Data science and analysis Books

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Book SynopsisDrawing on the author's extensive experience of supporting students undertaking projects, Scientific Data Analysis is a guide for any science undergraduate or beginning graduate who needs to analyse their own data, and wants a clear, step-by-step description of how to carry out their analysis in a robust, error-free way.Trade ReviewThis is an appealing introduction that would be accessible to a variety of students at the college level. Its strengths are clarity and directness with an abundance of good examples and case studies. * MAA Review *Table of ContentsPART I - UNDERSTANDING THE STATISTICS; PART II - ANALYSING EXPERIMENTAL DATA

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Book SynopsisThis second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.Trade ReviewThe book is readable, well-written, and amply illustrated * Technometrics, August 1995 (previous edition) *It belongs in the possession of every statistician who encouters longitudinal data. * Journal of the American Statistical Association *. . . provides an excellent bridge between novel concepts in theoretical statistics and their potential use in applied research. * Statistics in Medicine *The topics covered are too numerous to dwell on here ... If your work involves longitudinal data and you wish to update, this book will serve you very well. As a quick look-up, it is very useful. * Pharmaceutical Statistics *The authors conclude each chapter with a helpful summary or conclusion, often indicating further reading. Helpfully, they also mention the topics that they have chosen not to present, together with other recommended books for you to follow up ... They have also chosen a good selection of examples, many of them medical, with which the various methods are clearly illustrated. * Pharmaceutical Statistics *Readers with interests across a wide spectrum of application areas will find the ideas relevant and interesting ... The book is readable and well written ... It belongs to the possession of every statistician who encounters longitudinal data. * Zentralblatt MATH *Table of Contents1. Introduction ; 2. Design considerations ; 3. Exploring longitudinal data ; 4. General linear models ; 5. Parametric models for covariance structure ; 6. Analysis of variance methods ; 7. Generalized linear models for longitudinal data ; 8. Marginal models ; 9. Random effects models ; 10. Transition models ; 11. Likelihood-based methods for categorical data ; 12. Time-dependent covariates ; 13. Missing values in longitudinal data ; 14. Additional topics ; Appendix ; Bibliography ; Index

