Data science and analysis Books
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
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 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
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£32.30
Princeton University Press Modeling Social Behavior
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£45.00
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
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.
£28.49
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
£60.80
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
Cambridge University Press Comparing Cultures
Book SynopsisA new and important contribution to the re-emergent field of comparative anthropology, this book argues that comparative ethnographic methods are essential for more contextually sophisticated accounts of a number of pressing human concerns today. The book includes expert accounts from an international team of scholars, showing how these methods can be used to illuminate important theoretical and practical projects. Illustrated with examples of successful inter-disciplinary projects, it highlights the challenges, benefits, and innovative strategies involved in working collaboratively across disciplines. Through its focus on practical methodological and logistical accounts, it will be of value to both seasoned researchers who seek practical models for conducting their own cutting-edge comparative research, and to teachers and students who are looking for first-person accounts of comparative ethnographic research.Trade Review'Comparison is almost as fundamental to the human mind as air and water is to the body. It is therefore puzzling and paradoxical that anthropology, which was founded as an explicitly comparative discipline, has often been ambivalent, reluctant and even hostile to comparative research. This extremely timely book reinstates comparison as a key element in anthropological theory and methodology, demonstrating a variety in comparative strategies which reflect the diversity of anthropology itself and, indeed, the human world. Highly recommended.' Thomas Hylland Eriksen, University of Oslo'This book engages with a welcome and timely project: restoring comparative perspectives to anthropology. By exploring the challenges, dimensions, and complexities of comparative methodologies, it illustrates how critical comparisons can inform theory and illuminate underlying political economic and institutional processes.' Nina Glick Schiller, Max Planck Institute for Social AnthropologyTable of ContentsIntroduction. Comparative ethnography: its promise, process, and successful implementations Edward D. Lowe and Michael Schnegg; Part I. Binary Comparisons: 1. Thinking with comparison in the anthropology/historical anthropology of migration Caroline B. Brettell; 2. Comparing tangerines: Dorothy Lee and the search for an authentic individualism Richard Handler; 3. A comparative ethnographic study of suicide epidemics in two Pacific Island societies Edward D. Lowe; Part II. Regional Comparisons: 4. The comparison of structures and the comparison of systems: Lévi-Strauss, Dumont, Luhmann Guido Sprenger; 5. Regional comparison in historical anthropology: three case examples from South Arabia Andre Gingrich; 6. Scaling ethnography up Michael Schnegg; Part III. Distant and Fluid Comparisons: 7. Best, worst, and good enough: lessons learned from multi-sited comparative ethnography Jennifer S. Hirsch, Holly Wardlow, Daniel Jordan Smith, Harriet Phinney, Shanti Parikh and Constance A. Nathanson; 8. Research across cultures and disciplines: methodological challenges in an interdisciplinary and comparative research project on emotion socialization Birgitt Röttger-Rössler; 9. Global sport industries, comparison, and economics of scales Niko Besnier and Daniel Guinness.
£21.84
Cambridge University Press Polls Pollsters and Public Opinion
Book SynopsisThis is a book on public opinion told through the eyes of the pollster. It looks at public opinion as a concept and in historical context then turns to the use of public opinion practically as an analytic input; particularly, how to use it to forecast, convince, and assess.
