Data science and analysis Books
Taylor & Francis Ltd NextGeneration Sequencing Data Analysis
Book SynopsisNext-generation DNA and RNA sequencing has revolutionized biology and medicine. With sequencing costs continuously dropping and our ability to generate large datasets rising, data analysis becomes more important than ever. Next-Generation Sequencing Data Analysis walks readers through next-generation sequencing (NGS) data analysis step by step for a wide range of NGS applications. For each NGS application, this book covers topics from experimental design, sample processing, sequencing strategy formulation, to sequencing read quality control, data preprocessing, read mapping or assembly, and more advanced stages that are specific to each application. Major applications include: RNA-seq: Both bulk and single cell (separate chapters) Genotyping and variant discovery through whole genome/exome sequencing Clinical sequencing and detection of actionable variants De novo genome assembly Table of Contents 1. The Cellular System and The Code of Life. 2. DNA Sequence: the Genome Base. 3. RNA: the Transcribed Sequence. 4. Next-Generation Sequencing (NGS) Technologies: Ins and Outs. 5. Early-Stage Next-Generation Sequencing (NGS) Data Analysis: Common Steps. 6. Computing Needs for Next-Generation Sequencing (NGS) Data Management and Analysis. 7. Transcriptomics by Bulk RNA-Seq. 8. Transcriptomics by Single Cell RNA-Seq. 9. Small RNA Sequencing. 10. Genotyping and Variation Discovery by Whole Genome/Exome Sequencing. 11. Clinical Sequencing and Detection of Actionable Variants. 12. De Novo Genome Assembly with Long and/or Short Reads. 13. Mapping Protein-DNA Interactions with ChIP-Seq. 14. Epigenomics by DNA Methylation Sequencing. 15. Whole Metagenome Sequencing for Microbial Community Analysis. 16. What’s Next for Next-Generation Sequencing (NGS)?.
£71.24
Taylor & Francis Ltd Time Series for Data Science
Book SynopsisData Science students and practitioners want to find a forecast that works and don't want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject.This book is an accessible guide that doesn't require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.Features:Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of thesTrade Review"A well-structured text aimed at undergraduates pursuing a data science curriculum, or MBA students. The authors draw upon their vast combined experience in research and teaching to a variety of audiences to present the classical material on ARMA-based Box-Jenkins methodology without assuming a calculus background. Yet, their approach manages to be heuristic, while not sacrificing relevant theoretical detail that enriches understanding. The authors complement this material with chapters on multivariate models, and, refreshingly, a very enlightening discussion on neural networks. The exposition is lucid, well-organized, and copiously illustrated to reinforce comprehension of concepts. The companion R package (tswge) finds a niche in the growing list of time series toolboxes, by providing clean, straightforward functionality on such essentials as spectrum reconstruction and model factor tables to glean the structure of AR and MA polynomials."- Alex Trindade, Texas Tech University Table of Contents1. Working with Data Collected Over Time, 2. Exploring Time Series Data, 3. Statistical Basics for Time Series Analysis, 4. The Frequency Domain, 5. ARMA Models, 6. ARMA Fitting and Forecasting, 7. ARIMA, Seasonal,and ARCH/GARCH Models, 8. Time Series Regression, 9. Model Assessment, 10. Multivariate Time Series, 11. Deep Neural Network Based Time Series Models
£99.75
Taylor & Francis Ltd Ordinal Data Analysis
Book SynopsisThis book is a step-by-step data story for analyzing ordinal data from start to finish. The book is for researchers, statisticians and scientists who are working with datasets where the response is ordinal. This type of data is common in many disciplines, not just in surveys (as is often thought). For example, in the biological sciences, there is an interest in understanding and predicting the (growth) stage (of a plant or animal) based on a multitude of factors. Likewise, ordinal data is common in environmental sciences (for example, stage of a storm), chemical sciences (for example, type of reaction), physical sciences (for example, stage of damage when force is applied), medical sciences (for example, degree of pain) and social sciences (for example, demographic factors like social status categorized in brackets). There has been no complete text about how to model an ordinal response as a function of multiple numerical and categorical predictors. There has always been a reluctanc
£87.39
Taylor & Francis Data Analysis in Sport
Book SynopsisTable of Contents1. Principles of data analysis 2. Analysis facilities of commercial packages 3. Microsoft Excel 4. Visualisation 5. Statistical windows 6. Player tracking data 7. Matlab 8. Statistical analysis 9. Reliability
£45.59
John Wiley & Sons Inc Data Analysis and Visualization in Genomics and
Book SynopsisData Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems.Table of ContentsPreface. List of Contributors. SECTION I: INTRODUCTION - DATA DIVERSITY AND INTEGRATION. 1. Integrative Data Analysis and Visualization: Introduction to Critical Problems, Goals and Challenges (Francisco Azuaje and Joaquín Dopazo). 1.1 Data Analysis and Visualization: An Integrative Approach. 1.2 Critical Design and Implementation Factors. 1.3 Overview of Contributions. References. 2. Biological Databases: Infrastructure, Content and Integration (Allyson L. Williams, Paul J. Kersey, Manuela Pruess and Rolf Apweiler). 2.1 Introduction. 2.2 Data Integration. 2.3 Review of Molecular Biology Databases. 2.4 Conclusion. References. 3. Data and Predictive Model Integration: an Overview of Key Concepts, Problems and Solutions (Francisco Azuaje, Joaquín Dopazo and Haiying Wang). 3.1 Integrative Data Analysis and Visualization: Motivation and Approaches. 3.2 Integrating Informational Views and Complexity for Understanding Function. 3.3 Integrating Data Analysis Techniques for Supporting Functional Analysis. 3.4 Final Remarks. References. SECTION II: INTEGRATIVE DATA MINING AND VISUALIZATION -EMPHASIS ON COMBINATION OF MULTIPLE DATA TYPES. 4. Applications of Text Mining in Molecular Biology, from Name Recognition to Protein Interaction Maps (Martin Krallinger and Alfonso Valencia). 4.1 Introduction. 4.2 Introduction to Text Mining and NLP. 4.3 Databases and Resources for Biomedical Text Mining. 4.4 Text Mining and Protein-Protein Interactions. 4.5 Other Text-Mining Applications in Genomics. 4.6 The Future of NLP in Biomedicine. Acknowledgements. References. 5. Protein Interaction Prediction by Integrating Genomic Features and Protein Interaction Network Analysis (Long J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu, Falk Schubert and Mark Gerstein). 5.1 Introduction. 5.2 Genomic Features in Protein Interaction Predictions. 5.3 Machine Learning on Protein-Protein Interactions. 5.4 The Missing Value Problem. 5.5 Network Analysis of Protein Interactions. 5.6 Discussion. References. 6. Integration of Genomic and Phenotypic Data (Amanda Clare). 6.1 Phenotype. 6.2 Forward Genetics and QTL Analysis. 6.3 Reverse Genetics. 6.4 Prediction of Phenotype from Other Sources of Data. 6.5 Integrating Phenotype Data with Systems Biology. 6.6 Integration of Phenotype Data in Databases. 6.7 Conclusions. References. 7. Ontologies and Functional Genomics (Fátima Al-Shahrour and Joaquín Dopazo). 7.1 Information Mining in Genome-Wide Functional Analysis. 7.2 Sources of Information: Free Text Versus Curated Repositories. 7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics. 7.4 Using GO to Translate the Results of Functional Genomic Experiments into Biological Knowledge. 