Data science and analysis Books

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

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Book SynopsisTHE THOUGHT BOOK OF THE YEAR, THE TIMESA GUARDIAN BOOK OF THE YEAR''Game of Thrones-style intra-elite conflict meets big data'' TLS''Extraordinary. . . the culmination of many years of highly original and innovative work'' BloombergOne of the most iconoclastic thinkers of our time offers a brilliant new theory of how society works What leads to political turbulence and social breakdown? How do elites maintain their dominant position? And why do ruling classes sometimes suddenly lose their grip on power?For decades, complexity scientist Peter Turchin has been studying world history like no-one else. Assembling vast databases mined from 10,000 years of human activity, and then developing new models, he has transformed the way we learn from the past. End Times is the result: a ground-breaking account of how society works.The lessons, he argues, are clear. When the balance of power between the ruling class and the majority tips too far in favour of elites, income inequality surges. The rich get richer, the poor further impoverished. As more people try to join the elite, frustration with the establishment brims over, often with disastrous consequences. Elite overproduction led to state breakdown in imperial China, in medieval France, in the American Civil War - and it is happening now.But while we are far along the path toward violent political rupture, Turchin''s models also light the way to a brighter future. Drawing insight from those occasions in history where the balance was restored, End Times also points towards a different future: an escape from the patterns of the past.BEST BOOKS OF SUMMER 2023: THE GUARDIAN * THE TIMES * SUNDAY TIMES

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Book SynopsisThis is the first comprehensive overview of the exciting field of the 'science of science'. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists, policy makers, and administrators with an interest in the wider scientific enterprise.Trade Review'Wang and Barabási book is a manifesto for the science of science domain. Graduate students (as well as their mentors) owe the authors a debt of gratitude for this impressive synthesis of what is a fast-evolving field of research.' Pierre Azoulay, Massachusetts Institute of Technology'Analyzing quantitative aspects of science with state-of-art tools, Wang and Barabási have written an insightful and comprehensive book that will become a must-read for all scholars interested in science.' Yu Xie, Princeton University'In their engaging book, Wang and Barabási take a fresh look at the science of science. They convincingly argue that in the age of big data and AI applying the scientific method to science itself not only helps understand how science works but may even enhance it. We are compelled to consider the determinants of individual careers and what this means in the age of large-scale scientific collaborations. These and other questions around the meaning of scientific impact, in academia and beyond, make the book highly relevant to scientists, academic administrators and funders alike. By the time the final, forward-looking chapter ends we are hooked on all the correlations and predictions, and so it is only fitting that we are invited to join in, to help shape the field which is likely to be driven by a human-machine collaboration.' Magdalena Skipper, Nature'Overall, I found this book very stimulating. It made me wonder whether in-depth metrics analyses of 'only' the subjective narratives of authors, such as the references list they select, actually creates a foundation on which to form judgement rather than opinion? Namely, what fraction of these publications analysed for their metrics were actually underpinned by their data? As well as provoking thought, this book offers a feast of references, 424 in all. There are such further enticing reads as reference 396, Life3.0: Being Human in the Age of Artificial Intelligence. To conclude, I recommend this book for your library, and maybe even take it for your summer beach reading.' John R. Helliwell, Journal of Applied Crystallography'… a text that should appeal to practicing scientists curious about the structure of the whole scientific enterprise, academic administrators and policy makers interested in evidence-based decision-making, and researchers interested in contributing further to the "science of science." There is no better, handier, and more readable work to appeal to such audiences … Highly recommended.' M. Oromaner, Choice ConnectTable of ContentsIntroduction; Part I. The Science of Career: 1. Productivity of a scientist; 2. The H Index; 3. The Matthew Effect; 4. Age and Scientific Achievement; 5. Random Impact Rule; 6. The Q Factor; 7. Hot Streaks; Part II. The Science of Collaboration: 8. The increasing dominance of teams in science; 9. The Invisible College; 10. Coauthorship Networks; 11. Team Assembly; 12. Small and large teams; 13. Scientific Credit; 14. Credit Allocation; Part III. The Science of Impact: 15. Big Science; 16. Citation Disparity; 17. High Impact Papers; 18. Scientific Impact; 19. The Time Dimension of Science; 20. Ultimate Impact; Part IV. Outlook: 21. Can Science be Accelerated?; 22. Artificial Intelligence; 23. Bias and Causality in Science; Part V. Last thought; All the Science of Science: Appendix A1 Modeling team assembly; Appendix A2 Modeling Citations; References; Index.