£24.69
Cambridge University Press Time Series for Data Scientists
Book SynopsisLearn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book''s companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplinesTrade Review'This book provides an excellent introduction to time series modelling and forecasting which are increasingly important tools in the domain of official statistics. The clear descriptions and real-life examples provided in this text make it easy to digest for those not already familiar with the topic. In addition, the exercises allow readers to develop their understanding in more depth through hands-on applications of the methods to real data using open-source tools. The inclusion of modern topics such as machine learning and artificial intelligence are a valuable addition to make the text relevant and comprehensive.' Steve Matthews, Statistics Canada'This book is a great introduction to the ideas and methods of time series data analysis. Chapter by chapter, it will show you its most valuable features, like the wealth of real examples as well as practical uses of R and graphical visualization. You will certainly enjoy this text, as it is suitable for a wide range of statistical courses.' Vera Ioudina, Texas State University'Lots of good real world examples together with the use of R helps a lot as do the nice set of exercises. In time series, it is a tricky balance between overdoing theory or just hand waving and here the author does very well. This would make a lovely course text!' Gareth Janacek, University of East Anglia'Time Series for Data Scientists' develops your intuition before walking through classical and modern time series methods in easy-to-understand terms. With each algorithm Dr. Sanchez first helps you understand the motivation behind the approach; then walks you through the formulas step-by-step, outlining what we're doing and why; she also includes R code to help you apply the techniques learned to solve real-world business problems using real-world data sets; and takes the time to show you how to interpret the output, and discuss what to try next when an initial approach doesn't quite match the trends in the data. Whether you're an undergraduate or graduate student, are curious about time series methods, are looking for a self-paced book, or a reference guide, this is a must-have.' Irina Kukuyeva, Fractional Chief Data Officer'A fine textbook for an introductory time series course aimed at undergraduates in Statistics or Data Science. The author did an excellent work in the choice of topics, covering from classical exploratory techniques to modern machine learning approaches, while keeping the level of the exposition accessible to readers with a modicum of mathematical background. To be recommended!' Giovanni Petris, University of Arkansas'This book should be a serious contender if you are looking for an introductory text for an undergraduate course in time series. It is especially suited for a course populated with students having varying degrees of mathematical skill levels. Its conversational approach to introducing time series concepts and the use of insightful examples throughout the book makes it very accessible to students who are not highly trained in abstract mathematical reasoning. Nevertheless, it does not shy away from providing the theoretical underpinnings of various time series models but does so in a manner very accessible to students. The availability of R code throughout the book is an added plus. Even if I am teaching an upper-level graduate course in time series, I would use this book as a supplement simply because of the plethora of examples and data sources it provides.' V. A. Samaranayake, Missouri University of Science and TechnologyTable of ContentsPart I. Descriptive Features of Time Series Data: 1. Introduction to time series data; 2. Smoothing and decomposing a time series; 3. Summary statistics of stationary time series; Part II. Univariate Models of Temporal Dependence: 4. The algebra of differencing and backshifting; 5. Stationary stochastic processes; 6. ARIMA(p,d,q)(P,D,Q)$_F$ modeling and forecasting; Part III. Multivariate Modeling and Forecasting: 7. Latent process models for time series; 8. Vector autoregression; 9. Classical regression with ARMA residuals; 10. Machine learning methods for time series; References; Index.
£56.99
Cambridge University Press Dicing with Death
Book SynopsisAs a result of the COVID-19 pandemic, medical statistics and public health data have become staples of newsfeeds worldwide, with infection rates, deaths, case fatality and the mysterious R figure featuring regularly. However, we don''t all have the statistical background needed to translate this information into knowledge. In this lively account, Stephen Senn explains these statistical phenomena and demonstrates how statistics is essential to making rational decisions about medical care. The second edition has been thoroughly updated to cover developments of the last two decades and includes a new chapter on medical statistical challenges of COVID-19, along with additional material on infectious disease modelling and representation of women in clinical trials. Senn entertains with anecdotes, puzzles and paradoxes, while tackling big themes including: clinical trials and the development of medicines, life tables, vaccines and their risks or lack of them, smoking and lung cancer, and even the power of prayer.Trade Review'The COVID pandemic has shown the power of statistics to save millions of lives by revealing 'what works'. Yet statistical methods have a deeply controversial history, and provoke sometimes bitter debate to this day. Professor Stephen Senn is renowned for his brilliant insights on the subject, and in Dicing with Death he offers us a series of fascinating journeys through its vast and varied landscape.' Robert Matthews, Visiting Professor Aston University and author of Chancing It: The Laws of Chance and How They Can Work for YouTable of Contents1. Circling the square; 2. The diceman cometh; 3. Trials of life; 4. Of dice and men; 5. Sex and the single patient; 6. A hale view of pills (and other matters); 7. Time's tables; 8. A dip in the pool; 9. The things that bug us; 10. The law is a ass; 11. The empire of the sum; 12. Going viral; Notes; Index.