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Book SynopsisQuestioning Numbers: How to Read and Critique Research is a critical companion for students in research methods courses in any of the social sciences. This book helps teach students how to read and critique research that employs numbers in the course of empirical argument. Author Karin Gwinn Wilkins provides a list of guidelines for reading research and also presents a critical approach to judging and using numbers in navigating and changing social worlds. Illuminating the agendas and politics that can inform how research is conducted and interpreted, this text shows readers how to read and critique research contexts, research design, sampling strategies, definitions, research implementation, data analysis, and interpretation. It also provides strong pedagogical support, including key terms, review exercises, and end-of-chapter reflection questions.A flexible supplement to more comprehensive research texts, Questioning Numbers helps students to become more critical consumers and producTrade Review"A handy-dandy methods supplement that really does a great job of helping students understand why scholars make the choices they do in research design and implementation."--David M. Rhea, Governors State University "This book offers students a different lens to think about all scholarship, including questions about research design and power, who funds scholarship, who is privileged (and who is left out) of research, and who gets to approve and to deny the practice of scholarship. The book gives students the language necessary to ask fundamental questions about scholarly methods that are often assumed to be true or that are typically unquestioned. Students, like Americans in general, have grown up with the notion that science is objective and provides a superior form of understanding the world, when in fact the scientific method does create an argument, often around numbers, that should be examined with a critical eye."--Brant Short, Northern Arizona University "The book does a good job of showing students how to read and critique research that uses numbers. After reading this book, students will understand how research context, selection, definition, implementation, analysis, and interpretation can impact data. And understanding this context is very useful to students."--Gonzalo R. Soruco, University of Miami "A wonderful resource that will encourage students to critically examine the impact of numbers in their lives. Karin Gwinn Wilkins's examination of the social, political, and psychological ramifications of empirical conclusions will ensure that students never take research for granted again."--Andrea Lambert, Northern Kentucky UniversityTable of ContentsUNDERSTANDING NUMBERS; DEDUCTIVE AND INDUCTIVE PROCESSES; INTRINSIC VALUE; THE POLITICAL CONTEXT OF RESEARCH; RESEARCH ILLUSTRATIONS; NUMERICAL LITERACY; KEY TERMS; REFLECTION QUESTIONS; EXERCISE; WHO PRODUCED THE RESEARCH?; RESEARCHERS' HOME INSTITUTIONS; FUNDING INSTITUTIONS; PERMISSION GRANTING INSTITUTIONS; WHAT IS THE PURPOSE OF THE RESEARCH?; WHAT ARE THE RESEARCH QUESTIONS?; HOW IS THE RESEARCH JUSTIFIED?; WHY THE RESEARCH CONTEXT MATTERS; KEY TERMS; REVIEW QUESTIONS; REFLECTION QUESTIONS; EXERCISE; WHAT IS THE RESEARCH DESIGN?; WHAT ARE THE INTERNAL VALIDITY CONCERNS?; EXPERIMENTAL DESIGN; QUASI-EXPERIMENTAL CROSS-SECTIONAL DESIGN; QUASI-EXPERIMENTAL LONGITUDINAL DESIGN; CASE STUDY DESIGN; QUESTIONING RESEARCH DESIGNS; WHY RESEARCH DESIGN MATTERS; KEY TERMS; REVIEW QUESTIONS; REFLECTION QUESTIONS; EXERCISE; WHO OR WHAT IS THE SUBJECT OF THE RESEARCH?; VULNERABLE POPULATIONS; HOW ARE SUBJECTS SELECTED?; PROBABILITY SAMPLING; NON-PROBABILITY SAMPLING; WHAT ARE THE LIMITATIONS OF THE SAMPLE?; WHY SELECTION STRATEGIES MATTER; KEY TERMS; REVIEW QUESTIONS; REFLECTION QUESTIONS; EXERCISE; HOW ARE KEY CONCEPTS DEFINED?; HOW ARE VARIABLES OPERATIONALIZED FROM CONCEPTS?; WHAT DO WE KNOW ABOUT KEY VARIABLES?; MEASUREMENT; ASSESSMENT; WHY DEFINITIONS MATTER; KEY TERMS; REVIEW QUESTIONS; REFLECTION QUESTIONS; EXERCISE; HOW ARE DATA GATHERED?; HOW DO RESEARCHERS GAIN ACCESS TO DATA?; DECEPTION; VOLUNTARY PARTICIPATION; INFORMED CONSENT; PUBLIC SPACE; HOW DO RESEARCHERS DOCUMENT THEIR OBSERVATIONS?; RECORDING OBSERVATIONS; CONFIDENTIALITY AND ANONYMITY; WHY RESEARCH IMPLEMENTATION MATTERS; KEY TERMS; REVIEW QUESTIONS; REFLECTION QUESTIONS; EXERCISE; WHAT DO WE KNOW ABOUT PATTERNS ACROSS VARIABLES?; SIGNIFICANCE; STRENGTH; CAUSALITY; WHAT DO WE STILL NEED TO KNOW?; WHY ANALYSIS AND INTERPRETATION MATTER; KEY TERMS; REVIEW QUESTIONS; REFLECTION QUESTIONS; EXERCISE; GUIDELINE OF QUESTIONS; QUESTIONING THE RESEARCH CONTEXT; QUESTIONING THE RESEARCH DESIGN; QUESTIONING THE SELECTION STRATEGY; QUESTIONING KEY TERMS; QUESTIONING THE RESEARCH IMPLEMENTATION; QUESTIONING ANALYSIS & INTERPRETATION; WHY THE POLITICAL CONTEXT MATTERS

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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.