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Book SynopsisJonathan Ferrar is a globally recognised consultant, speaker and business advisor in HR strategy and analytics. He is chief executive officer of global people analytics firm Insight222, and board advisor to the Chartered Institute of Personnel & Development.David Green is an influencer, advisor and prolific speaker in the field of people analytics, human resources technology and the future of work. He is managing partner at Insight222 and host of the Digital HR Leaders podcast. In 2023 he was named as one of the Most Influential HR Thinkers by HR Magazine.Trade Review"In this book, cutting-edge practitioners share insights that you can start putting into action right away."" * Adam Grant, #1 New York Times bestselling author of THINK AGAIN and host of the TED podcast, WorkLife *"Exceptional and the standard for people analytics" * Dave Ulrich, Rensis Likert Professor, School of Business, University of Michigan Partner, The RBL Group *"A superb book with practical case studies applicable to every HR professional and business leader in the use of data analytics towards better decision making."" * Low Peck Kem, Chief Human Resources Officer, Public Service Division, Prime Minister’s Office, Singapore *"Filled with topical case studies that can support any people analytics and HR team in their pursuit of creating enterprise value." * Loren I. Shuster, Chief People Officer & Head of Corporate Affairs, The LEGO Group *"HR is closer to the business than ever, and this book shows how people analytics is a business activity that drives substantial value." * Katarina Berg, Chief Human Resources Officer, Spotify *"There is a need for companies to become more human in our increasingly digital age. I have found, as a CHRO, that analytics provides equal benefit to both employees and the business, and Excellence in People Analytics dovetails these two very well. Using analytics is clearly one of the most valuable tools for becoming more human, enabling personalization and consumerisation of the employee experience." * Leena Nair Chief Human Resources Officer, Unilever *"People analytics provides business executives with another lever to improve their strategy and operations. Jonathan and David have a deep understanding of this topic and its impact on people and performance. Their work with companies across the globe is now captured in this book, providing insight with a collection of terrific case studies and practical advice. It is an outstanding guide for executives wishing to create value using people analytics." * John Boudreau, Professor Emeritus, Marshall School of Business, University of Southern California *"Excellence in People Analytics is a delightful journey of discovery through the field of people analytics with 30 vivid case studies and practical models. Businesses have recognized that workforce data can unleash the potential of talent and create value for the company. Yet people analytics is one of the biggest capability gaps for organizations. This book inspires me and is a great guide to implement people analytics beyond the 'buzz' term." * Rosa Lee, Executive Vice President of Bosch China & Corporate HR, Head of Asia-Pacific *"Excellence in People Analytics will equip HR leaders and practitioners with the structures and use cases they need to keep up with technology and learn new skills. I have little doubt that this book will define HR's contribution to the workplace of the future." * Bernard Marr, Bestselling author of Data Strategy and Data-Driven HR, futurist and strategic advisor *"Brilliantly insightful, yet practically impactful. This is a foundational book in the field of people analytics that emphasizes a business-first approach for elevating human performance. The nine dimensions for success are complemented with powerful cases that will empower any practitioner to apply these concepts." * Michael J. Arena, VP Talent & Development, AWS and author of Adaptive Space *Table of Contents Section - PART ONE: The case for people analytics; Chapter - 00: Introduction; Chapter - 000: The business value of people analytics; Section - PART TWO: Nine Dimensions for Excellence in People Analytics; Chapter - 01: Governance; Chapter - 02: Methodology; Chapter - 03: Stakeholder Management; Chapter - 04: Skills; Chapter - 05: Technology; Chapter - 06: Data; Chapter - 07: Workforce Experiences; Chapter - 08: Business Outcomes; Chapter - 09: Culture; Section - PART THREE: The next steps for people analytics; Chapter - 10: Transforming people analytics; Chapter - 11: Epilogue - the future of people analytics; Chapter - 12: Concluding remarks; Chapter - 13: Glossary; Chapter - 14: Index