7.5 Statistical Approaches to Test Significant Biological Differences. 7.6 Using FatiGO to Find Significant Functional Associations in Clusters of Genes. 7.7 Other Tools. 7.8 Examples of Functional Analysis of Clusters of Genes. 7.9 Future Prospects. References. 8. The C. elegans Interactome: its Generation and Visualization (Alban Chesnau and Claude Sardet). 8.1 Introduction. 8.2 The ORFeome: the first step toward the interactome of C. elegans. 8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans Protein-Protein Interaction (Interactome) Network: Technical Aspects. 8.4 Visualization and Topology of Protein-Protein Interaction Networks. 8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale Genomics and Post-Genomics Data Sets. 8.6 Conclusion: From Interactions to Therapies. References. SECTION III: INTEGRATIVE DATA MINING AND VISUALIZATION - EMPHASIS ON COMBINATION OF MULTIPLE PREDICTION MODELS AND METHODS. 9. Integrated Approaches for Bioinformatic Data Analysis and Visualization - Challenges, Opportunities and New Solutions (Steve R. Pettifer, James R. Sinnott and Teresa K. Attwood). 9.1 Introduction. 9.2 Sequence Analysis Methods and Databases. 9.3 A View Through a Portal. 9.4 Problems with Monolithic Approaches: One Size Does Not Fit All. 9.5 A Toolkit View. 9.6 Challenges and Opportunities. 9.7 Extending the Desktop Metaphor. 9.8 Conclusions. Acknowledgements. References. 10. Advances in Cluster Analysis of Microarray Data (Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal and Bart De Moor). 10.1 Introduction. 10.2 Some Preliminaries. 10.3 Hierarchical Clustering. 10.4 k-Means Clustering. 10.5 Self-Organizing Maps. 10.6 A Wish List for Clustering Algorithms. 10.7 The Self-Organizing Tree Algorithm. 10.8 Quality-Based Clustering Algorithms. 10.9 Mixture Models. 10.10 Biclustering Algorithms. 10.11 Assessing Cluster Quality. 10.12 Open Horizons. References. 11. Unsupervised Machine Learning to Support Functional Characterization of Genes: Emphasis on Cluster Description and Class Discovery (Olga G. Troyanskaya). 11.1 Functional Genomics: Goals and Data Sources. 11.2 Functional Annotation by Unsupervised Analysis of Gene Expression Microarray Data. 11.3 Integration of Diverse Functional Data For Accurate Gene Function Prediction. 11.4 MAGIC - General Probabilistic Integration of Diverse Genomic Data. 11.5 Conclusion. References. 12. Supervised Methods with Genomic Data: a Review and Cautionary View (Ramón Díaz-Uriarte). 12.1 Chapter Objectives. 12.2 Class Prediction and Class Comparison. 12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes. 12.4 Class Prediction and Prognostic Prediction. 12.5 ROC Curves for Evaluating Predictors and Differential Expression. 12.6 Caveats and Admonitions. 12.7 Final Note: Source Code Should be Available. Acknowledgements. References. 13. A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models (Pedro Larrañaga, Iñaki Inza and Jose L. Flores). 13.1 Introduction. 13.2 Genetic Networks. 13.3 Probabilistic Graphical Models. 13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models. 13.5 Conclusions. Acknowledgements. References. 14. Integrative Models for the Prediction and Understanding of Protein Structure Patterns (Inge Jonassen). 14.1 Introduction. 14.2 Structure Prediction. 14.3 Classifications of Structures. 14.4 Comparing Protein Structures 14.5 Methods for the Discovery of Structure Motifs. 14.6 Discussion and Conclusions. References. Index.
£132.26
John Wiley & Sons Inc Practical Methods for Design and Analysis of
Book SynopsisStatistical complex survey analysis is a means to analyse the results, and gain information about a large population based on a complex survey of a sample of that population. A complex survey is a sample survey that divides the population into subgroups and collecting information from clusters within each subgroup and combining the results.Trade Review"As in the previous edition, this book is a good resource for practitioners and cross-disciplinary researchers who use data from complex survey designs." (Journal of the American Statistical Association, March 2006) "The first edition of the book was one of the first books in the excellent Wiley U.K. series on Statistics in Practice." (Technometrics, May 2005)Table of ContentsPreface. 1. Introduction. 2. Basic Sampling Techniques. 2.1 Basic definitions. 2.2 The Province’91 population. 2.3 Simple random sampling and design effect. 2.4 Systematic sampling and intra-class correlation. 2.5 Selection with probability proportional to size. 3. Further Use of Auxiliary Information. 3.1 Stratified sampling. 3.2 Cluster sampling. 3.3 Model-assisted estimation. 3.4 Efficiency comparison using design effects. 4. Handling Nonsampling Errors. 4.1 Reweighting. 4.2 Imputation. 4.3 Chapter summary and further reading. 5. Linearization and Sample Reuse in Variance Estimation. 5.1 The Mini-Finland Health Survey. 5.2 Ratio estimators. 5.3 Linearization method. 5.4 Sample reuse methods. 5.5 Comparison of variance estimators. 5.6 The Occupational Health C are Survey. 5.7 Linearization method for covariance-matrix estimation. 5.8 Chapter summary and further reading. 6. Model-assisted Estimation for Domains. 6.1 Framework for domain estimation. 6.2 Estimator type and model choice. 6.3 Construction of estimators and model specification. 6.4 Further comparison of estimators. 6.5 Chapter summary and further reading. 7. Analysis of One-way and Two-way Tables. 7.1 Introductory example. 7.2 Simple goodness-of-fit test. 7.3 Preliminaries for tests for two-way tables. 7.4 Test of homogeneity. 7.5 Test of independence. 7.6 Chapter summary and further reading. 8. Multivariate Survey Analysis. 8.1 Range of methods. 8.2 Types of models and options for analysis. 8.3 Analysis of categorical data. 8.4 Logistic and linear regression. 8.5 Chapter summary and further reading. 9. More Detailed Case Studies. 9.1 Monitoring quality in a long-term transport survey. 9.2 Estimation of mean salary in a business survey. 9.3 Model selection in a socioeconomic survey. 9.4 Multi-level modelling in an educational survey. References. Author Index. Subject Index. Web Extension. In addition to the printed book, electronic materials supporting the use of the book can be found in the web extension.
£100.76
John Wiley & Sons Inc How to Conduct Your Own Survey
Book SynopsisA nuts-and-bolts guide to conducting your own professional-quality surveys without paying professional fees. How can you gauge public support for a cause or test the market for a product or service? What are the best methods for validating opinions for use in a paper or dissertation? A well-documented survey is the answer.Table of ContentsPractical Surveys. Cornerstones of a Quality Survey. Deciding What Information You Need. Choosing a Survey Method. When and How to Select a Sample. Writing Good Questions. Questionnaire Design. Setting Your Survey in Motion and Getting It Done. From Questionnaires to Survey Results. Reporting Survey Results. Advice, Resources, and Maintaining Perspective. References. Index.
£31.88
John Wiley & Sons Inc How to Conduct Your Own Survey
Book SynopsisA nuts-and-bolts guide to conducting your own professional-quality surveys without paying professional fees. How can you gauge public support for a cause or test the market for a product or service? What are the best methods for validating opinions for use in a paper or dissertation? A well-documented survey is the answer.Table of ContentsPractical Surveys. Cornerstones of a Quality Survey. Deciding What Information You Need. Choosing a Survey Method. When and How to Select a Sample. Writing Good Questions. Questionnaire Design. Setting Your Survey in Motion and Getting It Done. From Questionnaires to Survey Results. Reporting Survey Results. Advice, Resources, and Maintaining Perspective. References. Index.