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Book SynopsisJennifer Edmond is Associate Professor of Trinity College Dublin and the co-director of the Trinity Center for Digital Humanities, Ireland. Jennifer also serves as President of the Board of Directors of the pan-European research infrastructure for the arts and humanities, DARIAH-EU and represents this body on the Open Science Policy Platform (OSPP), which supports the European Commission in developing and promoting Open Science policies. Nicola Horsley's qualitative research critiques the marginalisation of the social in various discourses and explores the dominance of scientific and technical knowledge as bases for policy and practice. Her co-authored book, Challenging the Politics of Early Intervention explores the scientific evidence base for early intervention policies.Jörg Lehmann is a post-doctoral research fellow at the Romanistic Seminar at Eberhard Karls University Tübingen, Germany. He has published two monographs on war literaTrade ReviewBy examining the much-hyped phenomenon of ‘big data’ through a humanist lens, the authors provide a rich account of the possibilities and limits. They focus on the importance of culture and context for understanding how data are imagined, collected, analysed and understood. * Sally Wyatt, Professor of Digital Cultures, Maastricht University, the Netherlands *Table of ContentsIntroduction Chapter 1: Data and Language Chapter 2: Data and Sensemaking Chapter 3: Data and Invisibility Chapter 4: Big Data and the Abyss of Aggregation Chapter 5: Data and Power Conclusion

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Book SynopsisWhat is Big Data, and why should you care?Big data knows where you''ve been and who your friends are. It knows what you like and what makes you angry. It can predict what you''ll buy, where you''ll be the victim of crime and when you''ll have a heart attack. Big data knows you better than you know yourself, or so it claims.But how well do you know big data?You''ve probably seen the phrase in newspaper headlines, at work in a marketing meeting, or on a fitness-tracking gadget. But can you understand it without being a Silicon Valley nerd who writes computer programs for fun?Yes. Yes, you can.Timandra Harkness writes comedy, not computer code. The only programmes she makes are on the radio. If you can read a newspaper you can read this book.Starting with the basics what IS data? And what makes it big? Timandra takes you on a whirlwind tour of how people are using big data today: from science to smart cities, business to politics, self-quantification to the Internet of ThiTrade ReviewA superb explanation of how we got to today. * Evening Standard *Harkness has the perfect combination of wit, charm and statistical insight to crunch big data. It's the book about stats, life and big data we've all been waiting for. -- Matt Parker, Stand-up MathematicianHarkness raises some very big questions indeed, not just about the grandiose claims of the big data evangelists, but also about how in the age of universal surveillance we can defend the concept of privacy. * The Herald *A wonderful collection of fascinating data stories, all told in Timandra's smart and chatty style. But this book also asks the important questions. If big data brings new opportunities, just what are the implications? -- Hannah Fry, author and mathematicianA brilliant guide to our brave new world. -- Brian CleggThis book is a great read – full of interesting stories and fun interviews. But it is not just another credulous tale of technological wonders – Harkness is suitably sceptical of the hype about data analytics, and serious about the challenges is brings. -- David Spiegelhalter, author and mathematicianTable of ContentsIntroduction: What is it? Where did it come from? 1: What Is Data? And what makes it big? 2: Death and Taxes. And Babies. 3: Thinking Machines What Has Big Data Done For Us? 4: Big Business 5: Big Science 6: Big Society 7: Data Driven Democracy Big Ideas? 8: Big Brother 9: Who Do We Think You Are? 10: Are You A Data Point Or A Human Being? Appendix - things you can do to keep your data private Acknowledgements

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Book SynopsisMona Khalil is a Data Science Manager at Greenhouse Software. Mona holds a degree in psychology from Fordham University and statistics at Baruch College, as well as having a decade of experience working with analytics and data science. Mona has worked with cross-functional teams in a variety of industries, including government, education, and HR technology.

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Book SynopsisAs an emerging discipline, data science broadly means different things across different areas. Exploring the relationship of data science with statistics, a well-established and principled data-analytic discipline, this book provides insights about commonalities in approach, and differences in emphasis.Featuring chapters from established authors in both disciplines, the book also presents a number of applications and accompanying papers.