£18.99
John Wiley & Sons Inc Presenting Data How to Communicate Your Message
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
£20.85
John Wiley & Sons Inc An Introduction to Categorical Data Analysis
Book SynopsisA valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: Illustrations of the use of R software to perform all the analyses in the book A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis New sections in many chapters introducing the Bayesian approach for the methodTable of ContentsPreface ix About the Companion Website xiii 1 Introduction 1 1.1 Categorical Response Data 1 1.2 Probability Distributions for Categorical Data 3 1.3 Statistical Inference for a Proportion 5 1.4 Statistical Inference for Discrete Data 10 1.5 Bayesian Inference for Proportions * 13 1.6 Using R Software for Statistical Inference about Proportions * 17 Exercises 21 2 Analyzing Contingency Tables 25 2.1 Probability Structure for Contingency Tables 26 2.2 Comparing Proportions in 2 × 2 Contingency Tables 29 2.3 The Odds Ratio 31 2.4 Chi-Squared Tests of Independence 36 2.5 Testing Independence for Ordinal Variables 42 2.6 Exact Frequentist and Bayesian Inference * 46 2.7 Association in Three-Way Tables 52 Exercises 56 3 Generalized Linear Models 65 3.1 Components of a Generalized Linear Model 66 3.2 Generalized Linear Models for Binary Data 68 3.3 Generalized Linear Models for Counts and Rates 72 3.4 Statistical Inference and Model Checking 76 3.5 Fitting Generalized Linear Models 82 Exercises 84 4 Logistic Regression 89 4.1 The Logistic Regression Model 89 4.2 Statistical Inference for Logistic Regression 94 4.3 Logistic Regression with Categorical Predictors 98 4.4 Multiple Logistic Regression 102 4.5 Summarizing Effects in Logistic Regression 107 4.6 Summarizing Predictive Power: Classification Tables, ROC Curves, and Multiple Correlation 110 Exercises 113 5 Building and Applying Logistic Regression Models 123 5.1 Strategies in Model Selection 123 5.2 Model Checking 130 5.3 Infinite Estimates in Logistic Regression 136 5.4 Bayesian Inference, Penalized Likelihood, and Conditional Likelihood for Logistic Regression * 140 5.5 Alternative Link Functions: Linear Probability and Probit Models * 145 5.6 Sample Size and Power for Logistic Regression * 150 Exercises 151 6 Multicategory Logit Models 159 6.1 Baseline-Category Logit Models for Nominal Responses 159 6.2 Cumulative Logit Models for Ordinal Responses 167 6.3 Cumulative Link Models: Model Checking and Extensions * 176 6.4 Paired-Category Logit Modeling of Ordinal Responses * 184 Exercises 187 7 Loglinear Models for Contingency Tables and Counts 193 7.1 Loglinear Models for Counts in Contingency Tables 194 7.2 Statistical Inference for Loglinear Models 200 7.3 The Loglinear – Logistic Model Connection 207 7.4 Independence Graphs and Collapsibility 210 7.5 Modeling Ordinal Associations in Contingency Tables 214 7.6 Loglinear Modeling of Count Response Variables * 217 Exercises 221 8 Models for Matched Pairs 227 8.1 Comparing Dependent Proportions for Binary Matched Pairs 228 8.2 Marginal Models and Subject-Specific Models for Matched Pairs 230 8.3 Comparing Proportions for Nominal Matched-Pairs Responses 235 8.4 Comparing Proportions for Ordinal Matched-Pairs Responses 239 8.5 Analyzing Rater Agreement * 243 8.6 Bradley–Terry Model for Paired Preferences * 247 Exercises 249 9 Marginal Modeling of Correlated, Clustered Responses 253 9.1 Marginal Models Versus Subject-Specific Models 254 9.2 Marginal Modeling: The Generalized Estimating Equations (GEE) Approach 255 9.3 Marginal Modeling for Clustered Multinomial Responses 260 9.4 Transitional Modeling, Given the Past 263 9.5 Dealing with Missing Data * 266 Exercises 268 10 Random Effects: Generalized Linear Mixed Models 273 10.1 Random Effects Modeling of Clustered Categorical Data 273 10.2 Examples: Random Effects Models for Binary Data 278 10.3 Extensions to Multinomial Responses and Multiple Random Effect Terms 284 10.4 Multilevel (Hierarchical) Models 288 10.5 Latent Class Models * 291 Exercises 295 11 Classification and Smoothing * 299 11.1 Classification: Linear Discriminant Analysis 300 11.2 Classification: Tree-Based Prediction 302 11.3 Cluster Analysis for Categorical Responses 306 11.4 Smoothing: Generalized Additive Models 310 11.5 Regularization for High-Dimensional Categorical Data (Large p) 313 Exercises 321 12 A Historical Tour of Categorical Data Analysis * 325 Appendix: Software for Categorical Data Analysis 331 A.1 R for Categorical Data Analysis 331 A.2 SAS for Categorical Data Analysis 332 A.3 Stata for Categorical Data Analysis 342 A.4 SPSS for Categorical Data Analysis 346 Brief Solutions to Odd-Numbered Exercises 349 Bibliography 363 Examples Index 365 Subject Index 369
£109.76
John Wiley & Sons Inc Hands On With Google Data Studio
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
£24.79
John Wiley & Sons Inc Minding the Machines
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
£24.79
John Wiley & Sons Inc Data Science Handbook
Book SynopsisTable of ContentsAcknowledgment xi Preface xiii 1 Data Munging Basics 1 Introduction 1 1.1 Filtering and Selecting Data 6 1.2 Treating Missing Values 11 1.3 Removing Duplicates 14 1.4 Concatenating and Transforming Data 16 1.5 Grouping and Data Aggregation 20 References 20 2 Data Visualization 23 2.1 Creating Standard Plots (Line, Bar, Pie) 26 2.2 Defining Elements of a Plot 30 2.3 Plot Formatting 33 2.4 Creating Labels and Annotations 38 2.5 Creating Visualizations from Time Series Data 42 2.6 Constructing Histograms, Box Plots, and Scatter Plots 44 References 54 3 Basic Math and Statistics 57 3.1 Linear Algebra 57 3.2 Calculus 58 3.2.1 Differential Calculus 58 3.2.2 Integral Calculus 58 3.3 Inferential Statistics 60 3.3.1 Central Limit Theorem 60 3.3.2 Hypothesis Testing 60 3.3.3 ANOVA 60 3.3.4 Qualitative Data Analysis 60 3.4 Using NumPy to Perform Arithmetic Operations on Data 61 3.5 Generating Summary Statistics Using Pandas and Scipy 64 3.6 Summarizing Categorical Data Using Pandas 68 3.7 Starting with Parametric Methods in Pandas and Scipy 84 3.8 Delving Into Non-Parametric Methods Using Pandas and Scipy 87 3.9 Transforming Dataset Distributions 91 References 94 4 Introduction to Machine Learning 97 4.1 Introduction to Machine Learning 97 4.2 Types of Machine Learning Algorithms 101 4.3 Explanatory Factor Analysis 114 4.4 Principal Component Analysis (PCA) 115 References 121 5 Outlier Analysis 123 5.1 Extreme Value Analysis Using Univariate Methods 123 5.2 Multivariate Analysis for Outlier Detection 125 5.3 DBSCan Clustering to Identify Outliers 127 References 133 6 Cluster Analysis 135 6.1 K-Means Algorithm 135 6.2 Hierarchial Methods 141 6.3 Instance-Based Learning w/ k-Nearest Neighbor 149 References 156 7 Network Analysis with NetworkX 157 7.1 Working with Graph Objects 159 7.2 Simulating a Social Network (ie; Directed Network Analysis) 163 7.3 Analyzing a Social Network 169 References 171 8 Basic Algorithmic Learning 173 8.1 Linear Regression 173 8.2 Logistic Regression 183 8.3 Naive Bayes Classifiers 189 References 195 9 Web-Based Data Visualizations with Plotly 197 9.1 Collaborative Aanalytics 197 9.2 Basic Charts 208 9.3 Statistical Charts 212 9.4 Plotly Maps 216 References 219 10 Web Scraping with Beautiful Soup 221 10.1 The BeautifulSoup Object 224 10.2 Exploring NavigableString Objects 228 10.3 Data Parsing 230 10.4 Web Scraping 233 10.5 Ensemble Models with Random Forests 235 References 254 Data Science Projects 257 11 Covid19 Detection and Prediction 259 Bibliography 275 12 Leaf Disease Detection 277 Bibliography 283 13 Brain Tumor Detection with Data Science 285 Bibliography 295 14 Color Detection with Python 297 Bibliography 300 15 Detecting Parkinson’s Disease 301 Bibliography 302 16 Sentiment Analysis 303 Bibliography 306 17 Road Lane Line Detection 307 Bibliography 315 18 Fake News Detection 317 Bibliography 318 19 Speech Emotion Recognition 319 Bibliography 322 20 Gender and Age Detection with Data Science 323 Bibliography 339 21 Diabetic Retinopathy 341 Bibliography 350 22 Driver Drowsiness Detection in Python 351 Bibliography 356 23 Chatbot Using Python 357 Bibliography 363 24 Handwritten Digit Recognition Project 365 Bibliography 368 25 Image Caption Generator Project in Python 369 Bibliography 379 26 Credit Card Fraud Detection Project 381 Bibliography 391 27 Movie Recommendation System 393 Bibliography 411 28 Customer Segmentation 413 Bibliography 431 29 Breast Cancer Classification 433 Bibliography 443 30 Traffic Signs Recognition 445 Bibliography 453
£119.70
Taylor & Francis Ltd Critical Theory and Qualitative Data Analysis in