£18.39
John Wiley & Sons Inc Analysis of Survey Data
Book SynopsisThis book is concerned with statistical methods for the analysis of data collected from a survey. A survey could consist of data collected from a questionnaire or from measurements, such as those taken as part of a quality control process.Table of ContentsPreface. List of Contributors. Introduction (R. L. Chambers & C. J. Skinner). PART A: APPROACHES TO INFERENCE. Introduction to Part A (R. L.Chambers). Design-based and Model-based Methods for Estimating Model Parameters(David A. Binder and Georgia R. Roberts). The Bayesian Approach to Sample Survey Inference (Roderick J. Little). Interpreting a Sample as Evidence about a Finite Population (Richard Royall). PART B: CATEGORICAL RESPONSE DATA. Introduction to Part B (C. J.Skinner). Analysis of Categorical Response Data from Complex Surveys: an Appraisal and Update (J. N. K. Rao and D. R. Thomas). Fitting Logistic Regression Models in Case-Control Studies with Complex Sampling (Alastair Scott and Chris Wild). PART C: CONTINUOUS AND GENERAL RESPONSE DATA. Introduction to Part C (R. L.Chambers). Graphical Displays of Complex Survey Data through Kernel Smoothing (D. R. Bellhouse, C. M. Goia, and J. E. Stafford) Nonparametric Regression with Complex Survey Data (R. L. Chambers, A. H. Dorfman and M. Yu. Sverchkov). Fitting Generalized Linear Models under Informative Sampling (Danny Pfeffermann and M. Yu. Sverchkov). PART D: LONGITUDINAL DATA. Introduction to Part D (C. J.Skinner). Random Effects Models for Longitudinal Survey Data (C. J.Skinner and D. J.Holmes). Event History Analysis and Longitudinal Surveys (J. F. Lawless). Applying Heterogeneous Transition Models in Labour Economics: the Role of Youth Training in Labour Market Transitions (Fabrizia Mealli and Stephen Pudney). PART E: INCOMPLETE DATA. Introduction to Part E (R. L.Chambers). Bayesian Methods for Unit and Item Nonresponse (Roderick J. Little). Estimation for Multiple Phase Samples (Wayne A. Fuller). Analysis Combining Survey and Geographically Aggregated Data (D. G. Steel, M. Tranmer and D. Holt). References. T. M. F.Smith: Publications up to 2002. Author Index. Subject Index.
£109.76
Faber & Faber Facts are Sacred
Book SynopsisWhat is the true human cost of the war in Afghanistan? What are the real effects of the austerity measure? And how did the London riots spread so quickly?Facts are Sacred, the Guardian''s award-winning datablog, publishes and analyses seemingly benign data - released under the auspices of transparency - to bring its readers astonishing revelations about the way we live now. It reveals how data has changed our world and what we can learn from it. Now, the most telling findings from the blog are brought together to give us the facts and figures behind the headlines, beautifully illustrated with extensive data visualisations. Ground-breaking and fascinating, it celebrates a resource that has pushed the boundaries of modern journalism and is a manifesto for a new way of seeing things.
£20.00
Princeton University Press Data Analysis for Scientists and Engineers
Book SynopsisTrade Review"Robinson's text is an excellent overview of modern statistical techniques and is sure to become a definitive reference. He ably and concisely presents all of the necessary foundational mathematics while also providing a thorough description of sophisticated methods used by practicing engineers and scientists. I particularly enjoyed the division of the book into frequentist and Bayesian approaches and Robinson's clear discussion of the relative merits of each method."—Jeremy Kasdin, Princeton University"With an accessible and consistent style, Data Analysis for Scientists and Engineers stands out for its depth of materials and pedagogical presentation. Building from simple concepts, the book's mathematical rigor and accuracy are solid and logical. This book is appropriate for senior undergraduates, graduate students at all levels, and practicing scientists."—Wade Fisher, Michigan State University
£68.00
Princeton University Press Quantitative Biosciences
Book Synopsis
£50.00
Princeton University Press Data Analysis for Social Science
Book Synopsis
£84.00
Princeton University Press Data Analysis for Social Science
Book Synopsis
£32.30
Princeton University Press You Are Not Expected to Understand This
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 *
£15.29
Princeton University Press Thinking Clearly with Data A Guide to
Book SynopsisTrade Review"I very much recommend this book, not only to all that teach statistics to (under)graduate students, but also those that use statistics for their own research, that would like to value the work of others, or engage in debates using actual or perceived facts."---Gijs Dekkers, International Statsitical Review
£70.40
Princeton University Press Quantitative Social Science
Book Synopsis
£80.00
Princeton University Press Data Science for Neuroimaging
Book Synopsis
£32.30
Princeton University Press Modeling Social Behavior
Book Synopsis
£106.25
Princeton University Press Modeling Social Behavior
Book Synopsis
£40.00
Pluto Press Data Power
Book SynopsisAn introduction to learning how to protect ourselves and organise against Big DataTrade Review'A call to arms [...] sets out a clear, persuasive argument for the need to challenge the power of platforms and systems, and details the tools to do so. A thought-provoking read' -- Prof. Rob Kitchin, Maynooth University‘The first non-technical guidebook on how to live with location data and it is a truly radical response for our times. Spatial data for us, not about us’ -- Jeremy W. Crampton, Professor of Urban Data Analysis, Newcastle University‘Brilliantly traces the closed loops of spatial data and suggests new escape routes, reminding us that our data can be remade to tell different stories’ -- Professor Kate Crawford, author of ‘Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence’'The book that I’ve long been waiting for, one that takes a material approach to the data geographies informing and being informed by technologies of everyday life’ -- Erin McElroy, Assistant Professor of American and Digital Studies at the University of Texas at Austin and cofounder of the Anti-Eviction Mapping Project'Data Power is an activist handbook wrapped in a theoretical treatise inside a media manifesto. The authors have a lively set of suggestions that provide a welcome antidote to the temptations of resignation and complacency' -- Mark Andrejevic, Professor in the School of Media, Film, and Journalism at Monash UniversityTable of ContentsList of Figures and Tables Series Preface Acknowledgments List of Abbreviations Introduction: Technology and the Axes of Hope and Fear 1. Life in the Age of Big Data 2. What Are Our Data, and What Are They Worth? 3. Existing Everyday Resistances 4. Contesting the Data Spectacle 5. Our Data Are Us, So Make Them Ours Epilogue Notes Bibliography Index
£17.99
Pluto Press Data Power Radical Geographies of Control and
Book SynopsisAn introduction to learning how to protect ourselves and organise against Big DataTrade Review'A call to arms [...] sets out a clear, persuasive argument for the need to challenge the power of platforms and systems, and details the tools to do so. A thought-provoking read' -- Prof. Rob Kitchin, Maynooth University‘The first non-technical guidebook on how to live with location data and it is a truly radical response for our times. Spatial data for us, not about us’ -- Jeremy W. Crampton, Professor of Urban Data Analysis, Newcastle University‘Brilliantly traces the closed loops of spatial data and suggests new escape routes, reminding us that our data can be remade to tell different stories’ -- Professor Kate Crawford, author of ‘Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence’'The book that I’ve long been waiting for, one that takes a material approach to the data geographies informing and being informed by technologies of everyday life’ -- Erin McElroy, Assistant Professor of American and Digital Studies at the University of Texas at Austin and cofounder of the Anti-Eviction Mapping Project'Data Power is an activist handbook wrapped in a theoretical treatise inside a media manifesto. The authors have a lively set of suggestions that provide a welcome antidote to the temptations of resignation and complacency' -- Mark Andrejevic, Professor in the School of Media, Film, and Journalism at Monash UniversityTable of ContentsList of Figures and Tables Series Preface Acknowledgments List of Abbreviations Introduction: Technology and the Axes of Hope and Fear 1. Life in the Age of Big Data 2. What Are Our Data, and What Are They Worth? 3. Existing Everyday Resistances 4. Contesting the Data Spectacle 5. Our Data Are Us, So Make Them Ours Epilogue Notes Bibliography Index
£68.00
Kogan Page Ltd Practical Text Analytics
Book SynopsisSteven Struhl PhD, MBA, MA has more than 25 years' experience in consulting and research, specializing in practical solutions based on statistical models of decision-making and behaviour. In addition to text analytics and data mining, his work addresses how buying decisions are made, optimizing service delivery and product configurations and finding the meaningful differences among products and services. Steven also has taught graduate courses on statistical methods and data analysis. He speaks at conferences and has given numerous seminars on pricing, choice modelling, market segmentation and presenting data.Trade Review"Textual analysis has recently become a useful research methodology, of great interest to both academics and practitioners. Dr. Steven Struhl provides relevant and lucid discussion of the topic, highlighting the fundamental issues involved in preparing, analyzing, and presenting textual data for meaningful interpretations. A very interesting and timely contribution that should be of interest to a wide range of audiences." * Dr. Jehoshua Eliashberg, Sebastian S. Kresge Professor of Marketing, Professor of Operations and Information Management, Wharton University *"Steven provides a broad and fair context in which to understand textual analysis in a very readable and informative way. I'm confident this would provide great value to anyone with an interest in the Internet and textual analysis, researcher and non-researcher alike." * Darrin Helsel, Co-Founder and Principal of Distill Research LLC, and Research Chair, American Marketing Association, Portland Chapter *"Steven Struhl has an incredible knack for demystifying complex analyses and analytic software, and making it accessible to those who are interested in what it does without delving too deeply into the incomprehensible elements of how it works. In his new book, Dr. Struhl takes on text analytics. I found the chapter on Bayes Nets particularly useful. In it he shows quite convincingly that, in some cases, they do a much better job with text than other predictive methods. He provides a story through crystal-clear examples that are immediately interesting and easy to follow." * Larry Durkin, Principal, MSP Analytics *"As I've been evaluating text analytics materials lately for my data science education engagements, much of what I've found published on this subject is written from a very academic and technical perspective that is not very approachable for someone that doesn't have a fairly deep expertise in statistics, math and programming. This book solves that disconnect. A welcome addition to any data scientist's library. In addition, the timely nature of the subject should provide much food-for-thought as the rise in interest in unstructured data processing techniques continues to be of interest. Highly recommended." * Daniel D. Gutierrez, Inside Big Data *"A fascinating, if not rather specialist book, which aims to be an accessible guide to the world of text analytics and data analysis for marketing folk." * Darren Ingram, Darren Ingram Media *Table of Contents Chapter - 01: Who should read this book? And what do you want to do today?; Chapter - 02: Getting ready: capturing, sorting, sifting, stemming and matching; Chapter - 03: In pictures: word clouds, wordles and beyond; Chapter - 04: Putting text together: clustering documents using words; Chapter - 05: In the mood for sentiment (and counting) ; Chapter - 06: Predictive models 1: having words with regressions; Chapter - 07: Predictive models 2: classifications that grow on trees; Chapter - 08: Predictive models 3: all in the family with Bayes Nets; Chapter - 09: Looking forward and back
£33.24
Kogan Page Ltd Practical Text Analytics
Book SynopsisSteven Struhl PhD, MBA, MA has more than 25 years' experience in consulting and research, specializing in practical solutions based on statistical models of decision-making and behaviour. In addition to text analytics and data mining, his work addresses how buying decisions are made, optimizing service delivery and product configurations and finding the meaningful differences among products and services. Steven also has taught graduate courses on statistical methods and data analysis. He speaks at conferences and has given numerous seminars on pricing, choice modelling, market segmentation and presenting data.Trade Review"Textual analysis has recently become a useful research methodology, of great interest to both academics and practitioners. Dr. Steven Struhl provides relevant and lucid discussion of the topic, highlighting the fundamental issues involved in preparing, analyzing, and presenting textual data for meaningful interpretations. A very interesting and timely contribution that should be of interest to a wide range of audiences." * Dr. Jehoshua Eliashberg, Sebastian S. Kresge Professor of Marketing, Professor of Operations and Information Management, Wharton University *"Steven provides a broad and fair context in which to understand textual analysis in a very readable and informative way. I'm confident this would provide great value to anyone with an interest in the Internet and textual analysis, researcher and non-researcher alike." * Darrin Helsel, Co-Founder and Principal of Distill Research LLC, and Research Chair, American Marketing Association, Portland Chapter *"Steven Struhl has an incredible knack for demystifying complex analyses and analytic software, and making it accessible to those who are interested in what it does without delving too deeply into the incomprehensible elements of how it works. In his new book, Dr. Struhl takes on text analytics. I found the chapter on Bayes Nets particularly useful. In it he shows quite convincingly that, in some cases, they do a much better job with text than other predictive methods. He provides a story through crystal-clear examples that are immediately interesting and easy to follow." * Larry Durkin, Principal, MSP Analytics *"As I've been evaluating text analytics materials lately for my data science education engagements, much of what I've found published on this subject is written from a very academic and technical perspective that is not very approachable for someone that doesn't have a fairly deep expertise in statistics, math and programming. This book solves that disconnect. A welcome addition to any data scientist's library. In addition, the timely nature of the subject should provide much food-for-thought as the rise in interest in unstructured data processing techniques continues to be of interest. Highly recommended." * Daniel D. Gutierrez, Inside Big Data *"A fascinating, if not rather specialist book, which aims to be an accessible guide to the world of text analytics and data analysis for marketing folk." * Darren Ingram, Darren Ingram Media *Table of Contents Chapter - 01: Who should read this book? And what do you want to do today?; Chapter - 02: Getting ready: capturing, sorting, sifting, stemming and matching; Chapter - 03: In pictures: word clouds, wordles and beyond; Chapter - 04: Putting text together: clustering documents using words; Chapter - 05: In the mood for sentiment (and counting) ; Chapter - 06: Predictive models 1: having words with regressions; Chapter - 07: Predictive models 2: classifications that grow on trees; Chapter - 08: Predictive models 3: all in the family with Bayes Nets; Chapter - 09: Looking forward and back
£95.00
Kogan Page Ltd Predictive Analytics for Marketers
Book SynopsisDr Barry Leventhal is a leading UK authority on geodemographics and a marketing analytics expert. He is Emeritus Chair of the Census and Geodemographics Group (CGG), which is an advisory board of The Market Research Society (MRS) and a leading voice in the UK information industry. He was recently awarded the MRS Gold Medal Award - the association's rarest accolade, presented for the first time since 2008 - in recognition of his lifetime of exceptional achievement and contribution to the research profession.Trade Review"This book is an invaluable aid in the journey from big data to smart data usage, which is where competitive advantage rests. Leventhal delivers lashings of common sense based on erudition and experience, making this a very pragmatic and useful work." * Jane Frost CBE, Chief Executive Office, Market Research Society *"A comprehensive, engaging and accessible introduction to the increasingly important field of predictive analytics and marketing from one of the leading practitioners. Leventhal takes each of the main application areas in turn and focuses on how to generate value from data for your organization." * Tom Smith, Managing Director, Office for National Statistics (ONS) Data Science Campus. *"Leventhal masterfully presents a complex subject in a highly accessible way, liberally illustrating the material with real-life examples from his own experience." * Professor David J. Hand, Emeritus Professor of Mathematics, Imperial College London and Chief Scientific Advisor, Winton Group *"Leventhal has distilled his wealth of rich practical experience into a clear and comprehensive text, sharing best practice in methods for collecting data, building models, and operationalizing and leveraging the power of data to maximize economic value. A mandatory book for anyone working with customer data or predictive analytics." * Paul Cushion, Customer and Digital Associate, KPMG Management Consultancy *"I highly recommend this book both to those starting out in a career in marketing and to those seasoned marketers in need of some new tricks if they are to stay relevant." * Giles Pavey, Head of Data Strategy at the Department for Work & Employment and Former Chief Data Scientist at dunnhumby Ltd *"In a world teeming with data, competitive advantage now firmly lies in how effectively data is analysed. This book provides a comprehensive guide on how to approach, execute, evaluate and get the most out of predictive analytics. It is very easy to read - even for the non-statistically minded." * Lynne Robinson, Research Director, Institute of Practitioners in Advertising (IPA) *"If you think predictive analytics is not for you, think again. It is vital for anyone in any management capacity. Leventhal's Predictive Analytics for Marketers is required reading for anyone who needs to understand the latest practical methods to segment and analyse data, whether for the public or the private sector, or to predict future success or understand reasons for failure." * Roger Holland, Executive Chairman, JICPOPS (the Joint Industry Committee for Population Standards) *"Throughout, this is a very practical guide, with a number of marketing-focused case studies bringing the power of the analytical techniques discussed to life. A book that's very definitely not just for the shelf!" * Paul Cresswell, Head of Data Governance, Experian Marketing Services - Targeting *"Predictive Analytics for Marketers clearly explains the analytics process and its commercial context in language understandable to managers, marketers, IT specialists and analysts. It addresses the essential areas of communication between these specialisms, giving lucid accounts of the process of planning an analytics project, the importance of framing the business problem, and the need for its alignment with appropriate methods. Leventhal's book is a welcome addition, covering current topics in analytics clearly and insightfully." * David Harris, Product Development Partner, CACI Ltd. *"This is much more than a lucid and comprehensive textbook on predictive analytics. Leventhal's profound expertise shines through as he shares his thoughts from a practical as well as technical point of view. For businesses who wish to be data driven, this unambiguous and wise advice will provide an accelerated path to success." * Gordon Farquharson, Director of Analytics, more2 ltd *"Leventhal helpfully clarifies key concepts and gives sound and practical advice, drawing on his extensive experience in marketing. No matter how much you think you know about analytics, I suggest you read this book, apply it, and benefit from it!" * Paul Allin, Visiting Professor in Statistics, Imperial College London *Table of Contents Section - 00: Introduction to predictive analytics; Section - 01: How can predictive analytics help your business?; Section - 02: Using data mining to build predictive models; Section - 03: Managing the data for predictive analytics; Section - 04: The analytical modelling toolkit; Section - 05: Software solutions for predictive analytics; Section - 06: Predicting customer behaviour using analytical models; Section - 07: Predicting lifetimes – from customers to machines; Section - 08: How to build a customer segmentation; Section - 09: Accounts, baskets, citizens or businesses – applying predictive analytics in various sectors; Section - 10: From people to products – using predictive analytics in retail; Section - 11: How to benefit from social network analysis; Section - 12: Testing the benefits of predictive models and other marketing effects; Section - 13: Top tips for gaining business value from predictive analytics;
£25.64
Ebury Publishing Think Clearly
Book SynopsisKiko Llaneras is a data journalist at El País and holds a Ph.D. in industrial engineering. He has taught at the University of Girona and the Polytechnic University of Valencia.
£15.29
John Wiley and Sons Ltd Animal Disease Surveillance and Survey Systems
Book SynopsisThis valuable text presents methods and techniques for conducting an animal disease surveillance program, and developing an animal health moitoring system. The text is a ''recipe book'' for these techniques as it explains modern techniques, while emphasizing the fundamentals and principles of using these techniques.The book is targeted to epidemiologists and other animal health authorities who are working in national, regional, and international programs. The book can be used as a text for professional and postgraduate training curricula. This text will be of value in veterinary epidemiology and regulatory medicine, where there is need for a concise collection of material on animal disease monitoring, surveillance, and reporting strategies. This need arises from a new era of international trade regulations based on animal diseases, new demands for accountability in utilization of research funds, and calls for prioritizing and economically justifying animal health regulatory aTable of ContentsPreface. Contributors. Chapter 1. Surveillance and Monitoring Systems for Animal Health Program and Disease Suveys (M.D. Salman). Chapter 2. Application of Surveillance and Monitoring Systems in Disease Control Programs (J. Christensen). Chapter 3. Planning Survey, Surveillance, and Monitoring Systems-Roles and Requirements (C. Zepeda and M.D. Salman). Chapter 4. Sampling Considerations in Surveys and Monitoring and Surveillance Systems (A. Cameron, I. Gardner, M.G. Doherr, and B. Wagner). Chapter 5. Statistical Analysis of Data from Surveys, Monitoring, and Surveillance Systems (B. Wagner, I. Gardner, A. Cameron, and M.G. Doherr). Chapter 6. Methods for Determining Temporal Clusters in Surveillance and Survey Programs (T.E. Carpenter and M.P. Ward). Chapter 7. Methods for Determining Spartial Clusters in Surveillance and Survey Programs (T.E. Carpenter and M.P. Ward). Chapter 8. Use of Sentinel Herds in Monitoring and Surveillance Systems (B.J. McCluskey). Chapter 9. Use of Animal Monitoring and Surveillance Systems When the Frequency of Health-Related Events is Near Zero (M.G. Doherr, L. Audigé, M.D. Salman, and I.A. Gardner). Chapter 10. Use of Simulation Models in Surveillance and Monitoring Systems (L. Audigé, M.G. Doherr, and B. Wagner). Chapter 11. Quality Assessment of Animal Disease Surveillance and Survey Systems (K.D.C. Stärk). Chapter 12. Dissemination of Surveillance Findings (N.E. Wineland and D.A. Dargatz). Chapter 13. Danish Swine Salmonellosis Control Program: 1993 to 2001 (J. Christensen). Index.
£75.56
MP-ALA American Library Assoc 3D Data Creation to Curation Community Standards
Book SynopsisCovers best practices for 3D data preservation, management, metadata, legal issues, and access. Beginning with surveys of current practices, the authors provide recommendations for implementing standards and identify areas in which further development is required. A glossary of key terms and acronyms is included for easy reference.Table of Contents Acknowledgments Chapter 1. Introduction Jennifer Moore, Adam Rountrey, and Hannah Scates Kettler Context for This Work The Democratization of 3D Data Production The Audience The Creators Values of CS3DP From Creation to Preservation Modalities Represented in the Chapters What to Expect Notes Bibliography Chapter 2. Best Practices for 3D Data Preservation Kristina Golubiewski-Davis, Jessica Maisano, Marcia McIntosh, Jennifer Moore, Kieron Niven, Will Rourk, and Rebecca Snyder Introduction Existing Standards Preservation Intervention Points Documentation Good/Better/Best Recommendations for Implementation Conclusion Notes Bibliography Chapter 3. Management and Storage of 3D Data Doug Boyer, Rachel Fernandez, Monique Lassere, Marcia McIntosh, Jennifer Moore, Francis P. McManamon, Albert Rozo, Todd P. Swanson, and Kate Webbink Introduction Survey Overview Management Technology Sustainability Conclusion Notes Bibliography Chapter 4. Metadata Requirements for 3D Data Jon Blundell, Jasmine L. Clark, Katherine E. DeVet, and Juliet L. Hardesty Introduction Methods Considerations, Decisions, and Scope Digital Asset Life Cycle and 3D Metadata Gap Analysis/Future Work Conclusion: Summary Recommendations Acknowledgments Notes Bibliography Chapter 5. Copyright and Legal Issues Surrounding 3D Data Andrea D’Andrea, Michael Conyers, Kyle K. Courtney, Emily Finch, Melissa Levine, Nicole Meyer, Adam Rountrey, Hannah Scates Kettler, Kate Webbink, and Ann Whiteside Introduction Foundations: Copyright and the “Bundle of Rights” Case Studies Conclusion Notes Bibliography Chapter 6. Accessing 3D Data Francesca Albrezzi, John Bonnett, Tassie Gniady, Heather Richards-Rissetto, and Lisa M. Snyder Introduction Modes of 3D Data Audiences for 3D Data Discovering 3D Assets and Decision-Making Issues Technology Requirements and Limitations Impacting Access Use Case Challenges and Outstanding Questions Recommendations for Next Steps Conclusion Notes Bibliography Chapter 7. Conclusion Jennifer Moore, Adam Rountrey, and Hannah Scates Kettler How Are 3D Data Different? Ideas from the Community Assessing Our Approach (CoP) Going Forward Notes Bibliography Glossary Biographies
£77.25
MP-ALA American Library Assoc The Data Literacy Cookbook
Book SynopsisPresents a variety of approaches to and lesson plans for teaching data literacy, from simple activities to self-paced learning modules to for-credit and discipline-specific courses. Sixty-five recipes are organised into nine sections based on learning outcomes.Table of Contents Introduction Section 1. Interpreting Polls and Surveys Chapter 1. Survey Literacy: A Skills-Based Approach to Teaching Survey Research Jesse Klein Chapter 2. Setting the Scene with Surveys: Using Polling Software to Demonstrate Primary and Secondary Data Wendy G. Pothier Chapter 3. The Mini-study: A Three-Part Assignment for Original Data Creation, Summation, and Visualization William Cuthbertson, Lyda Fontes McCartin, and Sara O’Donnell Section 2. Finding and Evaluating Data Chapter 4. Three-Step Data Searching Annelise Sklar Chapter 5. Transforming Research Questions into Variables: A Recipe for Finding Secondary Data Alicia Kubas and Jenny McBurney Chapter 6. Sweeten the Search: Discover Data for Reuse with a Tool That Links Publications to the Underlying Data Elizabeth Moss Chapter 7. The Most Vital Statistics: Finding and Analyzing Historical Mortality Rates Alisa Beth Rod and Jennie Correia Chapter 8. Understanding the Enumerated World: Making Sense of Data as an Information Source Alexandra Cooper, Elizabeth Hill, and Kristi Thompson Chapter 9. Looking at Data Kay K. Bjornen Chapter 10. Interrogating the Data: What Data Sets Can and Cannot Tell Us Kristin Fontichiaro Chapter 11. Data Zines: A Hands-On Approach to Community Curiosities Tess Wilson Chapter 12. On the Hunt: Understanding and Analyzing GSS Data Extraction for Incorporation within Sociological Research Projects Amy Dye-Reeves Chapter 13. Using Statistics to Define the Problem: Data and Service Learning Amy Harris Houk and Jenny Dale Chapter 14. Data and Statistics in the News and Media Kaetlyn Phillips Section 3. Data Manipulation and Transformation Chapter 15. A Kinesthetic Approach to Data: Moving to Understand Nominal, Ordinal, Interval, and Ratio Relationship in Data Wendy Stephens Chapter 16. Text Mining Charcuterie Board Yun Dai and Fan Luo Chapter 17. Anyone Can Cook (R)! Open Data with R, a Five-Week Mini-mester Jay Forrest and Ameet Doshi Chapter 18. Software Carpentry Al Dente: Rendering Tech Training for Online Artisans Peace Ossom-Williamson, Shiloh Williams, and Hammad Rauf Khan Chapter 19. A Recipe for Improving Online Instruction for the Carpentries Kay K. Bjornen and Clarke Iakovakis Section 4. Data Visualization Chapter 20. Correlation Does Not Equal Causality: Introducing Data Literacy through Infographics and Statistics in the Media Nick Ruhs Chapter 21. Pies, Bars, Charts, and Graphs, Oh My! A Data Visualization Appetizer Haley L. Lott Chapter 22. Data Visualizations: The Good, the Bad, and the Ugly Kaetlyn Phillips Chapter 23. Seasonal Visual Literacy: Using Current Events to Teach Data and Spatial Literacy Skills with Adaptable LibGuides Jacqueline Fleming and Theresa Quill Chapter 24. To Visualize Is to Experience Data Chapter elsea H. Barrett and Gerard Shea Chapter 25. Upping the Baseline for Data Literacy Instruction Jessica Vanderhoff Chapter 26. A Literacy-Based Approach to Learning Visualization with R’s ggplot2 Package Angela M. Zoss Chapter 27. Build Your Own Data Viz Pizza: A Modular Approach to Data Visualization Instruction Rachel Starry Chapter 28. Veggie Pizza: Choosing a Data Visualization Tool Rachel Starry Chapter 29. Four-Cheese Pizza: Color and Accessible Design Rachel Starry Chapter 30. Data Visualization using Web Apps in a Rainbow Layer Cake Yun Dai and Fan Luo Chapter 31. Graphical Abstracts: Creating Appetizing Infographics for Your Research Article Aleshia Huber Section 5. Data Management and Sharing Chapter 32. Making File Names for Digital Exhibits Kate Thornhill and Gabriele Hayden Chapter 33. Data Management Failures: Teaching the Importance of DMPs through Cautionary Examples Richard M. Mikulski Chapter 34. Low-Fat Research Data Management Elizabeth Blackwood Chapter 35. Managing Qualitative Social Science Data: An Open, Self-Guided Course Sebastian Karcher and Diana Kapiszewski Chapter 36. Seven Weeks, Seven DMPs: Iterative Learning around Data Management Plan Creation Emma Slayton and Hannah C. Gunderman Chapter 37. Equitable from the Beginning: Incorporating Critical Data Perspectives into Your Research Design Jodi Coalter, David Durden, and Leigh Amadi Dunewood Section 6. Geospatial Data Chapter 38. Challenge Accepted: Introducing Geospatial Data Literacy through an Online Learning Path Joshua Sadvari and Katie Phillips Chapter 39. GIS for Success Series: Learning the Basics of QGIS Workshop Kelly Grove Chapter 40. GIS for Success Series: Let’s Make a Map in QGIS Workshop Kelly Grove Chapter 41. Statistical and Geospatial Literacy for Integrative Genetics Jay Forrest and Chrissy Spencer Chapter 42. Web Map Layer Cake: Teaching Web Mapping Skills with Leaflet for R Sarah Zhang and Julie Jones Section 7. Data in the Disciplines Chapter 43. Data in Context: How Data Fit into the Scholarly Conversation Theresa Burress Chapter 44. Let the Dough Rise! Integrating Library Instruction in a Digital Humanities Course RenÉ Duplain and Chantal Ripp Chapter 45. Ethics and Biodiversity Data Rebecca Hill Renirie Chapter 46. Data Decisions and the Research Process in the Sciences and Social Sciences Nicole Helregel Chapter 47. Financial Data for Economics Students Jennifer Yao Weinraub Chapter 48. Stuffed Shiny App with Business Intelligence Yun Dai and Fan Luo Chapter 49. Fast Casual Marketing Strategies Juliann Couture, Halley Todd, and Natalia Tingle Dolan Chapter 50. When and Where: A Framework for Finding and Evaluating Social Science Data for Reuse Ari Gofman Chapter 51. Data Literacy Layered Lasagna for Preservice Teachers Brad Dennis and Allison Hart-Young Section 8. Data Literacy Outreach and Engagement Chapter 52. Data Visualization Day: Promoting Data Literacy with Campus Partners Wenli Gao Chapter 53. Getting Messy Ourselves: An Experiential Learning Curriculum for Subject Librarians to Engage with Data Literacy Adrienne Canino Chapter 54. Research Data Management Stone Soup: Gauging Team Competencies Michelle Armstrong, Megan Davis, Ellie Dworak, Yitzhak “Yitzy” Paul, and Elisabeth Shook Chapter 55. Data Literacy Family Style: Full-Day Professional Development Molly Ledermann, Emilia Marcyk, Terence O’Neill, and Dianna E. Sachs Chapter 56. Everyone Is Welcome at the Table: Outreach for Data Management and Data Literacy in Research Assignment Design Shannon Sheridan and Hilary Baribeau Chapter 57. Seasoning and Simmering: Cultivating Data Literacy Skills through an Open Data Hackathon Peace Ossom-Williamson Chapter 58. From Soup to Nuts: Finding Your Way around the Data Services Buffet Jane Fry and Chantal Ripp Chapter 59. Teaching Data Literacy and Computational Thinking in Educational Technology Lesley S. J. Farmer Section 9. Data Literacy Programs and Curricula Chapter 60. Cooking Up a Data Literacy Course Claire Nickerson Chapter 61. Baking a Data Layer Cake: Scaffolding Data Skills through Video Vignettes Shannon Sheridan Chapter 62. Building Data Literacy through Scaffolded Workshops: Experiences and Challenges Jiebei Luo and Yaqing (Allison) Xu Chapter 63. Data Literacy Appetizers: LibGuide Data Instruction Modules for Undergraduates Beth Hillemann and Aaron Albertson Chapter 64. Data as Curation: Framing Data Creation as a Critical Practice through Collections-Based Research Inquiry Gesina A. Phillips, Tyrica Terry Kapral, Matthew J. Lavin, and Aaron Brenner Chapter 65. Quantitative Data Skills for Undergraduates: A Seminar Series for Social Science Students Whitney Kramer and Amelia Kallaher
£66.00
Cambridge University Press Data Modeling for the Sciences
Book SynopsisThis accessible guide to data modeling introduces basic probabilistic concepts, gradually building toward state-of-the art data modeling and analysis techniques. Aimed at students and researchers in the sciences, the text is self-contained and pedagogical, including practical examples and end of chapter problems.Table of ContentsPart I. Concepts from Modeling, Inference, and Computing: 1. Probabilistic modeling and inference; 2. Dynamical systems and Markov processes; 3. Likelihoods and latent variables; 4. Bayesian inference; 5. Computational inference; Part II. Statistical Models: 6. Regression models; 7. Mixture models; 8. Hidden Markov models; 9. State-space models; 10. Continuous time models*; Part III. Appendix: Appendix A: Notation and other conventions; Appendix B: Numerical random variables; Appendix C: The Kronecker and Dirac deltas; Appendix D: Memoryless distributions; Appendix E: Foundational aspects of probabilistic modeling; Appendix F: Derivation of key relations; References; Index.
£56.99
Cambridge University Press Working with Network Data
Book SynopsisDrawing examples from real-world networks, this essential book traces the methods behind network analysis and equips you with a toolbox of diverse methods and data modelling approaches. Suitable for both graduate students and researchers across a range of disciplines, this novel text provides a fast-track to network data expertise.
£47.49
Cambridge University Press A Practical Guide to Data Analysis Using R
Book SynopsisUsing diverse real-world examples, this book explores the use of R for data analysis, with extensive use of graphical presentation. It assists scientists in the analysis of their own data, demonstrating how to check the underlying assumptions, and gives students in statistical theory exposure to practical data analysis.
£66.49
Cambridge University Press Applied Longitudinal Data Analysis for Medical
Book SynopsisDiscusses methods available for longitudinal data analysis in non-technical language, allowing readers to apply techniques easily to their work. Aimed at non-statisticians and researchers working in medical science and utilising longitudinal studies, the interpretation of the results of various methods of analysis is emphasised.Table of Contents1. Introduction; 2. Continuous outcome variables; 3. Continuous outcome variables – regression based methods; 4. The modelling of time; 5. Models to disentangle the between- and within-subjects relationship; 6. Causality in observational longitudinal studies; 7. Dichotomous outcome variables; 8. Categorical and count outcome variables; 9. Outcome variables with floor or ceiling effects; 10. Analysis of longitudinal intervention studies; 11. Missing data in longitudinal studies; 12. Sample size calculations; 13. Software for longitudinal data analysis.
£47.49
Cambridge University Press LargeScale Data Analytics with Python and Spark
Book SynopsisA hands-on textbook teaching how to carry out large-scale data analytics and implement machine learning solutions for big data. Including copious real-world examples, it offers a coherent teaching package with lab assignments, exercises, solutions for instructors, and lecture slides.Trade Review'With the growing ubiquity of large and complex datasets, MapReduce and Spark's dataflow programming models have become mission-critical skills for data scientists, data engineers, and ML engineers. Triguero and Galar leverage their extensive teaching experience on this topic to deliver this tour de force deep dive into both the technical concepts and programming knowhow needed for such modern large-scale data analytics. They interleave intuitive exposition of the concepts and examples from data engineering and classical ML pipelines with well-thought-out hands-on code and outputs. This book not only shows how all this knowledge is useful in practice today but also sets up the reader to be able to successfully 'generalize' to future workloads.' Arun Kumar, University of California, San DiegoTable of ContentsPart I. Understanding and Dealing with Big Data: 1. Introduction; 2. MapReduce; Part II. Big Data Frameworks: 3. Hadoop; 4. Spark; 5. Spark SQL and DataFrames; Part III. Machine Learning for Big Data: 6. Machine Learning with Spark; 7. Machine Learning for Big Data; 8. Implementing Classical Methods: k-means and Linear Regression; 9. Advanced Examples: Semi-supervised, Ensembles, Deep Learning Model Deployment.
£28.49
Cambridge University Press Inverse Problems and Data Assimilation
Book SynopsisThis concise introduction covers inverse problems and data assimilation, before exploring their inter-relations. Suitable for both classroom teaching and self-guided study, it is aimed at advanced undergraduates and beginning graduate students in mathematical sciences, together with researchers in science and engineering.Table of ContentsIntroduction; Part I. Inverse Problems: 1. Bayesian inverse problems and well-posedness; 2. The linear-Gaussian setting; 3. Optimization perspective; 4. Gaussian approximation; 5. Monte Carlo sampling and importance sampling; 6. Markov chain Monte Carlo; Exercises for Part I; Part II. Data Assimilation: 7. Filtering and smoothing problems and well-posedness; 8. The Kalman filter and smoother; 9. Optimization for filtering and smoothing: 3DVAR and 4DVAR; 10. The extended and ensemble Kalman filters; 11. Particle filter; 12. Optimal particle filter; Exercises for Part II; Part III. Kalman Inversion: 13. Blending inverse problems and data assimilation; References; Index.
£37.88
Cambridge University Press Linear Algebra for Data Science Machine Learning
Book SynopsisMaster basic matrix methods by seeing how the mathematics is used in practice in a range of data-driven applications. Includes a wealth of engaging exercises for quizzes, self-study and interactive learning, as well as online JULIA demos offering a hands-on learning experience for upper-level undergraduates and first-year graduate students.
£47.49
Cambridge University Press Financial Data Science
Book Synopsis
£56.99
Cambridge University Press Tangles
Book SynopsisThe mathematical theory of tangles, the centrepiece of the celebrated Robertson-Seymour theory of graph minors, finds precise structure in imprecise data. Assuming only basic undergraduate mathematics, this book shows how tangles can identify, relate, and structure types in data: of behaviour, political views, texts, or proteins.
£47.49
Cambridge University Press Designing Empirical Social Networks Research
Book SynopsisUser-friendly guide to help design research about how and why social networks matter.Focused on political scientists with applications across the social sciences, this book will get researchers building a theory, designing a strategy to collect data, preparing the data for analyses, conducting preliminary analyses, and planning the next steps.
£21.84
Cambridge University Press Ceramic Analysis
£18.00
Cambridge University Press Introduction to Probability and Statistics for
Book SynopsisThis textbook is designed for students in statistics, data science, biostatistics, engineering, and physical science programs who need a solid course in the fundamental concepts, methods and theory of statistics to understand, use, and build on modern statistical techniques for complex problems. Examples and exercises incorporate data and R code.
£64.59
Taylor & Francis Ltd Foundations of Data Science with Python
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
£68.39
SAGE Publications Inc Longitudinal Network Models
Book SynopsisAlthough longitudinal social network data are increasingly collected, there are few guides on how to navigate the range of available tools for longitudinal network analysis. The applied social scientist is left to wonder: Which model is most appropriate for my data? How should I get started with this modeling strategy? And how do I know if my model is any good? This book answers these questions. Author Scott Duxbury assumes that the reader is familiar with network measurement, description, and notation, and is versed in regression analysis, but is likely unfamiliar with statistical network methods. The goal of the book is to guide readers towards choosing, applying, assessing, and interpreting a longitudinal network model, and each chapter is organized with a specific data structure or research question in mind. A companion website includes data and R code to replicate the examples in the book.Trade ReviewA brilliant ′how to′ for modelling dynamic network data. An exquisite balance of model intuition, assumptions and practical advice, accessible to all network / data scientists. -- Alexander John BondThis is a very timely book that provides critical skills for conducting explanatory analysis of longitudinal social network data. Both beginners, and advanced analysts can benefit from reading this book as it provides many real life examples, illustrating computational processes, interpreting results, and even furnishing R codes. For those who aspire to learn advanced topics in analyzing longitudinal social network data, this is a must-have book. -- Song YangThis book presents the state-of-art of longitudinal network analysis. It is comprehensive while staying concise, well structured, and clearly written. Definitely a moneyball in the field! -- Weihua AnTable of ContentsChapter 1. Introduction Chapter 2: Temporal Exponential Random Graph Models Chapter 3: Stochastic Actor-oriented Models Chapter 4: Modeling Relational Event Data Chapter 5: Network Influence Models Chapter 6: Conclusion
£33.24
SAGE Publications Inc An R Companion to Political Analysis
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
£64.60
Cambridge University Press Introduction to Environmental Data Science
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.
£56.99
Cambridge University Press A First Course in Random Matrix Theory
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.
£55.09
Cambridge University Press HighDimensional Statistics
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.
£61.74
Cambridge University Press Data Analysis for Business Economics and Policy
Book SynopsisThis textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.Trade Review'This exciting new text covers everything today's aspiring data scientist needs to know, managing to be comprehensive as well as accessible. Like a good confidence interval, the Gabors have got you almost completely covered!' Joshua Angrist, Massachusetts Institute of Technology, winner of the Nobel Memorial Prize in Economic Sciences'This is an excellent book for students learning the art of modern data analytics. It combines the latest techniques with practical applications, replicating the implementation side of classroom teaching that is typically missing in textbooks. For example, they used the World Management Survey data to generate exercises on firm performance for students to gain experience in handling real data, with all its quirks, problems, and issues. For students looking to learn data analysis from one textbook, this is a great way to proceed.' Nicholas Bloom, Stanford University'I know of few books about data analysis and visualization that are as comprehensive, deep, practical, and current as this one; and I know of almost none that are as fun to read. Gábor Békés and Gábor Kézdi have created a most unusual and most compelling beast: a textbook that teaches you the subject matter well and that, at the same time, you can enjoy reading cover to cover.' Alberto Cairo, University of Miami'A beautiful integration of econometrics and data science that provides a direct path from data collection and exploratory analysis to conventional regression modeling, then on to prediction and causal modeling. Exactly what is needed to equip the next generation of students with the tools and insights from the two fields.' David Card, University of California, Berkeley, winner of the Nobel Memorial Prize in Economic Sciences'This textbook is excellent at dissecting and explaining the underlying process of data analysis. Békés and Kézdi have masterfully woven into their instruction a comprehensive range of case studies. The result is a rigorous textbook grounded in real-world learning, at once accessible and engaging to novice scholars and advanced practitioners alike. I have every confidence it will be valued by future generations.' Kerwin K. Charles, Yale School of Management'This book takes you by the hand in a journey that will bring you to understand the core value of data in the fields of machine learning and economics. The large amount of accessible examples combined with the intuitive explanation of foundational concepts is an ideal mix for anyone who wants to do data analysis. It is highly recommended to anyone interested in the new way in which data will be analyzed in the social sciences in the next years.' Christian Fons-Rosen, Barcelona Graduate School of Economics'This sophisticatedly simple book is ideal for undergraduate- or Master's-level Data Analytics courses with a broad audience. The authors discuss the key aspects of examining data, regression analysis, prediction, Lasso, and random forests, and more, with using elegant prose instead of algebra. Using well-chosen case studies, they illustrate the techniques and discuss all of them patiently and thoroughly.' Carter Hill, Louisiana State University'This is not an econometrics textbook. It is a data analysis textbook. And a highly unusual one - written in plain English, based on simplified notation, and full of case studies. An excellent starting point for future data analysts or anyone interested in finding out what data can tell us.' Beata Javorcik, University of Oxford'A multifaceted book that considers many sides of data analysis, all of them important for the contemporary student and practitioner. It brings together classical statistics, regression, and causal inference, sending the message that awareness of all three aspects is important for success in this field. Many 'best practices' are discussed in accessible language, and illustrated using interesting datasets.' llya Ryzhov, University of Maryland'This is a fantastic book to have. Strong data skills are critical for modern business and economic research, and this text provides a thorough and practical guide to acquiring them. Highly recommended.' John van Reenen, MIT Sloan'Energy and climate change is one of the most important public policy challenges, and high- quality data and its empirical analysis is a foundation of solid policy. Data Analysis for Business, Economics, and Policy will make an important contribution to this with its innovative approach. In addition to the comprehensive treatment of modern econometric techniques, the book also covers the less glamorous but crucial aspects of procuring and cleaning data, and drawing useful inferences from less-than-perfect datasets. As the center of gravity of the energy system shifts to developing economies where data quality is still an issue, this will provide an important and practical combination for both academic and policy professionals.' Laszlo Varro, Chief Economist, International Energy AgencyTable of ContentsPart I. Data Exploration: 1. Origins of data; 2. Preparing data for analysis; 3. Exploratory data analysis; 4. Comparison and correlation; 5. Generalizing from data; 6. Testing hypotheses; Part II. Regression Analysis: 7. Simple regression; 8. Complicated patterns and messy data; 9. Generalizing results of a regression; 10. Multiple linear regression; 11. Modeling probabilities; 12. Regression with time series data; Part III. Prediction: 13. A framework for prediction; 14. Model building for prediction; 15. Regression trees; 16. Random forest and boosting; 17. Probability prediction and classification; 18. Forecasting from time series data; Part IV. Causal Analysis: 19. A framework for causal analysis; 20. Designing and analyzing experiments; 21. Regression and matching with observational data; 22. Difference-in-differences; 23. Methods for panel data; 24. Appropriate control groups for panel data; Bibliography; Index.
£47.49
