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Complex Data Analytics with Formal Concept Analysis
FCA is an important formalism that is associated with a variety of research areas such as lattice theory, knowledge representation, data mining, machine learning, and semantic Web. It is successfully exploited in an increasing number of application domains such as software engineering, information retrieval, social network analysis, and bioinformatics. Its mathematical power comes from its concept lattice formalization in which each element in the lattice captures a formal concept while the whole structure represents a conceptual hierarchy that offers browsing, clustering and association rule mining.Complex data analytics refers to advanced methods and tools for mining and analyzing data with complex structures such as XML/Json data, text and image data, multidimensional data, graphs, sequences and streaming data. It also covers visualization mechanisms used to highlight the discovered knowledge.This edited book examines a set of important and relevant research directions in complex data management, and updates the contribution of the FCA community in analyzing complex and large data such as knowledge graphs and interlinked contexts. For example, Formal Concept Analysis and some of its extensions are exploited, revisited and coupled with recent processing parallel and distributed paradigms to maximize the benefits in analyzing large data.
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R 3,847.00
Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data
Although its roots can be traced to the 19th century, progress in the study of nonlinear dynamical systems has taken off in the last 30 years. While pertinent source material exists, it is strewn about the literature in mathematics, physics, biology, economics, and psychology at varying levels of accessibility. A compendium research methods reflecting the expertise of major contributors to NDS psychology, Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data examines the techniques proven to be the most useful in the behavioral sciences.The editors have brought together constructive work on new practical examples of methods and application built on nonlinear dynamics. They cover dynamics such as attractors, bifurcations, chaos, fractals, catastrophes, self-organization, and related issues in time series analysis, stationarity, modeling and hypothesis testing, probability, and experimental design. The analytic techniques discussed include several variants of the fractal dimension, several types of entropy, phase-space and state-space diagrams, recurrence analysis, spatial fractal analysis, oscillation functions, polynomial and Marquardt nonlinear regression, Markov chains, and symbolic dynamics.The book outlines the analytic requirements faced by social scientists and how they differ from those of mathematicians and natural scientists. It includes chapters centered on theory and procedural explanations for running the analyses with pertinent examples and others that illustrate applications where a particular form of analysis is seen in the context of a research problem. This combination of approaches conveys theoretical and practical knowledge that helps you develop skill and expertise in framing hypotheses dynamically and building viable analytic models to test them.
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Experimental Design and Analysis
"Brown and Melamed′s book is one of the best concise treatments of the design and analysis of experiments that I have seen. The authors begin by showing the significance of variability (variance) for the analysis of experiments, and clearly illustrate the utility of the analysis of variance (ANOVA) model to the analysis of experimental data. They also provide a clear discussion of more advanced topics such as nested, factorial, split-plot, and repeated measures designs. Their book is comprehensive, handles each topic deftly, and should be readily accessible to researchers with a good grounding in basic statistics."--Contemporary Sociology"The book is well written and includes useful examples. . . . Useful to researchers in both the planning and analysis phases of an experimental study." --ANNA Journal "Introductory, well written, and has illustrative examples. Highly recommended for introductory courses and self study; the book can be supplemented easily with a treatment of covariates from other available study materials."--Journal of Marketing ResearchThis volume introduces the reader to one of the most fundamental topics in social science statistics--experimental design. The authors clearly show how to select an experimental design based on the number of independent variables, the sources and number of extraneous variables, and the number of subjects. Other topics addressed include variability, hypothesis testing, how ANOVA can be extended to the multi-group situation, the logic of the t test, and completely randomized designs.
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Data Analysis for the Geosciences
Data Analysis for the Geosciences Essentials of Uncertainty, Comparison, and Visualization Data Analysis for the Geosciences: Essentials of Uncertainty, Comparison, and Visualization is a textbook for upper-level undergraduate STEM students, designed to be their statistics course in a degree program. This volume provides a comprehensive introduction to data analysis, visualization, and data-model comparisons and metrics, within the framework of the uncertainty around the values. It offers a learning experience based on real data from the Earth, ocean, atmospheric, space, and planetary sciences. About this volume: Serves as an initial course in scientific data analysis and hypothesis testingFocuses on the methods of data processingIntroduces a wide range of analysis techniquesDescribes the many ways to compare data with modelsCenters on applications rather than derivationsExplains how to select appropriate statistics for meaningful decisionsExplores the importance of the concept of uncertaintyUses examples from real geoscience observationsHomework problems at the end of chapters The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
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Time Series Analysis for the State-Space Model with R/Stan
This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader’s analytical capability.
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Statistical and Machine Learning Approaches for Network Analysis
Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networksAn introduction to complex networks—measures, statistical properties, and modelsModeling for evolving biological networksThe structure of an evolving random bipartite graphDensity-based enumeration in structured dataHyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.
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Bayesian Data Analysis for the Behavioral and Neural Sciences
This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced mathematical techniques. This approach goes beyond “frequentist” concepts of p-values and null hypothesis testing, using the full power of modern probability theory to solve real-world problems. The book offers a fully self-contained course, which demonstrates analysis techniques throughout with worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general algorithms that help students solve the measurement and model selection (also called “hypothesis testing”) problems most frequently encountered in real-world applications.
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Network Analysis
The size and availability of network information has exploded over the last decade. Social scientists now share the stage of network analysis with computer scientists, physicists, and statisticians. While a number of introductions to network analysis are now available, most focus on theory, methods, or application alone. This book integrates all three. Network Analysis is an introduction to both the why and how of Social Network Analysis (SNA). It presents a broad theoretical overview rooted in social scientific approaches and guides users in how network analysis can answer core theoretical questions. It provides a comprehensive overview of descriptive and analytical approaches, including practical tutorials in R with sample data sets. Using an integrated approach, this book aims to quickly bring novice network researchers up to speed while avoiding common programming and analysis mistakes so that they might gain insight into the fundamental theories, key concepts, and methodological application of SNA.
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R 1,139.00
Applied Multivariate Analysis
This book provides a broad overview of the basic theory and methods of applied multivariate analysis. The presentation integrates both theory and practice including both the analysis of formal linear multivariate models and exploratory data analysis techniques. Each chapter contains the development of basic theoretical results with numerous applications illustrated using examples from the social and behavioral sciences, and other disciplines. All examples are analyzed using SAS for Windows Version 8.0.
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Introducing Survival and Event History Analysis
Introducing Survival Analysis and Event History Analysis is an accessible, practical and comprehensive guide for researchers and students who want to understand the basics of survival and event history analysis and apply these methods without getting entangled in mathematical and theoretical technicalities. Inside, readers are offered a blueprint for their entire research project from data preparation to model selection and diagnostics. Engaging, easy to read, functional and packed with enlightening examples, ′hands-on′ exercises and resources for both students and instructors, Introducing Survival Analysis and Event History Analysis allows researchers to quickly master these advanced statistical techniques. This book is written from the perspective of the ′user′, making it suitable as both a self-learning tool and graduate-level textbook. Introducing Survival Analysis and Event History Analysis covers the most up-to-date innovations in the field, including advancements in the assessment of model fit, frailty and recurrent event models, discrete-time methods, competing and multistate models and sequence analysis. Practical instructions are also included, focusing on the statistical program R and Stata, enabling readers to replicate the examples described in the text. This book comes with a glossary, a range of practical and user-friendly examples, cases and exercises.
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Missing Data Analysis in Practice
Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts, with the data sets and codes available online.The book underscores the development of missing data methods and their adaptation to practical problems. It mainly focuses on the traditional missing data problem. The author also shows how to use the missing data framework in many other statistical problems, such as measurement error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference.
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Qualitative Data Analysis
With numerous approaches to choose from, knowing where to start when doing qualitative data analysis (QDA) can be a challenge. This book gives you direction with an accessible and thorough introduction to nine different approaches to QDA, written by a multi-disciplinary team with years of experience teaching and analysing data using these methods. With a clear focus on the ‘how to’ of QDA, each chapter includes: •Step-by-step descriptions of how to apply each approach in your research •Online and in-text activities to help you practice your skills •Annotated reading lists so you can dig deeper into key topics •Case studies from a range of disciplines so you can see how each approach works in practice. The perfect companion on your journey through QDA, the book also offers a comprehensive introduction to the use of NVivo QDA software, helping both new and experienced researchers get to grips with the essentials.
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Statistical and Econometric Methods for Transportation Data Analysis
The book's website (with databases and other support materials) can be accessed here.Praise for the Second Edition:The second edition introduces an especially broad set of statistical methods … As a lecturer in both transportation and marketing research, I find this book an excellent textbook for advanced undergraduate, Master’s and Ph.D. students, covering topics from simple descriptive statistics to complex Bayesian models. … It is one of the few books that cover an extensive set of statistical methods needed for data analysis in transportation. The book offers a wealth of examples from the transportation field. —The American Statistician Statistical and Econometric Methods for Transportation Data Analysis, Third Edition offers an expansion over the first and second editions in response to the recent methodological advancements in the fields of econometrics and statistics and to provide an increasing range of examples and corresponding data sets. It describes and illustrates some of the statistical and econometric tools commonly used in transportation data analysis. It provides a wide breadth of examples and case studies, covering applications in various aspects of transportation planning, engineering, safety, and economics. Ample analytical rigor is provided in each chapter so that fundamental concepts and principles are clear and numerous references are provided for those seeking additional technical details and applications. New to the Third Edition Updated references and improved examples throughout. New sections on random parameters linear regression and ordered probability models including the hierarchical ordered probit model. A new section on random parameters models with heterogeneity in the means and variances of parameter estimates. Multiple new sections on correlated random parameters and correlated grouped random parameters in probit, logit and hazard-based models. A new section discussing the practical aspects of random parameters model estimation. A new chapter on Latent Class Models. A new chapter on Bivariate and Multivariate Dependent Variable Models. Statistical and Econometric Methods for Transportation Data Analysis, Third Edition can serve as a textbook for advanced undergraduate, Masters, and Ph.D. students in transportation-related disciplines including engineering, economics, urban and regional planning, and sociology. The book also serves as a technical reference for researchers and practitioners wishing to examine and understand a broad range of statistical and econometric tools required to study transportation problems.
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Qualitative Analysis
"Qualitative Analysis" offers a detailed introduction to the practice of data analysis that is both user-friendly and theoretically grounded. Drawing on his experience of qualitative research, Douglas Ezzy reviews approaches to data analysis in established research traditions including ethnography, phenomenology and symbolic interactionism, alongside the newer approaches informed by cultural studies and feminism. He explains the difference between inductive, deductive and abductive theory building, provides a guide to computer-assisted analysis and outlines techniques such as journal writing, team meetings and participant reviews. At the heart of insightful qualitative research is a dialogue between tradition and innovation, ideas and observations, theory and data, and interpretation and action. Ezzy explains how to engage in this dialogue by choosing methods of data analysis to suit the research project, adapting these methods as needed. This title is a useful reference for research students and for professional researchers in the social sciences and health.
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Qualitative Data Analysis
Balancing theoretical foundations with practical strategies, this book helps you develop an approach to your qualitative analysis that is both systematic and insightful.It demonstrates the importance of tying analysis into every aspect of research, from design, through data collection and management, to writing up, and provides step-by-step guidance on how to embed analysis from start to finish. Grounded in the reality of doing research, this second edition: • Presents visual and text-based methods for analysis, using manual and digital tools • Inspires confidence as you code, connect and interrogate observational, text and visual data • Showcases best practice and helps you navigate real-life dilemmas using case studies of research from across the social sciences.Together with rich online resources including videos, datasets and journal articles, this is an important new edition for all students undertaking qualitative research, with a focus on analysis and design.
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Age-Period-Cohort Analysis
Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications is based on a decade of the authors’ collaborative work in age-period-cohort (APC) analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. The authors show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables develop along the age, period, and cohort dimensions.The book makes two essential contributions to quantitative studies of time-related change. Through the introduction of the GLMM framework, it shows how innovative estimation methods and new model specifications can be used to tackle the "model identification problem" that has hampered the development and empirical application of APC analysis. The book also addresses the major criticism against APC analysis by explaining the use of new models within the GLMM framework to uncover mechanisms underlying age patterns and temporal trends. Encompassing both methodological expositions and empirical studies, this book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for APC analysis. It compares new and existing models and methods and provides useful guidelines on how to conduct APC analysis. For empirical illustrations, the text incorporates examples from a variety of disciplines, such as sociology, demography, and epidemiology. Along with details on empirical analyses, software and programs to estimate the models are available on the book’s web page.
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Introduction to Data Science
Introduction to Data Science: Statistics and Prediction Algorithms Through Case Studies teaches data science as a way of thinking statistically, not just as a collection of computational tools. Building on the topics covered in Introduction to Data Science: Data Wrangling and Visualization with R, this book is designed for students with some programming experience and basic mathematical maturity, this book builds the foundations of probability, statistical inference, regression, high-dimensional data analysis, and machine learning through real data examples and reproducible R code. It is suitable for one-semester course in advanced data science.The book shows how to reason about variability, uncertainty, prediction error, model assumptions, and validation. Through case studies involving polling, genetics, baseball, recommendation systems, image classification, and other modern datasets, readers learn how to connect probability models to data, summarize complex information, quantify uncertainty, fit and interpret models, evaluate prediction algorithms, and understand the statistical ideas behind machine learning. Each chapter is designed to support classroom teaching, self-study, and hands-on analysis, with exercises and companion web materials available through the book website.Key Features:Includes base R, data.table, and tidyverse code.Focuses on the statistical and probabilistic foundations of machine learning.Contains real-world case studies.Rafael A. Irizarry is Professor and Chair of the Department of Data Science at Dana-Farber Cancer Institute and Professor of Applied Statistics at Harvard. His research focuses on Genomics and he has taught several Data Science courses.
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Practice of Data Analysis
This collection of essays brings together many of the world's most distinguished statisticians to discuss a wide array of the most important recent developments in data analysis. The book honors John W. Tukey, one of the most influential statisticians of the twentieth century, on the occasion of his eightieth birthday. Contributors, some of them Tukey's former students, use his general theoretical work and his specific contributions to Exploratory Data Analysis as the point of departure for their papers. They cover topics from "pure" data analysis, such as gaussianizing transformations and regression estimates, and from "applied" subjects, such as the best way to rank the abilities of chess players or to estimate the abundance of birds in a particular area. Tukey may be best known for coining the common computer term "bit," for binary digit, but his broader work has revolutionized the way statisticians think about and analyze sets of data. In a personal interview that opens the book, he reviews these extraordinary contributions and his life with characteristic modesty, humor, and intelligence.The book will be valuable both to researchers and students interested in current theoretical and practical data analysis and as a testament to Tukey's lasting influence. The essays are by Dhammika Amaratunga, David Andrews, David Brillinger, Christopher Field, Leo Goodman, Frank Hampel, John Hartigan, Peter Huber, Mia Hubert, Clifford Hurvich, Karen Kafadar, Colin Mallows, Stephan Morgenthaler, Frederick Mosteller, Ha Nguyen, Elvezio Ronchetti, Peter Rousseeuw, Allan Seheult, Paul Velleman, Maria-Pia Victoria-Feser, and Alessandro Villa. Originally published in 1998. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.
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Applied Data Analysis for Urban Planning and Management
This book showcases the different ways in which contemporary forms of data analysis are being used in urban planning and management. It highlights the emerging possibilities that city-regional governance, technology and data have for better planning and urban management - and discusses how you can apply them to your research.Including perspectives from across the globe, it’s packed with examples of good practice and helps to demystify the process of using big and open data.Learn about different kinds of emergent data sources and how they are processed, visualised and presented.Understand how spatial analysis and GIS are used in city planning.See examples of how contemporary data analytics methods are being applied in a variety of contexts, such as ‘smart’ city management and megacities.Aimed at upper undergraduate and postgraduate students studying spatial analysis and planning, this timely text is the perfect companion to enable you to apply data analytics approaches in your research.
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Contextual Analysis
Contextual analysis, the study of the role of the group context on actions and attitudes of individuals, is a useful technique in the study of education, neighborhoods, census tracts, election districts, and the family. However, the effective use of contextual analysis has involved overcoming a number of issues, such as group boundaries, the mobility of the individuals within a group, overlapping groups, missing individual data, and the choice of statistical models. Contextual Analysis offers researchers a guide for selecting the best model to use. Written in a straightforward style, the book explores such topics as contextual analysis with absolute effects, with relative effects, and the choice between regression coefficients as fixed parameters or as random variables.
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Applied Quantitative Analysis in Education and the Social Sciences
To say that complex data analyses are ubiquitous in the education and social sciences might be an understatement. Funding agencies and peer-review journals alike require that researchers use the most appropriate models and methods for explaining phenomena. Univariate and multivariate data structures often require the application of more rigorous methods than basic correlational or analysis of variance models. Additionally, though a vast set of resources may exist on how to run analysis, difficulties may be encountered when explicit direction is not provided as to how one should run a model and interpret results. The mission of this book is to expose the reader to advanced quantitative methods as it pertains to individual level analysis, multilevel analysis, item-level analysis, and covariance structure analysis. Each chapter is self-contained and follows a common format so that readers can run the analysis and correctly interpret the output for reporting.
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Signed Distance Measure in Fuzzy Statistical Analysis
The main focus of this book is on presenting advances in fuzzy statistics, and on proposing a methodology for testing hypotheses in the fuzzy environment based on the estimation of fuzzy confidence intervals, a context in which not only the data but also the hypotheses are considered to be fuzzy. The proposed method for estimating these intervals is based on the likelihood method and employs the bootstrap technique. A new metric generalizing the signed distance measure is also developed. In turn, the book presents two conceptually diverse applications in which defended intervals play a role: one is a novel methodology for evaluating linguistic questionnaires developed at the global and individual levels; the other is an extension of the multi-ways analysis of variance to the space of fuzzy sets. To illustrate these approaches, the book presents several empirical and simulation-based studies with synthetic and real data sets. In closing, it presents a coherent R package called “FuzzySTs” which covers all the previously mentioned concepts with full documentation and selected use cases. Given its scope, the book will be of interest to all researchers whose work involves advanced fuzzy statistical methods.
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Data Science Applied to Sustainability Analysis
Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas.
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Research Design and Analysis
A concise, straightforward overview of research design and analysis, helping readers form a general basis for designing and conducting research The practice of designing and analyzing research continues to evolve with advances in technology that enable greater technical analysis of data—strengthening the ability of researchers to study the interventions and relationships of factors and assisting consumers of research to understand and evaluate research reports. Research Design and Analysis is an accessible, wide-ranging overview of how to design, conduct, analyze, interpret, and present research. This book helps those in the sciences conduct their own research without requiring expertise in statistics and related fields and enables informed reading of published research. Requiring no background in statistics, this book reviews the purpose, ethics, and rules of research, explains the fundamentals of research design and validity, and describes how to select and employ appropriate statistical techniques and reporting methods. Readers gain knowledge central to various research scenarios, from sifting through reports of meta-analyses and preparing a research paper for submission to a peer-reviewed journal to discussing, evaluating, and communicating research results. This book: Provides end-to-end guidance on the entire research design and analysis processTeaches readers how to both conduct their own research and evaluate the research of othersOffers a clear, concise introduction to fundamental topics ideal for both reference and general education functionsPresents information derived from the author’s experience teaching the subject in real-world classroom settingsIncludes a full array of learning tools including tables, examples, additional resource suggestions, complete references, and appendices that cover statistical analysis software and data sets Research Design and Analysis: A Primer for the Non-Statistician is a valuable source of information for students and trainees in medical and allied health professions, journalism, education, and those interested in reading and comprehending research literature.
R 4,395.00
Qualitative Data Analysis
First Published in 2004. Learning how to analyze qualitative data by computer can be fun. That is one assumption underpinning this introduction to qualitative analysis, which takes account of how computing techniques have enhanced and transformed the field. The author provides a practical discussion of the main procedures for analyzing qualitative data by computer, with most of its examples taken from humour or everyday life. He examines ways in which computers can contribute to greater rigour and creativity, as well as greater efficiency in analysis. He discusses some of the pitfalls and paradoxes as well as the practicalities of computer-based qualitative analysis. The perspective of "Qualitative Data Analysis" is pragmatic rather than prescriptive, introducing different possibilities without advocating one particular approach. The result is a largely discipline-neutral text, which is suitable for arts and social science students and first-time qualitative analysts.
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Fundamentals of Descriptive Statistics
• Do your students need to organize and summarize data for term projects? Will they need to perform these tasks on the job? This book gives them thorough preparation.• In twelve short chapters, your students will learn the purposes of descriptive statistics, their calculation, and proper interpretation.• Actual data on the emotional health of foster-care adolescents are used throughout the book to illustrate various ways of deriving meaning from the data with descriptive statistics. Other interesting examples are also included.• Computational procedures are illustrated with step-by-step, easy-to-follow examples.• End-of-chapter exercises provide ample practice for students to master both computations and statistical concepts.• Eliminates the need for students to buy a traditional statistics book that emphasizes inferential statistics.• Thoroughly field-tested for student comprehension.• This book will please you and your students with its clarity of presentation.• Outstanding supplement for students who need to describe term project data.
R 1,459.53
Research Methods in Social Network Analysis
Since the publication of Herbert Spencer's Principles of Sociology in 1875, the use of social structure as a defining concept has produced a large body of creative speculations, insights, and intuitions about social life. However, writers in this tradition do not always provide the sorts of formal definitons and propositions that are the building blocks of modern social research. In its broad-ranging examination of the kind of data that form the basis for the systematic study of social structure, Research Methods in Social Network Analysis marks a significant methodological advance in network studies.As used in this volume, social structure refers to a bundle of intuitive natural language ideas and concepts about patterning in social relationships among people. In contrast, social networks is used to refer to a collection of precise analytic and methodological concepts and procedures that facilitate the collection of data and the systematic study of such patterning. Accordingly, the book's five sections are arranged to address analytical problems in a series of logically ordered stages or processes.The major contributors define the fundamental modes by which social structural phenomena are to be represented; how boundaries to a social structure are set; how the relations of a network are measured in terms of structure and content; the ways in which the relational structure of a network affects system actors; and how actors within a social network are clustered into cliques or groups. The chapters in the last section build on solutions to problems proposed in the previous sections. This highly unified approach to research design combined with a representative diversity of viewpoints makes Research Methods in Social Network Analysis a state-of-the-art volume.
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Computational Methods for Next Generation Sequencing Data Analysis
Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithmsDiscusses the mathematical and computational challenges in NGS technologiesCovers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.
R 4,525.00
Learning Geospatial Analysis with Python -
An effective guide to geographic information systems and remote sensing analysis using Python 3About This Book• Construct applications for GIS development by exploiting Python• This focuses on built-in Python modules and libraries compatible with the Python Packaging Index distribution system—no compiling of C libraries necessary• This practical, hands-on tutorial teaches you all about Geospatial analysis in Python Who This Book Is ForIf you are a Python developer, researcher, or analyst who wants to perform Geospatial, modeling, and GIS analysis with Python, then this book is for you. Familarity with digital mapping and analysis using Python or another scripting language for automation or crunching data manually is appreciatedWhat You Will Learn• Automate Geospatial analysis workflows using Python• Code the simplest possible GIS in 60 lines of Python• Mold thematic maps with Python tools• Get hold of the various forms that geospatial data comes in• Produce elevation contours using Python tools• Create flood inundation models• Apply Geospatial analysis to find out about real-time data tracking and for storm chasingIn DetailGeospatial Analysis is used in almost every field you can think of from medicine, to defense, to farming. This book will guide you gently into this exciting and complex field. It walks you through the building blocks of geospatial analysis and how to apply them to influence decision making using the latest Python software.Learning Geospatial Analysis with Python, 2nd Edition uses the expressive and powerful Python 3 programming language to guide you through geographic information systems, remote sensing, topography, and more, while providing a framework for you to approach geospatial analysis effectively, but on your own terms. We start by giving you a little background on the field, and a survey of the techniques and technology used. We then split the field into its component specialty areas: GIS, remote sensing, elevation data, advanced modeling, and real-time data.This book will teach you everything you need to know about, Geospatial Analysis from using a particular software package or API to using generic algorithms that can be applied. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don't become bogged down in just getting ready to do analysis. This book will round out your technical library through handy recipes that will give you a good understanding of a field that supplements many a modern day human endeavors.Style and approach This is a practical, hands-on tutorial that teaches you all about Geospatial analysis interactively using Python.
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Functional and Shape Data Analysis
This textbook for courses on function data analysis and shape data analysis describes how to define, compare, and mathematically represent shapes, with a focus on statistical modeling and inference. It is aimed at graduate students in analysis in statistics, engineering, applied mathematics, neuroscience, biology, bioinformatics, and other related areas. The interdisciplinary nature of the broad range of ideas covered—from introductory theory to algorithmic implementations and some statistical case studies—is meant to familiarize graduate students with an array of tools that are relevant in developing computational solutions for shape and related analyses. These tools, gleaned from geometry, algebra, statistics, and computational science, are traditionally scattered across different courses, departments, and disciplines; Functional and Shape Data Analysis offers a unified, comprehensive solution by integrating the registration problem into shape analysis, better preparing graduate students for handling future scientific challenges.Recently, a data-driven and application-oriented focus on shape analysis has been trending. This text offers a self-contained treatment of this new generation of methods in shape analysis of curves. Its main focus is shape analysis of functions and curves—in one, two, and higher dimensions—both closed and open. It develops elegant Riemannian frameworks that provide both quantification of shape differences and registration of curves at the same time. Additionally, these methods are used for statistically summarizing given curve data, performing dimension reduction, and modeling observed variability. It is recommended that the reader have a background in calculus, linear algebra, numerical analysis, and computation.
R 4,079.91
Spatial Analysis Methods and Practice
This is an introductory textbook on spatial analysis and spatial statistics through GIS. Each chapter presents methods and metrics, explains how to interpret results, and provides worked examples. Topics include: describing and mapping data through exploratory spatial data analysis; analyzing geographic distributions and point patterns; spatial autocorrelation; spatial clustering; geographically weighted regression and OLS regression; and spatial econometrics. The worked examples link theory to practice through a single real-world case study, with software and illustrated guidance. Exercises are solved twice: first through ArcGIS, and then GeoDa. Through a simple methodological framework the book describes the dataset, explores spatial relations and associations, and builds models. Results are critically interpreted, and the advantages and pitfalls of using various spatial analysis methods are discussed. This is a valuable resource for graduate students and researchers analyzing geospatial data through a spatial analysis lens, including those using GIS in the environmental sciences, geography, and social sciences.
R 5,917.00
Data Analysis for Physical Scientists
The ability to summarise data, compare models and apply computer-based analysis tools are vital skills necessary for studying and working in the physical sciences. This textbook supports undergraduate students as they develop and enhance these skills. Introducing data analysis techniques, this textbook pays particular attention to the internationally recognised guidelines for calculating and expressing measurement uncertainty. This new edition has been revised to incorporate Excel® 2010. It also provides a practical approach to fitting models to data using non-linear least squares, a powerful technique which can be applied to many types of model. Worked examples using actual experimental data help students understand how the calculations apply to real situations. Over 200 in-text exercises and end-of-chapter problems give students the opportunity to use the techniques themselves and gain confidence in applying them. Answers to the exercises and problems are given at the end of the book.
R 3,986.81
Multivariate Data Analysis
Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set of general and astronomical bibliographic references follows each chapter, providing valuable entry-points for research workers in all astronomical sub-disciplines. Although the applications considered focus on astronomy, the algorithms used can be applied to similar problems in other branches of science. Fortran programs are provided for many of the methods described.
R 4,643.15
High-Dimensional Data Analysis in Cancer Research
Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data.
R 3,380.84
Basic Environmental Data Analysis for Scientists and Engineers
Classroom tested and the result of over 30 years of teaching and research, this textbook is an invaluable tool for undergraduate and graduate data analysis courses in environmental sciences and engineering. It is also a useful reference on modern digital data analysis for the extensive and growing community of Earth scientists and engineers. Basic Environmental Data Analysis for Scientists and Engineers introduces practical concepts of modern digital data analysis and graphics, including numerical/graphical calculus, measurement units and dimensional analysis, error propagation and statistics, and least squares data modeling. It emphasizes array-based or matrix inversion and spectral analysis using the fast Fourier transform (FFT) that dominates modern data analysis. Divided into two parts, this comprehensive hands-on textbook is excellent for exploring data analysis principles and practice using MATLAB®, Mathematica, Mathcad, and other modern equation solving software. Part I, for beginning undergraduate students, introduces the basic approaches for quantifying data variations in terms of environmental parameters. These approaches emphasize uses of the data array or matrix, which is the fundamental data and mathematical processing format of modern electronic computing. Part II, for advanced undergraduate and beginning graduate students, extends the inverse problem to least squares solutions involving more than two unknowns. Features: Offers a uniquely practical guide for making students proficient in modern electronic data analysis and graphics Includes topics that are not explained in any existing textbook on environmental data analysis Data analysis topics are very well organized into a two-semester course that meets general education curriculum requirements in science and engineering Facilitates learning by beginning each chapter with an ‘Overview’ section highlighting the topics covered, and ending it with a ‘Key Concepts’ section summarizing the main technical details that the reader should have acquired Indexes many numerical examples for ready access in the classroom or other venues serviced by electronic equation solvers like MATLAB®, Mathematica, Mathcad, etc. Offers supplemental exercises and materials to enhance understanding the principles and practice of modern data analysis
R 2,564.00
Public Policy Analysis
Public Policy Analysis, the most widely cited book on the subject, provides students with a comprehensive methodology of policy analysis. It starts from the premise that policy analysis is an applied social science discipline designed for solving practical problems facing public and nonprofit organizations. This thoroughly revised sixth edition contains a number of important updates: Each chapter includes an all-new "big ideas" case study in policy analysis to stimulate student interest in timely and important problems. The dedicated chapter on evidence-based policy and the role of field experiments has been thoroughly rewritten and expanded. New sections on important developments in the field have been added, including using scientific evidence in public policymaking, systematic reviews, meta-analyses, and "big data." Data sets to apply analytical techniques are included online as IBM SPSS 23.0 files and are convertible to Excel, Stata, and R statistical software programs to suit a variety of course needs and teaching styles. All-new PowerPoint slides are included to make instructor preparation easier than ever before.Designed to prepare students from a variety of academic backgrounds to conduct policy analysis on their own, without requiring a background in microeconomics, Public Policy Analysis, Sixth Edition helps students develop the practical skills needed to communicate findings through memos, position papers, and other forms of structured analytical writing. The text engages students by challenging them to critically analyze the arguments of policy practitioners as well as political scientists, economists, and political philosophers.
R 5,495.00