Book SynopsisCritical Theory and Qualitative Data Analysis in Education offers a path-breaking explanation of how critical theories can be used within the analysis of qualitative data to inform research processes, such as data collection, analysis, and interpretation. This contributed volume offers examples of qualitative data analysis techniques and exemplars of empirical studies that employ critical theory concepts in data analysis. By creating a clear and accessible bridge between data analysis and critical social theories, this book helps scholars and researchers effectively translate their research designs and findings to multiple audiences for more equitable outcomes and disruption of historical and contemporary inequality.Trade Review"In addition to offering ways for scholars to build on existing methodologies, contributors to this volume also offer new paradigms while inviting all scholars to consider their tools…. I am encouraged by education researchers desire to transcend contrived borders and employ the methods, forms of methods, analysis, and strategic reconstruction of these forms in order to ask an entirely new set of questions."—from the Foreword by Maisha T. Winn, Chancellor's Leadership Professor, and Co-Director of Transformative Justice in Education (TJE) at the University of California- Davis, USAThis book provides critical scholars interested in paying attention to the larger social structural issues and oppression with tangible "how to’s" for critical qualitative data analysis and, in this way, encourage researchers to work toward social change. Too often data analysis processes are reductive and void of theory and, importantly, the authors fill a gap in the existing literature. This thoughtful book is useful for introductory qualitative researchers and senior scholars alike. — Penny A. Pasque, Professor and Department Head, Educational Leadership, Policy, and Human Development, North Carolina State University, USATable of ContentsContentsForeword: Reflections on Research that is Humanizing Maisha T. WinnPreface Ashley N. Gaskew, Jamile Lee-Johnson, and Rachelle Winkle-WagnerSection I The Need for Partnering Critical Theory and Data Analysis in EducationChapter 1 The Missing Link in Data Analysis: An Introduction to the Use of Critical Theory to Guide Data Analysis Rachelle Winkle-Wagner, Ashley Gaskew, and Jamila Lee-JohnsonChapter 2 The Missing InfinitePhil Frances CarspeckenChapter 3 Trouble the Translating: Border Thinking, Indigenous Knowledges, and Undoing MethodNathaniel BeckChapter 4 De-Norming the Syllabus: An Analysis Situated in Critical and Caring PedagogiesVirginia M. Schwarz Section II Critical Theory and Analysis with Marginalized Populations Section Introduction Keon M. McGuire Chapter 5 Illuminating Systemic Inequality in Educations: Using Bourdieu in Critical Qualitative Data AnalysisTangela Blakely Reavis Chapter 6 "Wanna Be Startnin’ Somethin:" Answering the Methodological "Call" To Position Complex Blackness in Conversation with Hermeneutic PhenomenologySteve D. Mobley, Jr. Chapter 7 Thinking with Queer of Color Critique: A Multidimensional Approach to Analyzing and Interpreting DataReginald A. Blockett Chapter 8 Globalization, Higher Education, and Crisis: A Model for Applying Critical Geography toward Data AnalysisChelsea A. Blackburn CohenChapter 9 Context and Materiality: Inclusive Appropriations of New Materialism for Qualitative AnalysisBarbara Dennis Section III Critical Theories and Data Analysis in Institutions and Policies Section IntroductionChristina W. Yao Chapter 10 A Culture of Values: Rethinking School Quality and Culture through Fanonian Critical TheoryJacqueline M. ForbesChapter 11 Discourse, Representation, and "Othering": Postcolonial Analysis of Donald Trump’s Education ReformMercy AgyepongChapter 12 Analyzing Policy Critically: Using Critical Race Theory to AnalyzeRachelle Winkle-Wagner, Thandi Sulè, and Dina C. Maramba Chapter 13 Habermas and Data Analysis in For-Profit Higher Education InstitutionsAshley GaskewChapter 14 Sistahs’ are Doin’ it for themselves: Using Social Media to (Re)Center Black Women’s Voices in Educational ResearchJamila Lee-Johnson and Lora HendersonAfterwordJamila Lee-Johnson, Ashley Gaskew, and Rachelle Winkle-WagnerContributor Bios
£40.84