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Book SynopsisThis is a book about how ecologists can integrate remote sensing and GIS in their research. It will allow readers to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. An Introduction to Spatial Data Analysis introduces spatial data handling using the open source software Quantum GIS (QGIS). In addition, readers will be guided through their first steps in the R programming language. The authors explain the fundamentals of spatial data handling and analysis, empowering the reader to turn data acquired in the field into actual spatial data. Readers will learn to process and analyse spatial data of different types and interpret the data and results. After finishing this book, readers will be able to address questions such as “What is the distance to the border of the protected area?”, “Which points are located close to a road?”, “Which fraction of land cover types exist in my study area?” using different software and techniques. This book is for novice spatial data users and does not assume any prior knowledge of spatial data itself or practical experience working with such data sets. Readers will likely include student and professional ecologists, geographers and any environmental scientists or practitioners who need to collect, visualize and analyse spatial data. The software used is the widely applied open source scientific programs QGIS and R. All scripts and data sets used in the book will be provided online at book.ecosens.org. This book covers specific methods including: what to consider before collecting in situ data how to work with spatial data collected in situ the difference between raster and vector data how to acquire further vector and raster data how to create relevant environmental information how to combine and analyse in situ and remote sensing data how to create useful maps for field work and presentations how to use QGIS and R for spatial analysis how to develop analysis scripts Table of ContentsPreface 1. Introduction and overview 1.1 Spatial data 1.2 First spatial data analysis 1.3 Next steps Part I. Data acquisition, data preparation and map creation 2. Data acquisition 2.1 Spatial data for a research question 2.2 AOI 2.3 Thematic raster map acquisition 2.4 Thematic vector map acquisition 2.5 Satellite sensor data acquisition 2.6 Summary and further reading 3. Data preparation 3.1 Deciding on a projection 3.2 Reprojecting raster and vector layers 3.3 Clipping to an AOI 3.4 Stacking raster layers 3.5 Visualizing a raster stack as RGB 3.6 Summary and further reading 4. Creating maps 4.1 Maps in QGIS 4.2 Maps for presentations 4.3 Maps with statistical information 4.4 Common mistakes and recommendations 4.5 Summary and further reading Part II. Spatial field data acquisition and auxiliary data 5. Field data planning and preparation 5.1 Field sampling strategies 5.2 From GIS to global positioning system (GPS) 5.3 On-screen digitization 5.4 Summary and further reading6. Field sampling using a global positioning system (GPS) 97 6.1 GPS in the field 98 6.2 GPX from GPS 101 6.3 Summary 102 7. From global positioning system (GPS) to geographic information system (GIS) 103 7.1 Joint coordinates and measurement sheet 104 7.2 Separate coordinates and measurement sheet 105 7.3 Point measurement to information 106 7.4 Summary 108 Part III. Data analysis and new spatial information 8. Vector data analysis 110 8.1 Percentage area covered 114 8.2 Spatial distances 118 8.3 Summary and further analyses 121 9. Raster analysis 122 9.1 Spectral landscape indices 122 9.2 Topographic indices 128 9.3 Spectral landscape categories 128 9.4 Summary and further analysis 133 10. Raster-vector intersection 134 10.1 Point statistics 135 10.2 Zonal statistics 136 10.3 Summary 138 Part IV. Spatial coding 11. Introduction to coding 140 11.1 Why use the command line and what is ‘R’? 140 11.2 Getting started 142 11.3 Your very first command 142 11.4 Classes of data 144 11.5 Data indexing (subsetting) 145 11.6 Importing and exporting data 147 11.7 Functions 148 11.8 Loops 149 11.9 Scripts 149 11.10 Expanding functionality 150 11.11 Bugs, problems and challenges 151 11.12 Notation 152 11.13 Summary and further reading 15212. Getting started with spatial coding 153 12.1 Spatial data in R 153 12.2 Importing and exporting data 158 12.3 Modifying spatial data 162 12.4 Downloading spatial data from within R 166 12.5 Organization of spatial analysis scripts 170 12.6 Summary 171 13. Spatial analysis in R 172 13.1 Vegetation indices 172 13.2 Digital elevation model (DEM) derivatives 174 13.3 Classification 175 13.4 Raster-vector interaction 179 13.5 Calculating and saving aggregated values 182 13.6 Summary and further reading 184 14. Creating graphs in R 185 14.1 Aggregated environmental information 185 14.2 Non-aggregated environmental information 189 14.3 Finalizing and saving the plot 194 14.4 Summary and further reading 195 15. Creating maps in R 196 15.1 Vector data 197 15.2 Plotting study area data 202 15.3 Summary and further reading 206 Afterword and acknowledgements 207 References 209 Index 210

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Book SynopsisThis isthedefinitive guide to doing constructivist grounded theory.From gathering rich data and conducting interviews, to undertaking coding and writing up your study, this down-to-earth book guides you through all the steps you need to do grounded theory research.This revised third edition: Showcases 9 new case studies of grounded theory research in action from scholars across the globe, including Australia, Canada, Japan and the United States. Enables you to see, at a glance, how each chapter will develop your understanding with new learning objectives. Supports you to expand your knowledge with new further reading suggestions in every chapter. Retaining Kathy Charmaz's characteristic warm and accessible style, this book is essential reading for anyone - undergraduate, postgraduate or researcher - looking to understand and do grounded theory research.

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Book SynopsisWhen would a casino stop a gambler from playing his next hand? How could a company use statistical analysis to blackball you from the job you want? Why should you worry when customer services pay attention to your needs? Beginning with examples of the mathematician who out-predicted wine buffs in determining the best vintages, and the sports scouts who now use statistics rather than intuition to pick winners, Super Crunchers exposes the hidden patterns all around us. No businessperson, academic, student, or consumer (statistically that''s everyone) should make another move without getting to grips with thinking-by-numbers - the new way to be smart, savvy and statistically superior.Trade ReviewGroundbreaking ... Not only is it fun to read. It just may change the way you think' * Stephen D Levitt co-author of Freakonomics *'Entertaining and enlightening' * Financial Times *'Convincing' * Economist *

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Book Synopsis Andrea De Mauro is currently Head of Data & Analytics at Vodafone. He has more than 15 years of international experience managing Data Analytics and Data Science organizations. He is a social media influencer, a professor, a researcher, and a popular science author of several books in English and Italian. He appeared in 2022 global Forty Under 40 list by CDO magazine. Previously, he served as Director of Business Analytics at Procter & Gamble, looking after the continuous elevation of data analytics in the business and the development of digital fluency across the global organization.   Michele Pacifico is currently Senior Product Manager for Commercial Analytics in the global analytics team of Nestlé. He has spent the last 15 years in various Business Intelligence and Data Analytics roles in Fast Moving Consumer Goods and software industries, mostly focusing on business facing responsibilities across countries and organiz

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

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Book Synopsis'Emily Oster is the non-judgemental girlfriend holding our hand and guiding us through pregnancy and motherhood. She has done the work to get us the hard facts in a soft, understandable way' Amy Schumer Parenting is full of decisions, nearly all of which can be agonized over. There is an abundance of often-conflicting advice hurled at you from doctors, family, friends, and strangers on the internet. But the benefits of these choices can be overstated, and the trade-offs can be profound. How do you make your own best decision? