edition has been updated to include up to SPSS version 26. From the formulation of research questions, to the design of the study and analysis of data, to reporting the results, Julie discusses basic and advanced statistical techniques. She outlines each technique clearly, with step-by-step procedures for performing the analysis, a detailed guide to interpreting data output and an example of how to present the results in a report.For both beginners and experienced users in Psychology, Sociology, Health Sciences, Medicine, Education, Business and related disciplines, the SPSS Survival Manual is an essential text. It is illustrated throughout with screen grabs, examples of output andTable of ContentsPrefaceData files and websiteIntroduction and overviewPart One Getting started1 Designing a study2 Preparing a codebook3 Getting to know IBM SPSS StatisticsPart Two Preparing the data file4 Creating a data file and entering data5 Screening and cleaning the dataPart Three Preliminary analyses6 Descriptive statistics7 Using graphs to describe and explore the data8 Manipulating the data9 Checking the reliability of a scale10 Choosing the right statisticPart Four Statistical techniques to explore relationships among variables11 Correlation12 Partial correlation13 Multiple regression14 Logistic regression15 Factor analysisPart Five Statistical techniques to compare groups16 Non-parametric statistics17 T-tests18 One-way analysis of variance19 Two-way between-groups ANOVA20 Mixed between-within subjects analysis of variance21 Multivariate analysis of variance22 Analysis of covarianceAppendix: Details of data filesRecommended readingReferencesIndex

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Book SynopsisEvery new tractor now contains built-in sensors that collect data and stream it to cloud-based infrastructure. Seed and chemical companies are using these data, and these agribusinesses are a form of big tech alongside firms like Google and Facebook.The Immaculate Conception of Data peeks behind the secretive legal agreements surrounding agricultural big data to trace how it is used and with what consequences. Agribusinesses are among the oldest oligopoly corporations in the world, and their concentration gives them an advantage over other food system actors. Kelly Bronson explores what happens when big data get caught up in pre-existing arrangements of power. Her richly ethnographic account details the work of corporate scientists, farmers using the data, and activist hackers building open-source data platforms. Actors working in private and public contexts have divergent views on whom new technology is for, how it should be developed, and what kinds of agriculture it Trade Review“In The Immaculate Conception of Data, Kelly Bronson plunges into an increasingly intricate web of precision farming, agribusiness, computerized models, data accumulation, and the current (d)evolution of modern food production. The ongoing attempt to marry traditional crop cultivation with computer science and artificial intelligence (AI) is a perplexing fusion of two very different worlds, which Bronson does an excellent job of critically analyzing. For anyone interested in gaining a critical perspective on the accelerated digitalization of the planet, as well as a better understanding of why farming is increasingly spoken of with a language and jargon that previously belonged to computer scientists and programmers, [this book] is an exceptional starting point.” Journal of Agriculture, Food Systems, and Community Development“The Immaculate Conception of Data shines in its ability to speak meaningfully to a variety of audiences from those interested in data privacy, the future of agriculture and science studies. The book also importantly reminds us that, despite their prominence, agricultural technologies and the data they collect are not immaculate. They are produced, trained and contained by agronomists and even activists. While I have been left contemplating these critical, nuanced arguments, I walked away with a practical point: Despite all the hype, data did not grow the wheat in my breakfast cereal.” Journal of Agrarian Change“Kelly Bronson’s concise and reader-friendly book constitutes a necessary warning about the risks of putting a blind faith in the promise of digitisation. Behind the book’s message lies a powerful futuristic imaginary that reproduces capitalism and its consequences—but also diminishes the critical reflectiveness of practitioners and scholars and compromises their emphasis on food justice. The prophetic, positivist aim to empower ‘raw data’ to shape reality serves economic interests eager to modify and capitalise on conventional farmers’ practices. Her call for politicising our perceptions of data is therefore salutary.” Sociologia Ruralis

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Book SynopsisFay and Brittain present statistical hypothesis testing and compatible confidence intervals, focusing on application and proper interpretation. The emphasis is on equipping applied statisticians with enough tools - and advice on choosing among them - to find reasonable methods for almost any problem and enough theory to tackle new problems by modifying existing methods. After covering the basic mathematical theory and scientific principles, tests and confidence intervals are developed for specific types of data. Essential methods for applications are covered, such as general procedures for creating tests (e.g., likelihood ratio, bootstrap, permutation, testing from models), adjustments for multiple testing, clustering, stratification, causality, censoring, missing data, group sequential tests, and non-inferiority tests. New methods developed by the authors are included throughout, such as melded confidence intervals for comparing two samples and confidence intervals associated with WilTrade Review'A necessary book for the applied statistician seeking to understand the theoretical underpinnings of statistical methods and for graduate students knowledgeable about statistical theory but lacking experience in application. The book is chock full of challenging examples that point to the complexities of choice of method. A particularly valuable feature of the book is the authors' description of competing methods coupled with their clarity in explaining and justifying why they prefer one method over others. Fay and Brittain should sit on every statistician's bookshelf.' Janet Wittes, WCG Statistics Collaborative'Good statistical hypothesis testing and confidence interval construction involves mathematical aspects of finding a good test given a probability model and scientific aspects of determining the appropriateness of a probability model for answering a scientific question. This book provides a lucid discussion of both these mathematical and scientific aspects with compelling scientific examples. I most highly recommend this book.' Dylan Small, University of Pennsylvania'Congratulations to Fay and Brittain for this wonderful reference book that does what its somewhat unusual title suggests: puts hypothesis testing in the context of science. The vast coverage of topics, extensive bibliography and notes, and easy to understand explanations make 'Statistical Hypothesis Testing in Context: Reproducibility, Inference, and Science' an indispensable tool in the arsenal of any applied or theoretical statistician or biostatistician. I enthusiastically recommend buying the book!' Michael A. Proschan, National Institute of Allergy and Infectious DiseasesTable of Contents1. Introduction; 2. Theory of tests, p-values, and confidence intervals; 3. From scientific theory to statistical hypothesis test; 4. One sample studies with binary responses; 5. One sample studies with ordinal or numeric responses; 6. Paired data; 7. Two sample studies with binary responses; 8. Assumptions and hypothesis tests; 9. Two sample studies with ordinal or numeric responses; 10. General methods for creating decision rules; 11. K-Sample studies and trend tests; 12. Clustering and stratification; 13. Multiplicity in testing; 14. Testing from models; 15. Causality; 16. Censoring; 17. Missing data; 18. Group sequential and related adaptive methods; 19. Testing fit, equivalence, and non-inferiority; 20. Power and sample size.

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Book SynopsisTaking the Fear Out of Data Analysis provides readers with the necessary knowledge and skills to understand, perform, and interpret quantitative data analysis effectively. Acknowledging that people often dislike statistics and quantitative methods, this book illustrates that statistical reasoning can be a fun and intuitive part of our lives.Key Features: Split into three sections covering how to understand data, preparing data for analysis and carrying out the analysis Blends theory with practical examples in a logical and straightforward manner to guide readers in making sense of statistical inference Offers universal knowledge that can be applied to a variety of software applications with limited technical complexity to aid the learning process Short and concise chapters focusing on the essence of the topics covered, such as analytical techniques that are typically used in behavioral and social science research Significantly revised and updated, this textbook is an essential text for both undergraduate and postgraduate students in fields such as information systems, international business and marketing. It will also be beneficial for practitioners involved in data science, data analytics, and market research.Trade Review‘Written with wry wit and incredible clarity, the authors provide the reader with a detailed understanding of seminal issues in data analysis. A masterful work that truly does “take the fear out of data analysis” – this book is a rare treat indeed.’ -- David A. Griffith, Mays Business School, Texas A&M University, US‘Written by a proficient team of authors, Taking the Fear out of Data Analysis is a fascinating … ah, forget the marketing blurb. This is a great text, you should read it! There is no doubt that you will devour this book in no time and learn a lot about statistics on the way.' -- Marko Sarstedt, Ludwig-Maximilians-University (LMU), Germany‘Statistics. I know – you hate it. It’s hard and confusing. Students of all levels find the topic hard. I tell them to get this book. And no! They cannot borrow mine, I don’t want to lose it. Diamantopoulos, Schlegelmilch and Halkias knock another one out of the park with this excellent introduction to a great array of statistical issues. They start right at the beginning – which is always a good place to start if you’re a beginner – and gently, often hilariously, and successfully guide the reader through the various learning moments that need to be negotiated if one is to become fearless in the face of columns of data. Priceless.’ -- John Cadogan, School of Business and Economics, Loughborough University, UK‘The new edition of this book provides excellent guidance to data knowledge and competence using a problem-solving approach. With the digital becoming increasingly important, analytical skills should be key competencies in everybody’s daily life. To achieve this goal, Taking the Fear out of Data Analysis is highly recommended.’ -- Zhongming Wang, Zhejiang University, China‘The significantly extended, new edition is increasingly relevant as the world of quantitative methods has kept on expanding, in part due to an explosion in software programs that scholars can use seemingly without much understanding. Do not let the light-hearted nature of this book fool you. It is a statistics book that carefully leads authors through all the necessary stages of analysis. It effortlessly explains the analysis details and assumptions that PhD examiners, journal reviewers, and conference presentation audience members insist on raising. This excellent new edition is destined to be very well thumbed.’ -- Matthew Robson, Cardiff Business School, UKTable of ContentsContents: Pre-publication reviews from around the world Introduction to Taking the Fear out of Data Analysis PART I UNDERSTANDING DATA 1. What is data (and can you do it in your sleep)? 2. Does sampling have a purpose other than providing employment for statisticians? 3. Why should you be concerned about different types of measurement? PART II PREPARING DATA FOR ANALYSIS 4. Have you cleaned your data and found the mistakes you made? 5. Why do you need to know your objective before you fail to achieve it? PART III CARRYING OUT THE ANALYSIS 6. Why not take it easy initially and describe your data? 7. Can you use few numbers in place of many to summarize your data? 8. What about using estimation to see what the population looks like? 9. How about sitting back and hypothesizing? 10. Simple things first: One variable, one sample 11. Getting experienced: Making comparisons 12. Getting adventurous: Searching for relationships 13. Getting hooked: A look into multivariate analysis 14. Getting obsessed: A further look into multivariate analysis 15 It’s all over … or is it? Index

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Book SynopsisEvery country, every subnational government, and every district has a designated population, and this has a bearing on politics in ways most citizens and policymakers are barely aware of. Population and Politics provides a comprehensive evaluation of the political implications stemming from the size of a political unit ? on social cohesion, the number of representatives, overall representativeness, particularism (''pork''), citizen engagement and participation, political trust, electoral contestation, leadership succession, professionalism in government, power concentration in the central apparatus of the state, government intervention, civil conflict, and overall political power. A multimethod approach combines field research in small states and islands with cross-country and within-country data analysis. Population and Politics will be of interest to academics, policymakers, and anyone concerned with decentralization and multilevel governance.Trade ReviewThe size of a polity is crucial to its politics. Political scientists have known this since Plato, but the impact of population size is complex because affects many political outcomes. Gerring and Veenendaal offer the authoritative account of the impact of scale by bringing together the results and ideas of a large and diverse literature with new empirical evidence on thirteen important aspects of how democracy thrives in small and large political communities. Søren Serritzlew, Aarhus UniversityScale matters in profound ways for politics. That is the conclusion of this bold, wide-ranging, data rich, and strikingly original book. The book shows empirically the extent to which size matters for dozens of outcomes ranging from cabinet size to extent of steel production. In discovering various scale effects, the authors provide new data for answering the fundamental question that intrigued the classical theorists: What is the optimal size for political communities? James Mahoney, Northwestern University'This book will shake up what you think you know about governance. Scale effects – which we rarely reflect on – turn out to be pervasive in their effects on political institutions. Bigger states are more powerful, but are they better governed? Better places to live? Gerring and Veenendaal confront these questions and more and deliver powerful findings on how polity size shapes politics.' Jack A. Goldstone, Virginia E. and John T. Hazel, Jr Professor of Public Policy and Eminent Scholar, Schar School of Policy and Government, George Mason University'… a big book on the association between population size and a wide range of political outcomes.' Michael Laver, Department of PhilosophyTable of ContentsPart I. Framework 1; 1. Scaling the Political World; 2 Approaches; Part II. Scale Effects; 3 Cohesion; 4 Representatives; 5 Representativeness; 6 Particularism; 7. Participation; 8. Contestation; 9. Institutionalized Succession; 10. Professionalism; 11. Concentration; 12, Intervention; 13.Power; 14. Civil Conflict; 15. Other Outcomes; Part III. Conclusions; 16. How Scale Matters.

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Book SynopsisData has dramatically changed how our world works. Understanding and using data is now one of the most transferable and desirable skills. Whether you're an entrepreneur wanting to boost your business, a jobseeker looking for that employable edge, or simply hoping to make the most of your current career, Confident Data Skills is here to help. This updated second edition takes you through the basics of data: from data mining and preparing and analysing your data, to visualizing and communicating your insights. It now contains exciting new content on neural networks and deep learning. Featuring in-depth international case studies from companies including Amazon, LinkedIn and Mike's Hard Lemonade Co, as well as easy-to understand language and inspiring advice and guidance, Confident Data Skills will help you use your new-found data skills to give your career that cutting-edge boost. About the Confident series... From coding and web design to data, digital content and cyber security, the Confident books are the perfect beginner's resource for enhancing your professional life, whatever your career path.Trade Review"The most comprehensive book I have seen for those wanting to get into data science - what Harvard Business Review called 'the sexiest job of the 21st century'." * Ben Taylor, Chief AI Evangelist, DataRobot *"Kirill Eremenko's book skilfully unravels the mysteries behind all the popular analytics tools and techniques, as well as many of the algorithms that power intelligent systems. I would recommend it to anyone who wants to pursue a career in data science. " * Dan Shiebler, Senior Machine Learning Engineer, Twitter Cortex *"Kirill Eremenko has come up with an amazing, unique way of making data science simple. From novices to the most experienced, anyone wanting to learn about data science will benefit from this book. Kirill covers everything from what data is and how to wrangle it, to helping you develop your own data analysis process, to effectively communicating with data. This book has it all! " * Andy Kriebel, Head Coach, The Information Lab Data School *"Eremenko is an established voice in the field, and his book is a must-read for anyone with an interest in using data science for business. Crammed with advice, Confident Data Skills provides the means to broaden one's horizons through data." * Michael Segala, CEO and Co-Founder, SFL Scientific *"Terrific. Eremenko has a knack for rendering complex theories in clear, elegant prose. Instructive and spirited, it will help you think - not only about the world around you but also about yourself." * Damian Mingle, Chief Data Scientist, Intermedix *Table of Contents Chapter - 00: Introduction; Section - ONE: "What is it?" key principles; Chapter - 01: Defining data; Chapter - 02: How data fulfils our needs; Chapter - 03: AI and our Future; Section - TWO: "When and where can I get it?" data gathering and analysis; Chapter - 04: Identify the problem; Chapter - 05: Data preparation; Chapter - 06: Data analysis (part I); Chapter - 07: Data analysis (part II); Section - THREE: "How can I present it?" communicating data; Chapter - 08: Data visualization; Chapter - 09: Data presentation; Chapter - 10: Your career in data science

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Book Synopsis*What do advanced statistical techniques do?*When is it appropriate to use them?*How are they carried out and reported?There are a variety of statistical techniques used to analyse quantitative data that masters students, advanced undergraduates and researchers in the social sciences are expected to be able to understand and undertake. This book explains these techniques, when it is appropriate to use them, how to carry them out and how to write up the results. Most books which describe these techniques do so at too advanced or technical a level to be readily understood by many students who need to use them. In contrast the following features characterise this book:- concise and accessible introduction to calculating and interpreting advanced statistical techniques- use of a small data set of simple numbers specifically designed to illustrate the nature and manual calculation of the most important statistics in each technique- succinct illustration of wrTable of ContentsSeries editor’s foreword Preface 1 IntroductionPART 1Grouping quantitative variables together2 Exploratory factor analysis3 Confirmatory factor analysis4 Cluster analysisPART 2Explaining the variance of a quantitative variable5 Stepwise multiple regression6 Hierarchical multiple regressionPART 3Sequencing the relationships between three or more quantitativevariables7 Path analysis assuming no measurement error8 Path analysis accounting for measurement error PART 4Explaining the probability of a dichotomous variable 9 Binary logistic regression PART 5Testing differences between group means 10 An introduction to analysis of variance and covariance 11 Unrelated one-way analysis of covariance 12 Unrelated two-way analysis of variance PART 6Discriminating between groups 13 Discriminant analysisPART 7Analysing frequency tables with three or more qualitative variables 14 Log-linear analysis Glossary References Index

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisThis book provides thorough and comprehensive coverage of most of the new and important quantitative methods of data analysis for graduate students and practitioners. In recent years, data analysis methods have exploded alongside advanced computing power, and it is critical to understand such methods to get the most out of data, and to extract signal from noise. The book excels in explaining difficult concepts through simple explanations and detailed explanatory illustrations. Most unique is the focus on confidence limits for power spectra and their proper interpretation, something rare or completely missing in other books. Likewise, there is a thorough discussion of how to assess uncertainty via use of Expectancy, and the easy to apply and understand Bootstrap method. The book is written so that descriptions of each method are as self-contained as possible. Many examples are presented to clarify interpretations, as are user tips in highlighted boxes.Trade Review'This text is suitable for undergraduates and graduates, as well as seasoned scientists and engineers seeking to broaden their statistical skills. It will have lasting value as it is comprehensive, containing detailed explanations of a wide range of statistical methods. The book is clearly written by a meticulous scientist who is an expert in the field and an award winning teacher.' James Hays, Columbia University, New York'At last: a guide for getting the most out of your data analysis while avoiding the many pitfalls, hazards and common mistakes. This book is an invaluable and inspired opus on the fundamentals of quantitative data analysis. It is both comprehensive and illuminating, with many a nugget of enlightened wisdom, as well as succinctly summarized 'take-home' points in each and every section. A very accessible must-have guide for exploring data in the most informed way, and a gem of a textbook for students, teachers and practitioners alike.' Sharon Stammerjohn, University of Colorado, Boulder'Coherent book-length treatments are so valuable in the Data Age: the internet is full of algorithms - but described flatly, and in myriad notations and nomenclatures. This long-time teacher's lucid text expresses the spirit and strategy of data analysis, as well as the details. Boxes set off optional advanced derivations, appendices survey matrix algebra and uncertainty analysis, and the chapters aim for standalone readability, making this a valuable reference as well as a flexible textbook (with questions). Spectral estimation is especially well covered.' Brian Mapes, University of Miami'This is a competent development of many data analysis methods … Overall, the book is the outgrowth of teaching the subject for 30 years, which shows in the well-developed, clear narrative descriptions accompanying the theory.' D. A. Vaccari, ChoiceTable of ContentsPart I. Fundamentals: 1. The nature of data and analysis; 2. Probability theory; 3. Statistics; Part II. Fitting Curves to Data; 4. Interpolation; 5. Smoothed curve fitting; 6. Special curve fitting; Part III. Sequential Data Fundamentals: 7. Serial products; 8. Fourier series; 9. Fourier transform; 10. Fourier sampling theory; 11. Spectral analysis; 12. Cross spectral analysis; 13. Filtering and deconvolution; 14. Linear parametric models; 15. Empirical orthogonal function (EOF) analysis; A1. Overview of matrix algebra; A2. Uncertainty analysis; References; Index.

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Book SynopsisThis textbook presents an innovative new perspective on the economics of development, including insights from a broad range of disciplines. It starts with the current state of affairs, a discussion of data availability, reliability, and analysis, and an historic overview of the deep influence of fundamental factors on human prosperity. Next, it focuses on the role of human interaction in terms of trade, capital, and knowledge flows, as well as the associated implications for institutions, contracts, and finance. The book also highlights differences in the development paths of emerging countries in order to provide a better understanding of the concepts of development and the Millennium Development Goals. Insights from other disciplines are used help to understand human development with regard to other issues, such as inequalities, health, demography, education, and poverty. The book concludes by emphasizing the importance of connections, location, and human interaction in determining fTrade Review'This is a masterful textbook on development. It extensively discusses the root causes of development, and more recent topics such as randomized controlled trials. The book stands out by also providing a rich discussion of 'international' issues relevant for development, such as globalization, international trade, migration, and international financial flows.' Robert Lensink, University of Groningen'A splendid new textbook by van Marrewijk and Brakman! Their lucid exposition is wide ranging, deeply informed and up to date. The student will acquire a broad knowledge of developing and emerging economies and, more importantly, understand the data, theories, and methods that inform the authors' insights.' Donald Davis, Columbia University'This excellent new textbook on development economics is up to date in its coverage of research - history, data, and theories. It explains difficult concepts simply and clearly. The visual presentation - figures, charts, and use of color - is outstanding. It is balanced and thoughtful in its assessment of the issues and policies. A treasure for students and teachers alike.' Avinash Dixit, Princeton UniversityTable of ContentsPart I. Introduction and Deep Roots: 1. Economic Development Today; 2. Data and Methods; 3. Uneven Playing Field; 4. Geo-Human Interaction; Part II. Human Interaction: 5. Globalization and Development; 6. International Trade; 7. Economic Growth; 8. Institutions and Contracts; 9. Money and Finance; Part III. Human Development: 10. Poverty, Inequality, and Gender; 11. Poor Economics; 12. Population and Migration; 13. Education; 14. Health; Part IV. Connections and Interactions: 15. Agriculture and Development; 16. Urbanization and Agglomeration; 17. Geographical Economics and Development; 18. Heterogeneous and Multinational Firms; 19. Sustainability and Development.

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Book SynopsisHow do planetary scientists analyze and interpret data from laboratory, telescopic, and spacecraft observations of planetary surfaces? What elements, minerals, and volatiles are found on the surfaces of our Solar System''s planets, moons, asteroids, and comets? This comprehensive volume answers these topical questions by providing an overview of the theory and techniques of remote compositional analysis of planetary surfaces. Bringing together eminent researchers in Solar System exploration, it describes state-of-the-art results from spectroscopic, mineralogical, and geochemical techniques used to analyze the surfaces of planets, moons, and small bodies. The book introduces the methodology and theoretical background of each technique, and presents the latest advances in space exploration, telescopic and laboratory instrumentation, and major new work in theoretical studies. This engaging volume provides a comprehensive reference on planetary surface composition and mineralogy for advancTrade Review'… provides a thoroughly updated entry point to the field, covering the techniques used on missions from Mercury to Pluto and almost everywhere in between … Researchers will appreciate the copious end-of-chapter references (chapter 3 alone provides 132 citations). Though the density of information may be intimidating to novices, libraries supporting graduate astronomy programs should certainly acquire this book.' S. G. Decker, ChoiceTable of ContentsPart I. Theory of Remote Compositional Analysis Techniques and Laboratory Measurements: 1. Electronic spectra of minerals in the visible and near-infrared regions; 2. Theory of reflectance and emittance spectroscopy of geologic materials in the visible and infrared regions; 3. Mid-IR (thermal) emission and reflectance spectroscopy: laboratory spectra of geologic materials; 4. Visible and near-infrared reflectance spectroscopy: laboratory spectra of geologic materials; 5. Visible and infrared spectroscopy of ices, volatiles and organics; 6. Raman spectroscopy: theory and laboratory spectra of geologic materials; 7. Mössbauer spectroscopy: theory and laboratory spectra of geologic materials; 8. Laser-induced breakdown spectroscopy: theory and laboratory spectra of geologic materials; 9. Fundamentals of neutron, X-ray and gamma ray spectroscopy; 10. Radar remote sensing: theory and applications; Part II. Terrestrial Field and Airborne Applications: 11. Visible and near-infrared reflectance spectroscopy: field and airborne measurements; 12. Raman spectroscopy: field measurements; Part III. Analysis Methods: 13. Effects of environmental conditions on spectral measurements; 14. Hyper- and multispectral VNIR imaging analysis; 15. Thermal infrared spectral modeling; 16. Geochemical interpretations using multiple remote datasets; Part IV. Applications to Planetary Surfaces: 17. Spectral analyses of Mercury; 18. Compositional analysis of the Moon from the visible and near-infrared; 19. Spectral analyses of asteroids; 20. VIS-NIR spectral analyses of asteroids and comets from Dawn and Rosetta; 21. Spectral analyses of Saturn's moons using Cassini-VIMS; 22. Spectroscopy of Pluto and its satellites; 23. VSWIR spectral analyses of Mars from orbit using CRISM and OMEGA; 24. Thermal infrared spectral analyses of Mars from orbit using TES and THEMIS; 25. Rover-based thermal infrared remote sensing of Mars using the mini-TES instrument; 26. Compositional and mineralogic analyses of Mars using multispectral imaging on the Mars Exploration Rover, Phoenix, and Mars Science Laboratory Missions; 27. Iron mineralogy, oxidation state, and alteration on Mars from Mössbauer spectroscopy at Gusev Crater and Meridiani Planum; 28. Elemental analyses of Mars using APXS; 29. Elemental analyses of Mars with LIBS by ChemCam and SuperCam; 30. X-ray, gamma-ray, and neutron spectroscopy: planetary missions; 31. Radar remote sensing of planetary bodies.

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Book SynopsisThis book integrates philosophy of science, data acquisition methods, and statistical modeling techniques to present readers with a forward-thinking perspective on clinical science. It reviews modern research practices in clinical psychology that support the goals of psychological science, study designs that promote good research, and quantitative methods that can test specific scientific questions. It covers new themes in research including intensive longitudinal designs, neurobiology, developmental psychopathology, and advanced computational methods such as machine learning. Core chapters examine significant statistical topics, for example missing data, causality, meta-analysis, latent variable analysis, and dyadic data analysis. A balanced overview of observational and experimental designs is also supplied, including preclinical research and intervention science. This is a foundational resource that supports the methodological training of the current and future generations of clinicTrade Review'The editors have produced an indispensable tome. For the first time, the various methods and approaches used by clinical psychology researchers have been brought together. This book represents a watershed moment in the development of clinical psychology as a scientific discipline and belongs on the bookshelves of all clinical psychologists.' Robert F. Krueger, Distinguished McKnight University Professor, University of Minnesota'This handbook provides a much-needed primer on a range of methods important for clinical science. It would be an excellent addition to a graduate introductory course on methods in clinical science or a very useful resource for more advanced researchers wanting to learn about new approaches and methods in the field.' Deanna M. Barch, Chair and Professor of Psychological and Brain Sciences, Washington University, St Louis'Assembled by two highly respected clinical scientists, this handbook is current, comprehensive, and sophisticated. Researchers across a wide spectrum of experience will find this volume invaluable to their work. Written by leading experts, the chapters discuss methodological and quantitative approaches to address clinical psychology's most pressing questions.' Josh Miller, Professor and Director of Clinical Training, University of Georgia'This comprehensive treatment of the cutting-edge methods and procedures used in the rapidly evolving field of clinical research should be a go-to resource for anyone interested in the state-of-the-art in the discipline of clinical psychology. Topics as diverse as latent variable models, molecular genetics, functional imaging, and the replication crisis all receive clear and detailed consideration.' Leslie C. Morey, George T. and Gladys H. Abell Professor, Texas A&M UniversityTable of ContentsSection I. Clinical Psychological Science: An Evolving Field: 1. Trends in the evolving discipline of clinical psychology; 2. Defining and refining phenotypes: operational definitions as open concepts; 3. Building models of psychopathology spanning multiple modalities of measurements; Section II. Observational Approaches: 4. The conceptual foundations of descriptive psychopathology; 5. Survey and interview methods; 6. Psychometrics in clinical psychology research; 7. Latent variable models in clinical psychology; 8. Psychiatric epidemiology methods; Section III. Experimental and Biological Approaches: 9. Conceptual foundations of experimental psychopathology: historical context, scientific posture, and reflections on substantive and method matters; 10. A practical guide for designing and conducting cognitive studies in child psychopathology; 11. Peripheral psychophysiology; 12. Behavioral and molecular genetics; 13. Concepts and principles of clinical functional magnetic resonance imaging; 14. Reinforcement learning approaches to computational clinical neuroscience; Section IV. Developmental Psychopathology and Longitudinal Methods: 15. Studying psychopathology in early life: foundations of developmental psychopathology; 16. Adolescence and puberty: understanding the emergency of psychopathology; 17. Quantitative genetics research strategies for studying gene-environment interplay in the development of child and adolescent psychopathology; 18. Designing and managing longitudinal studies; 19. Measurement and comorbidity models for longitudinal data; Section V. Intervention Approaches: 20. The multiphase optimization strategy for developing and evaluating behavioral interventions; 21. Future directions in developing and evaluating psychological interventions; 22. Health psychology and behavioral medicine: methodological issues in the study of psychosocial influences on disease; Section VI. Intensive Longitudinal Designs: 23. Ambulatory assessment; 24. Modeling intensive longitudinal data; 25. Modeling the individual: bridging nomothetic and idiographic levels of analysis; 26. Social processes and dyadic designs; 27. Models for dyadic data; Section VII. General Analytic Considerations: 28. Reproducibility in clinical psychology; 29. Meta-analysis: integration of empirical findings through quantitative modeling; 30. Mediation, moderation, and conditional process analysis: regression-based approaches for clinical research; 31. Statistical inference for causal effects in clinical psychology: fundamental concepts and analytical approaches; 32. Analyzing nested data: multilevel modeling and alternative approaches; 33. Missing data analyses; 34. Machine learning for clinical psychology and clinical neuroscience.

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Book SynopsisThe increased and widespread availability of large network data resources in recent years has resulted in a growing need for effective methods for their analysis. The challenge is to detect patterns that provide a better understanding of the data. However, this is not a straightforward task because of the size of the data sets and the computer power required for the analysis. The solution is to devise methods for approximately answering the questions posed, and these methods will vary depending on the data sets under scrutiny. This cutting-edge text introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical data sets, using a wide range of real-life examples. Bringing together leading experts, this text provides an ideal introduction to and insight into the interdisciplinary field of network daTable of Contents1. From genetic data to medicine: from DNA samples to disease risk prediction in personalized genetic tests Luis Leal, Rok Košir and Nataša Pržulj; 2. Epigenetic data and disease Rodrigo González-Barrios, Marisol Salgado-Albarrán, Nicolás Alcaraz, Cristian Arriaga-Canon, Lissania Guerra-Calderas, Laura Contreras-Espinoza and Ernesto Soto-Reyes; 3. Introduction to graph and network theory Thomas Gaudelet and Nataša Pržulj; 4. Protein-protein interaction data, their quality, and major public databases Anne-Christin Hauschild, Chiara Pastrello, Max Kotlyar and Igor Jurisica; 5. Graphlets in network science and computational biology Khalique Newaz and Tijana Milenković; 6. Cluster analysis Richard Röttger; 7. Machine learning for data integration in cancer precision medicine: matrix factorization approaches Noël Malod-Dognin, Sam Windels and Nataša Pržulj; 8. Machine learning for biomarker discovery: significant pattern mining F. Llinares-Lopez and K. Borgwardt; 9. Network alignment Noël Malod-Dogning and Nataša Pržulj; 10. Network medicine Pisanu Buphamalai, Michael Caldera, Felix Müller and Jörg Menche; 11. Elucidating genotype-to-phenotype relationships via analyzes of human tissue interactomes Idan Hekselman, Moran Sharon, Omer Basha and Esti Yeger-Lotem; 12. Network neuroscience Alberto Cacciola, Alessandro Muscoloni and Carlo Vittorio Cannistraci; 13. Cytoscape: tool for analyzing and visualizing network data John H. Morris; 14. Analysis of the signatures of cancer stem cells in malignant tumours using protein interactomes and STRING database Krešimir Pavelić, Marko Klobučar, Dolores Kuzelj, Nataša Pržulj and Sandra Kraljević Pavelić.

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Book SynopsisA new and important contribution to the re-emergent field of comparative anthropology, this book argues that comparative ethnographic methods are essential for more contextually sophisticated accounts of a number of pressing human concerns today. The book includes expert accounts from an international team of scholars, showing how these methods can be used to illuminate important theoretical and practical projects. Illustrated with examples of successful inter-disciplinary projects, it highlights the challenges, benefits, and innovative strategies involved in working collaboratively across disciplines. Through its focus on practical methodological and logistical accounts, it will be of value to both seasoned researchers who seek practical models for conducting their own cutting-edge comparative research, and to teachers and students who are looking for first-person accounts of comparative ethnographic research.Trade Review'Comparison is almost as fundamental to the human mind as air and water is to the body. It is therefore puzzling and paradoxical that anthropology, which was founded as an explicitly comparative discipline, has often been ambivalent, reluctant and even hostile to comparative research. This extremely timely book reinstates comparison as a key element in anthropological theory and methodology, demonstrating a variety in comparative strategies which reflect the diversity of anthropology itself and, indeed, the human world. Highly recommended.' Thomas Hylland Eriksen, University of Oslo'This book engages with a welcome and timely project: restoring comparative perspectives to anthropology. By exploring the challenges, dimensions, and complexities of comparative methodologies, it illustrates how critical comparisons can inform theory and illuminate underlying political economic and institutional processes.' Nina Glick Schiller, Max Planck Institute for Social AnthropologyTable of ContentsIntroduction. Comparative ethnography: its promise, process, and successful implementations Edward D. Lowe and Michael Schnegg; Part I. Binary Comparisons: 1. Thinking with comparison in the anthropology/historical anthropology of migration Caroline B. Brettell; 2. Comparing tangerines: Dorothy Lee and the search for an authentic individualism Richard Handler; 3. A comparative ethnographic study of suicide epidemics in two Pacific Island societies Edward D. Lowe; Part II. Regional Comparisons: 4. The comparison of structures and the comparison of systems: Lévi-Strauss, Dumont, Luhmann Guido Sprenger; 5. Regional comparison in historical anthropology: three case examples from South Arabia Andre Gingrich; 6. Scaling ethnography up Michael Schnegg; Part III. Distant and Fluid Comparisons: 7. Best, worst, and good enough: lessons learned from multi-sited comparative ethnography Jennifer S. Hirsch, Holly Wardlow, Daniel Jordan Smith, Harriet Phinney, Shanti Parikh and Constance A. Nathanson; 8. Research across cultures and disciplines: methodological challenges in an interdisciplinary and comparative research project on emotion socialization Birgitt Röttger-Rössler; 9. Global sport industries, comparison, and economics of scales Niko Besnier and Daniel Guinness.

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Book SynopsisNetwork thinking and network analysis are rapidly expanding features of ecological research. Network analysis of ecological systems include representations and modelling of the interactions in an ecosystem, in which species or factors are joined by pairwise connections. This book provides an overview of ecological network analysis including generating processes, the relationship between structure and dynamic function, and statistics and models for these networks. Starting with a general introduction to the composition of networks and their characteristics, it includes details on such topics as measures of network complexity, applications of spectral graph theory, how best to include indirect species interactions, and multilayer, multiplex and multilevel networks. Graduate students and researchers who want to develop and understand ecological networks in their research will find this volume inspiring and helpful. Detailed guidance to those already working in network ecology but looking Trade Review'Recommended.' M. P. Gustafson, Choice MagazineTable of ContentsPreface; 1. Ecological Processes and Network Systems; 2. Structural Properties of Networks; 3. Quantitative Analysis of Dynamic Networks; 4. Multi-layer, -type, and -level Networks; 5. Tying it all together: Summary and Synthesis.

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Book SynopsisExperts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science.Trade Review'There are only a few industries that generate an equally large amount of data with a comparable variety, and societal importance. Data analytics is thus rightfully at the heart of modern power systems operations and planning. Focusing on applications in power systems, this book gives an excellent account of recent developments and of the broad range of algorithms and tools in the area of data analytics, as well as of the applications of these tools for solving challenging problems from a novel angle. Covering a wide range of fundamental problems, from state estimation to load scheduling and anomaly detection, the book is not only an excellent source of inspiration, but can also serve as an extensive reference for the gamut of operational problems faced in the power industry.' György Dán, KTH Royal Institute of Technology'The editors have brought together leading researchers at the intersection of data analytics and power systems to provide us with an authoritative reference that is comprehensive, coherent and timely. It treats classical topics such as state estimation, optimal power flow, and anomaly identification, as well as emerging topics such as phase measurement unit data recovery and privacy, probabilistic price forecasting, and distributed load management. It introduces a wide array of modern techniques to power system analysis from sparse representation, graph signal processing, distributed and feedback optimization, statistics and random matrix theory, deep learning, and mean field games. A useful reference for students, researchers, and practitioners.' Steven Low, CaltechTable of ContentsIntroduction; Preface Ali Tajer, Samir M. Perlaza and H. Vincent Poor; 1. Learning power grid topologies Guido Cavraro, Vassilis Kekatos, Liang Zhang and Georgios B. Giannakis; 2. Probabilistic forecasting of power system and market operations Yuting Ji, Lang Tong and Weisi Deng; 3. Deep learning in power systems Yue Zhao and Baosen Zhang; 4. Estimating the system state and network model errors Ali Abur, Murat Gol and Yuzhang Lin; 5. Quickest detection and isolation of tranmission line outages Venugopal V. Veeravalli and Alejandro Dominguez-Garcia; 6. Active sensing for quickest anomaly detection Ali Tajer and Javad Heydari; 7. Random matrix theory for analyzing spatio-temporal data Robert Qiu, Xing He, Lei Chu and Xin Shi; 8. Graph-theoretic analysis of power grid robustness Dorcas Ofori-Boateng, Asim Kumer Dey, Yulia R. Gel and H. Vincent Poor; 9. Bayesian attacks Inaki Esnaola, Samir M Perlaza and Ke Sun; 10. Smart meter data privacy Giulio Giaconia, Deniz Gunduz and H. Vincent Poor; 11. Data quality and privacy enhancement Meng Wang and Joe H Chow; 12. Frequency estimation using voltage phasor angles revisited Danilo P. Mandic, Sithan Kanna, Yili Xia and Anthony G. Constantinides; 13. Graph signal processing for the power grid Anna Scaglione, Raksha Ramakrishna and Mahdi Jamei; 14. A sparse representation approach for anomaly identification Hao Zhu and Chen Chen; 15. Uncertainty-aware power systems operation Daniel Bienstock; 16. Distributed optimization for power and energy systems Emiliano Dall'Anese and Nikolaos Gatsis; 17. Distributed load management Changhong Zhao, Vijay Gupta and Ufuk Topcu; 18. Analytical models for emerging energy storage applications I. Safak Bayram and Michael Devetsikiotis; 19. Distributed power consumption scheduling Samson Lasaulce, Olivier Beaude and Mauricio Gonz´alez; 20. Electric vehicles and mean-field Dario Bauso and Toru Namerikawa; 21. Prosumer behaviour: decision making with bounded horizon Mohsen Rajabpour, Arnold Glass, Robert Mulligan and Narayan B. Mandayam; 22. Storage allocation for price volatility management in electricity markets Amin Masoumzadeh, Ehsan Nekouei and Tansu Alpcan.

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Book SynopsisWhilst a great deal of progress has been made in recent decades, concerns persist about the course of the social sciences. Progress in these disciplines is hard to assess and core scientific goals such as discovery, transparency, reproducibility, and cumulation remain frustratingly out of reach. Despite having technical acumen and an array tools at their disposal, today''s social scientists may be only slightly better equipped to vanquish error and construct an edifice of truth than their forbears who conducted analyses with slide rules and wrote up results with typewriters. This volume considers the challenges facing the social sciences, as well as possible solutions. In doing so, we adopt a systemic view of the subject matter. What are the rules and norms governing behavior in the social sciences? What kinds of research, and which sorts of researcher, succeed and fail under the current system? In what ways does this incentive structure serve, or subvert, the goal of scientific progrTrade ReviewSocial science is simultaneously more successful and more troubled than ever before. This welcome collection of essays, on different aspects of the social structure of social science, is helpful for understanding what's gone wrong and how we can do better. Andrew Gelman, Professor of Statistics and Political Science, Columbia UniversityMany of society's biggest challenges and greatest opportunities depend on understanding social behavior. With such challenges in mind, contributors to this volume describe a systemic approach to social science knowledge production that is simultaneously level-headed and visionary. The book not only develops diverse and dynamic conceptions of what researchers can “know”, but also offers cogent advice about what institutions can do to increase the value of such knowledge. The stakes inherent in understanding human behavior are high. The service that social science can provide to society is great. For those who seek to contribute to society by energizing and advancing social science research, this book is a vital reference. Arthur Lupia, Hal R Varian Collegiate Professor, University of MichiganTable of Contents1. Introduction John Gerring, James Mahoney and Colin Elman; Part I. Discovery: 2. Exploratory Research Richard Swedberg; 3. Research Cycles Evan Lieberman; Part II. Publishing: 4. Peer Review Tim Liao; 5. Length Limits John Gerring and Lee Cojocaru; Part III. Transparency and Reproducibility: 6. Transparency and Reproducibility: Conceptualizing the Problem Garret Christensen and Edward Miguel; 7. Transparency and Reproducibility: Potential Solutions Garret Christensen and Edward Miguel; 8. Making Research Data Accessible Diana Kapiszewski, Sebastian Karcher; 9. Pre-registration and Results-Free Review in Observational and Qualitative Research Alan M. Jacobs; Part IV. Appraisal: 10. Replication for Quantitative Research Jeremy Freese and David Peterson; 11. Measurement Replication in Qualitative and Quantitative Studies Dan Reiter; 12. Reliability of Inference: Analogs of Replication in Qualitative Research Tasha Fairfield and Andrew Charman; 13. Coordinating Reappraisals John Gerring; 14. Comprehensive Appraisal John Gerring; 15. Impact Metrics John Gerring, Sebastian Karcher and Brendan Apfeld; Part V. Diversity: 16. Gender Diversity Dawn Teele; 17. Ideological Diversity Neil Gross and Christopher Robertson; VI. Conclusion: 18. Proposals John Gerring, James Mahoney and Colin Elman

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Book SynopsisDiscover this multi-disciplinary and insightful work, which integrates machine learning, edge computing, and big data. Presents the basics of training machine learning models, key challenges and issues, as well as comprehensive techniques including edge learning algorithms, and system design issues. Describes architectures, frameworks, and key technologies for learning performance, security, and privacy, as well as incentive issues in training/inference at the network edge. Intended to stimulate fruitful discussions, inspire further research ideas, and inform readers from both academia and industry backgrounds. Essential reading for experienced researchers and developers, or for those who are just entering the field.Table of Contents1. Introduction; 2. Preliminary; 3. Fundamental Theory and Algorithms of Edge Learning; 4. Communication-Efficient Edge Learning; 5. Computation Acceleration; 6. Efficient Training with Heterogeneous Data Distribution; 7. Security and Privacy Issues in Edge Learning Systems; 8. Edge Learning Architecture Design for System Scalability; 9. Incentive Mechanisms in Edge Learning Systems; 10. Edge Learning Applications.

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Book SynopsisA comprehensive introduction and teaching resource for state-of-the-art Qualitative Comparative Analysis (QCA) using R software. This guide facilitates the efficient teaching, independent learning, and use of QCA with the best available software, reducing the time and effort required when encountering not just the logic of a new method, but also new software. With its applied and practical focus, the book offers a genuinely simple and intuitive resource for implementing the most complete protocol of QCA. To make the lives of students, teachers, researchers, and practitioners as easy as possible, the book includes learning goals, core points, empirical examples, and tips for good practices. The freely available online material provides a rich body of additional resources to aid users in their learning process. Beyond performing core analyses with the R package QCA, the book also facilitates a close integration with the R package SetMethods allowing for a host of additional protocols forTrade ReviewIn a relatively short, clear, and well-written textbook, the authors cover all the essentials of QCA. It includes all the current practices and developments that one needs to do a complete QCA analysis. I am using it in my QCA class and I think the students will like it. Gary Goertz, Kroc Institute for International Peace Studies, University of Notre DameThis book is simply a must for anyone aiming at exploiting 'hands on' the distinctive analytic leverage of QCA, via the vast possibilities of the R environment. Benoît Rihoux, University of Louvain and COMPASSS global network (compasss.org)Every now and then, a book comes along of which you think 'why wasn't this around when I was learning this stuff?'. Because sometimes, you just want to learn from the best. The authors have written an excellent guide for both first-time users and experienced QCA scholars: robust, powerful, and a must-read. Bart Cambré, Antwerp Management SchoolEasily accessible and loaded with a wealth of examples, this important book by Oana, Schneider, and Thomann provides an expert guide to the set-analytic perspective. The integration of concepts and research strategies with the R software package makes their approach particularly successful. I expect it will quickly become the standard introduction to QCA. Peer C. Fiss, University of Southern CaliforniaSocial scientists interested in QCA should not miss this book. Oana, Schneider, and Thomann offer an introduction to QCA that is friendly, up to date, and technically advanced. The volume covers set relations, calibration of “crisp” and “fuzzy” sets, necessary conditions, sufficient configurations, temporality, advanced diagnostics, and post-QCA tools. Each chapter contains intuitive examples, advanced tips, and detailed implementation instructions in R. Aníbal Pérez-Liñán , University of Notre DameTable of ContentsPart I. Getting started: Introduction: QCA in a nutshell; Part II. Before the analytic moment: 2. Calibrating and combining sets; Part III. During the analytic moment: 3. Necessary conditions; 4. Sufficient conditions; Part IV. After the analytic moment: 5. Rounding up solid a QCA; 6. Post-QCA tools; 7. Summary and outlook.

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Book SynopsisThis book integrates philosophy of science, data acquisition methods, and statistical modeling techniques to present readers with a forward-thinking perspective on clinical science. It reviews modern research practices in clinical psychology that support the goals of psychological science, study designs that promote good research, and quantitative methods that can test specific scientific questions. It covers new themes in research including intensive longitudinal designs, neurobiology, developmental psychopathology, and advanced computational methods such as machine learning. Core chapters examine significant statistical topics, for example missing data, causality, meta-analysis, latent variable analysis, and dyadic data analysis. A balanced overview of observational and experimental designs is also supplied, including preclinical research and intervention science. This is a foundational resource that supports the methodological training of the current and future generations of clinicTrade Review'The editors have produced an indispensable tome. For the first time, the various methods and approaches used by clinical psychology researchers have been brought together. This book represents a watershed moment in the development of clinical psychology as a scientific discipline and belongs on the bookshelves of all clinical psychologists.' Robert F. Krueger, Distinguished McKnight University Professor, University of Minnesota'This handbook provides a much-needed primer on a range of methods important for clinical science. It would be an excellent addition to a graduate introductory course on methods in clinical science or a very useful resource for more advanced researchers wanting to learn about new approaches and methods in the field.' Deanna M. Barch, Chair and Professor of Psychological and Brain Sciences, Washington University, St Louis'Assembled by two highly respected clinical scientists, this handbook is current, comprehensive, and sophisticated. Researchers across a wide spectrum of experience will find this volume invaluable to their work. Written by leading experts, the chapters discuss methodological and quantitative approaches to address clinical psychology's most pressing questions.' Josh Miller, Professor and Director of Clinical Training, University of Georgia'This comprehensive treatment of the cutting-edge methods and procedures used in the rapidly evolving field of clinical research should be a go-to resource for anyone interested in the state-of-the-art in the discipline of clinical psychology. Topics as diverse as latent variable models, molecular genetics, functional imaging, and the replication crisis all receive clear and detailed consideration.' Leslie C. Morey, George T. and Gladys H. Abell Professor, Texas A&M UniversityTable of ContentsSection I. Clinical Psychological Science: An Evolving Field: 1. Trends in the evolving discipline of clinical psychology; 2. Defining and refining phenotypes: operational definitions as open concepts; 3. Building models of psychopathology spanning multiple modalities of measurements; Section II. Observational Approaches: 4. The conceptual foundations of descriptive psychopathology; 5. Survey and interview methods; 6. Psychometrics in clinical psychology research; 7. Latent variable models in clinical psychology; 8. Psychiatric epidemiology methods; Section III. Experimental and Biological Approaches: 9. Conceptual foundations of experimental psychopathology: historical context, scientific posture, and reflections on substantive and method matters; 10. A practical guide for designing and conducting cognitive studies in child psychopathology; 11. Peripheral psychophysiology; 12. Behavioral and molecular genetics; 13. Concepts and principles of clinical functional magnetic resonance imaging; 14. Reinforcement learning approaches to computational clinical neuroscience; Section IV. Developmental Psychopathology and Longitudinal Methods: 15. Studying psychopathology in early life: foundations of developmental psychopathology; 16. Adolescence and puberty: understanding the emergency of psychopathology; 17. Quantitative genetics research strategies for studying gene-environment interplay in the development of child and adolescent psychopathology; 18. Designing and managing longitudinal studies; 19. Measurement and comorbidity models for longitudinal data; Section V. Intervention Approaches: 20. The multiphase optimization strategy for developing and evaluating behavioral interventions; 21. Future directions in developing and evaluating psychological interventions; 22. Health psychology and behavioral medicine: methodological issues in the study of psychosocial influences on disease; Section VI. Intensive Longitudinal Designs: 23. Ambulatory assessment; 24. Modeling intensive longitudinal data; 25. Modeling the individual: bridging nomothetic and idiographic levels of analysis; 26. Social processes and dyadic designs; 27. Models for dyadic data; Section VII. General Analytic Considerations: 28. Reproducibility in clinical psychology; 29. Meta-analysis: integration of empirical findings through quantitative modeling; 30. Mediation, moderation, and conditional process analysis: regression-based approaches for clinical research; 31. Statistical inference for causal effects in clinical psychology: fundamental concepts and analytical approaches; 32. Analyzing nested data: multilevel modeling and alternative approaches; 33. Missing data analyses; 34. Machine learning for clinical psychology and clinical neuroscience.

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Book SynopsisIn order to assist a hospital in managing its resources and patients, modelling the length of stay is highly important. Recent health scholarship and practice has largely remained empirical, dwelling on primary data. This is critically important, first, because health planners generally rely on data to establish trends and patterns of disease burden at national or regional level. Secondly, epidemiologists depend on data to investigate possible risk factors of the disease. Yet the use of routine or secondary data has, in recent years, proved increasingly significant in such endeavours. Various units within the health systems collected such data primarily as part of the process for surveillance, monitoring and evaluation. Such data is sometimes periodically supplemented by population-based sample survey datasets. Thirdly, coupled with statistical tools, public health professionals are able to analyze health data and breathe life into what may turn out to be meaningless data. The main focus of this book is to present and showcase advanced modelling of routine or secondary survey data. Studies demonstrate that statistical literacy and knowledge are needed to understand health research outputs. The advent of user-friendly statistical packages combined with computing power and widespread availability of public health data resulted in more reported epidemiological studies in literature. However, analysis of secondary data, has some unique challenges. These are most widely reported health literature, so far has failed to recognize resulting in inappropriate analysis, and erroneous conclusions. This book presents the application of advanced statistical techniques to real examples emanating from routine or secondary survey data. These are essentially datasets in which the two editors have been involved, demonstrating how to tackle these challenges. Some of these challenges are: the complex sampling design of the surveys, the hierarchical nature of the data, the dependence of data at the sampled cluster and missing data among many more challenges. Using data from the Health Management Information System (HMIS), and Demographic and Health Survey (DHS), we provide various approaches and techniques of dealing with data complexity, how to handle correlated or clustered data. Each chapter presents an example code, which can be used to analyze similar data in R, Stata or SPSS. To make the book more concise, we have provided the codes on the book's website. The book considers four main topics in the field of health sciences research: (i) structural equation modeling; (ii) spatial and spatio-temporal modeling; (iii) correlated or clustered copula modeling; and (iv) survival analysis. The book has potential to impact methodologists, including students undertaking Master's or Doctoral level programmes as well as other researchers seeking some related reference on quantitative analysis in public health or health sciences or other areas where data of similar nature would be applicable. Further the book can be a resource to public health professionals interested in quantitative approaches to answer questions of epidemiological nature. Each chapter starts with a motivating background, review of statistical methods, analysis and results, ending discussion and possible recommendations.Table of ContentsPrefaceAnalysis and Modelling of Complex Secondary Data: An Overview of Methodological Issues and ChallengesA Mixed Discrete-Time Survival Analysis of Length of Hospitalization: Applications to Malaria Admissions among Peadiatric Children in MalawiBivariate Model of Health Seeking Behaviour among Women for Their Under-Five Children with FeverMover-Stayer Model on Future Contraceptive Use among Married Women in MalawiInvestigating Causal and Mediating Risk Factors for Stunting in under Five Children in Malawi Using Structural Equation Modelling TechniquesLinking Food Insecurity to Quality of Life Using Structural Equation ModelsA Zero-Truncated Negative Binomial Regression Model for Dietary Diversity in Namibian Under-5 ChildrenA Copula Approach to Sample Selection Modelling of Treatment Adherence and Viral Suppression among HIV Patients on Antiretroviral Therapy (ART) in NamibiaCopula-Linked Generalized Joint Regression Model for Water, Sanitation and Hygiene (WASH) Coverage in NamibiaBivariate Copula-Based Regression to Model Timing and Frequency of Antenatal Care UtilizationMultiscale Spatial Modelling of Diabetes and Hypertension in NamibiaModels for Analyzing Spatial Patterns in Risk of Urban Malaria: A Case Study of Blantyre, MalawiSpatio-Temporal Modelling of Malaria Risk in Malawi: An Application to Health Management Information System DataModelling Spatial and Spatial-Temporal Patterns of TB and HIV Mortality in NamibiaAttrition of Women Initiating Antiretroviral Therapy (ART) under Option B+: Cox Proportional Hazards, Competing Risks and Multistate Survival ModelsEpilogueAbout the ContributorsIndex.

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Book SynopsisThe last decade has witnessed various technological advances in life sciences, especially high throughput technologies. These technologies provide a way to perform parallel scientific studies in a very short period of time with low cost. High throughput techniques, mainly, next generation sequencing, microarray and mass spectrometry, have strengthened the omics vision in the last decades (study of complete system) and now resulted in well-developed branches of omics i.e., genomics, transcriptomics, proteomics and metabolomics, which deal with almost every level of central dogma of life. Practice of high throughput techniques throughout the world with different aims and objectives resulted in a voluminous data, which required computational applications, i.e., database, algorithm and software to store, process and get biological interpretation from primary raw data. Researchers from different fields are looking to analyze these raw data for different purposes, but lacking of proper information and knowledge in proper documented form creates different kinds of hurdles and raises the challenges. This book contains thirteen chapters that deal with different computational biology/bioinformatics resources and concepts which are already in practice by the scientific community or can be utilized to handle various aspects of different classes of omics data. It includes different computational concepts, algorithm, resources and recent trends belonging to the four major branches of omics (i.e., genomics, transcriptomics, proteomics and metabolomics), including integrative omics. It will help all scholars who are working in any branch of computational omics and bioinformatics field as well as those who would like to perform research at a systemic biology through computational approaches.Table of ContentsFor more information, please visit our website at:https://novapublishers.com/shop/recent-trends-in-computational-omics-concepts-and-methodology/

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Book SynopsisMany people go through life in a rather hit-or-miss fashion, casting about for ideas to explain why their projects improve or decline, why they are successful or why they are not. Guessing and "hunches," however, are not very reliable. And without the knowledge of how to actually investigate situations, good or bad, and get the true facts, a person is set adrift in a sea of unevaluated data. Accurate investigation is, in fact, a rare commodity. Man's tendency in matters he doesn't understand is to accept the first proffered explanation, no matter how faulty. Thus investigatory technology had not actually been practiced or refined. However, L. Ron Hubbard made a breakthrough in the subject of logic and reasoning which led to his development of the first truly effective way to search for and consistently find the actual causes for things. Knowing how to investigate gives one the power to navigate through the random facts and opinions and emerge with the real reasons behind success or failure in any aspect of life. By really finding out why things are the way they are, one is therefore able to remedy and improve a situation-any situation. This is an invaluable technology for people in all walks of life.

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Book SynopsisData Centres in India are political institutions influencing state-capital power dynamics. This book examines their social impact, highlighting power theft and territorial changes in Navi Mumbai. It explores how the Indian state adapts governance norms in the digital era with initiatives like Aadhaar and demonetisation.

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