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Book SynopsisThe connected world offers the potential for radical new business insights gleaned from previously unimaginable volumes of data. But business has got bogged down in the process of collecting and storing that data; money has been wasted on data lakes in which many IT departments have drowned without being able to deliver useful insights to business leaders. Big data has new and exciting answers to offer, but business leaders must first decide what questions it would like to see answered. Data may be the new oil, but to date we have only built oil depots. This book analyses the new, Fourth Wave of business transformation, which will build the refineries that turn data into useful products. Business has started from 'data up' and needs to start again from 'value down', going back to the drivers of real business value and deciding what insights would help realize that value. Only then can we begin to interrogate data with purpose.

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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.

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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.

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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.

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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.

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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.

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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

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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

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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

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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

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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

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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;

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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.

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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

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Book SynopsisA clear easy-to-read guide to presenting your message using statistical data Poor presentation of data is everywhere; basic principles are forgotten or ignored. As a result, audiences are presented with confusing tables and charts that do not make immediate sense. This book is intended to be read by all who present data in any form. The author, a chartered statistician who has run many courses on the subject of data presentation, presents numerous examples alongside an explanation of how improvements can be made and basic principles to adopt. He advocates following four key C' words in all messages: Clear, Concise, Correct and Consistent. Following the principles in the book will lead to clearer, simpler and easier to understand messages which can then be assimilated faster. Anyone from student to researcher, journalist to policy adviser, charity worker to government statistician, will benefit from reading this book. More importantly, it will also benefit the reTrade Review�Highly recommended to a very wide audience.� (Mathematical Association of America, 1 January 2015) �Presenting data is, of course, more than a good table or an informative graphic. The words that describe the data must also be correct, clear, concise and consistent, and the author devotes a chapter to ensuring that the expert�s hard work is not undone by commentary that is too technical or too laboured to be understood by non-expert audiences.� (The Royal Statistical Society, 1 February 2015) �This book must be mandatory to any data analyst and statistician, and indeed anyone who presents numeric data. The word "numeric" is important, as non-numeric data are not discussed in the book.� (ENBIS News, 10 December 2014) �The words that describe the data, and the messages it may have, must also be correct, clear, concise and consistent, and the author devotes a chapter to ensuring that the expert�s hard work is not undone by commentary that is too technical or too laboured to be understood by non-expert audiences.� (Significance, 1 February 2014) "The book is easy to read and can be consumed in a single train journey. It is also easy to look at and the illustrations and graphics occupy as much space as the text. The author has indeed put his money where his mouth is and the graphics both illustrate his points and makes them alone. The highlight for me was in the chapter on tables where the author reduces a table containing seven columns and five rows to a two by two table which beautifully makes the point when previously it had been lost in a forest of figures... This is an excellent book and I would recommend it as essential reading to anyone who is writing a thesis or dissertation, making a presentation or writing a paper. That�s all of us." (Occupational Medicine, 15th February 2016)Table of ContentsList of Tables vii List of Figures ix Introduction xiii Preface xvii Acknowledgements xix 1 Understanding number 1 1.1 Thousands separator 2 1.2 Decimal separator 3 1.3 Level of detail in comparisons 4 1.4 Justification of data 5 1.5 Basic rounding 7 1.6 Effective rounding 9 Notes 16 2 Tables 17 2.1 Position of totals in tables 17 2.2 What is a table? 19 2.3 Reference tables 19 2.4 Summary tables 22 2.5 How tables are read 24 2.6 Layout of data in tables 25 2.7 Capital letters for table titles and headings in tables 29 2.8 Use of bold typeface 30 2.9 Use of gridlines and other lines in tables 30 Notes 31 3 Charts (bar charts, histograms, pie charts, graphs) 33 3.1 How the user interprets charts 33 3.2 Written aims for charts 35 3.3 Scale definition and display 37 3.4 Difference between bar charts and histograms 49 3.5 Pie chart principles 51 3.6 Issues with pie charts 55 3.7 Graph principles 63 3.8 Issues with graphs 64 3.9 Pictogram principles 79 3.10 Comparative charts: Multiple pies, multiple bar charts, double scale graphs 82 3.11 Graphics 88 3.12 Three-dimensional charts 90 Notes 92 4 Numbers in text 93 4.1 Numbers written as text 94 4.1.1 Correct numbers 94 4.1.2 Clear numbers 94 4.1.3 Concise numbers 95 4.1.4 Consistent numbers 96 4.2 Ordering of data 97 4.3 Technical terms 98 4.4 Plain language 100 4.5 Emotive language 102 4.6 Key messages 103 Notes 105 5 Data presentation on the Internet 107 5.1 The early years 110 5.2 Statistics on CD-ROMs 113 5.3 Data on the Internet 116 5.4 Charts on the Internet 120 5.5 Text on the Internet 128 Notes 130

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Book SynopsisLearn how to easily transform your data into engaging, interactive visual reports! Data is no longer the sole domain of tech professionals and scientists. Whether in our personal, business, or community lives, data is rapidly increasing in both importance and sheer volume. The ability to visualize all kinds of data is now within reach for anyone with a computer and an internet connection. Google Data Studio, quickly becoming the most popular free tool in data visualization, offers users a flexible, powerful way to transform private and public data into interactive knowledge that can be easily shared and understood. Hands On With Google Data Studio teaches you how to visualize your data today and produce professional quality results quickly and easily. No previous experience is required to get started right awayall you need is this guide, a Gmail account, and a little curiosity to access and visualize data just like large businesses and organizationTable of ContentsIntroduction xv Part I Data Studio Basics 1 Chapter 1 Data Studio and the Data Citizen 3 Data, Data Everywhere 4 Accessibility of Data 4 Deriving Value from Data 5 The Role of Data Studio 7 A Brief History of Data Studio 9 Fundamentals First 12 Where to Go for Help 12 Time to Get Started! 13 Chapter 2 Cooking with Google Data Studio 15 Our First Example 15 Step 1. Select a Dish to Prepare: Visualizing a Bank Account 16 Before We Start: Things You’ll Need 19 Step 2. Let’s Go Shopping! Getting the Data 19 Step 3. Unpack the Groceries: Setting Up Google Drive 21 Step 4. Preparing the Ingredients: Working with Google Sheets 22 Step 5. Familiarizing Yourself with the Kitchen: Data Studio Home Screen and Starting a New Report 25 Step 6. Assemble the Ingredients: Connecting the Data to the Report 28 Step 7. Set the Table: Adding Your First Chart 35 Step 8. Keep Score with Scorecards 40 Step 9. It’s About Time: Building the Time-Series Chart 45 Step 10. Serving Suggestions: Finishing Touches and Sharing Your Report 50 Summary 55 Chapter 3 Enhancing Basic Graphs 57 Static Report Design 57 Before Getting Started: Things You’ll Need 59 Step 1. Copy the Report 60 Step 2. Modify the Header and Set the Date Range 61 Step 3. Modify Scorecards 63 Step 4. Modify Time-Series Chart 65 Step 5. Modify the Transactions Table 66 Step 6. Save the Report as PDF for Sharing 69 Summary 70 Chapter 4 Data Exploration with Interactive Elements 73 Building Our Workbench Example 73 Step 1. Selecting a Dish to Prepare: Exploration Workbench for a Bank Account 74 Step 2. Let’s Go Shopping! Getting the Data Set 77 Step 3. Assembling the Ingredients: Connecting Data to the Report 78 Adding Chart Components and Graphic Elements 82 Step 4. Reuse, Recycle, and Repurpose: Copying Elements from Other Reports 82 Step 5. Easy as Pie! Adding a Pie Chart 84 Step 6. Step Up to the Bar: Adding the Bar Chart 85 Step 7. Lining Things Up: Adding the Line Chart 90 Step 8. Tables, Again!? Setting Up the Table 91 Adding and Configuring Filter Controls 92 Step 9. Fun with Filters: Setting Up the Standard Filter Controls 92 Tips for Using Interactive Filters 94 Find the Answers: Filter Challenge 95 Summary 97 Part II Business and Marketing Applications 99 Chapter 5 Web Data Visualization with Google Analytics 101 Google Services for Websites and Business 102 A Brief Primer for Google Analytics 104 Using a Template to Create a New Report 106 Building the Google Analytics Report 110 Modifying the Charts 112 Working with the New Report 117 A Real-World Example: The Effects of a Beach Party in Spain on Web Traffic 117 The Limits of Demographic Data from Google Analytics 124 Now for a Side Dish: Adding a Page and Using Filters 126 Summary 132 Chapter 6 Using Google Search Console for Audience Insights 135 Search Console and Search Results Pages 136 Creating a Search Console Report with Data Studio 141 Step 1. Selecting the Dish to Prepare 142 Step 2. Let’s Go Shopping! 144 Step 3. Assemble the Ingredients 147 Step 4. Setting the Table 153 Bon Appetite! Using the Search Console Report 157 An Example for Your Soul! 161 Summary 170 Chapter 7 Viewing Local Organization Data from Google My Business 173 Google Search and the Local Organization 173 GMB: The New Home Page in the Search Results 175 What the User Sees in a GMB Listing 176 What the Owner Sees in a GMB Listing 180 Why Use Data Studio for GMB? 185 Step 1. Selecting the Dish to Prepare 187 The Actions Page 189 The Reviews Page 190 The Search Page 192 Before You Begin: What You Need to Get Started 194 Step 2. Let’s Go Shopping and Assemble the Ingredients 195 Step 3. Setting the Table 202 Report, Page, and Header Setup 203 Page Navigation Button Setup 206 Building the Actions Page 209 Building the Search Page 213 Building the Reviews Page 215 Summary 218 Part III Beyond the Office 221 Chapter 8 Getting Personal 223 Creating Your Own Data 223 Using Google Forms to Collect Your Own Data 225 Curating Data from Other Sources 248 Setting Up IFTTT to Capture Tweets 250 Preparing the Google Sheet for Data Studio 256 Create a Data Source for the Google Sheet 256 Create the New Twitter Report 258 Working with Data Generated for You 262 Music Service Tracking Example 262 Google Fit Community Connector Example 269 Community Connector Setup for Google Fit 270 Bringing It All Together 280 Created Data 280 Curated Data 283 Generated Data 284 Summary 288 Chapter 9 Going Public 291 Shared Data Sets 291 Searching for Data Sets 292 Getting Data from Kaggle 294 Using a Kaggle Account 294 Building the UFO Sightings Workbench 302 Dealing with Performance Issues in Reports 307 Using the Extract Data Connector to Speed Up Reports 307 data.world and Makeover Monday 314 Bringing It Home: Real Estate in Your Neighborhood 330 Selecting the Dish to Prepare 331 Going Shopping: Hunting for Housing Data 332 Unpacking the Groceries: Moving the File to Google Drive 337 Preparing the Ingredients: Fixing Data Formatting Issues 338 Assembling the Ingredients: Connecting the Data to the Report 340 Preparing the Meal: The Plan Meets Reality 340 Sharing Your Dish: Embedding Reports 346 Embedding a Report in a Google Site 351 Embedding a Report in a WordPress Site 353 Dealing with Pivoted Data 356 Summary 361 Chapter 10 Where Do You Go from Here? 363 Helping Your Audience See the Light 363 Annotation Ideas for Static Reports 364 Annotation Ideas for Interactive Reports 367 Providing Detailed Instructions for Users 371 Community Visualizations 375 Data Studio as an Application Development Platform 377 Exotic Ingredients and Your Pantry 383 Data Blending 383 File Upload 388 BigQuery for Big Data 390 Community Connectors 395 The Latest Dishes: New Developments and Releases 396 Summary 396 Index 399

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Book SynopsisOrganize, plan, and build an exceptional data analytics team within your organization In Minding the Machines: Building and Leading Data Science and Analytics Teams, AI and analytics strategy expert Jeremy Adamson delivers an accessible and insightful roadmap to structuring and leading a successful analytics team. The book explores the tasks, strategies, methods, and frameworks necessary for an organization beginning their first foray into the analytics space or one that is rebooting its team for the umpteenth time in search of success. In this book, you'll discover: A focus on the three pillars of strategy, process, and people and their role in the iterative and ongoing effort of building an analytics teamRepeated emphasis on three guiding principles followed by successful analytics teams: start early, go slow, and fully commitThe importance of creating clear goals and objectives when creating a new analytics unit in an organization Perfect for executives, managers, team leads, and other business leaders tasked with structuring and leading a successful analytics team, Minding the Machines is also an indispensable resource for data scientists and analysts who seek to better understand how their individual efforts fit into their team's overall results.Table of ContentsForeword xiii Introduction xvi Chapter 1 Prologue 1 For the Leader from the Business 5 For the Career Transitioner 6 For the Motivated Practitioner 6 For the Student 7 For the Analytics Leader 8 Structure of This Book 8 Why is This Book Needed? 9 Communication Gap 9 Troubles with Taylorism 10 Rinse, Report, Repeat 12 Too Fast, Too Slow 13 More Data, More Problems 14 Summary 15 Chapter 2 Strategy 17 The Role of Analytics in the Organization 20 The Analytics Playbook 20 Data and Analytics as a Culture Change 24 Current State Assessment 26 Readiness Assessment 26 Capability Modeling and Mapping 28 Technology Stack Review 32 Data Quality and Governance 34 Stakeholder Engagement 35 Defining the Future State 37 Defining the Mandate 39 Analytics Governance Model 40 Target Operating Model 42 Define Your Principles 43 Functions, Services, and Capabilities 43 Interaction Models 44 Organizational Design 48 Community of Practice 52 Project Delivery Model 55 Closing the Gap 57 Setting the Horizon 58 Establishing a Talent Roadmap 59 Consultants and Contractors 60 Change Management 62 Implementing Governance Models 64 Summary 65 Chapter 3 Process 69 Project Planning 73 Intake and Prioritization 73 Project Pipelines 77 Portfolio Project Management 80 Project Scoping and Planning 83 Scoping and Requirements Definition 86 Planning 92 Project Execution 96 Governance Structure and Communication Plan 99 Project Kickoff 102 Agile Analytics 103 Change and Stakeholder Management 106 Skeuomorphs 106 AI 101 and Project Brainstorming 107 Iterative Insights 110 Closeout and Delivery 111 Automation 112 Project Debrief 114 Summary 118 Chapter 4 People 121 Building the Team 122 Success Factors 123 Team Composition 128 Hiring and Onboarding 129 Talent Development 131 Retention 136 Departures 137 The Data Scientist Hierarchy of Needs 139 Culture 140 Innovation 145 Communication 147 Succession Planning 149 Potential Pitfalls 151 Dunning-Kruger Effect 152 Diderot Effect 153 Leading the Team 154 Data Scientists as Craftspeople 157 Team Conventions 160 Formal Meetings 162 Coffee Chats 164 Managing Conflict 167 Relationship Management 169 Owning the Narrative 175 Performance Metrics 177 Summary 181 Chapter 5 Future of Business Analytics 187 AutoML and the No‐Code Movement 189 Data Science is Dead 192 The Data Warehouse 195 True Operationalization 196 Exogenous Data 198 Edge AI 199 Analytics for Good 200 Analytics for Evil 201 Ethics and Bias 203 Analytics Talent Shortages 204 Death of the Career Transitioner 206 Chapter 6 Summary 211 Chapter 7 Coda 213 Index 215

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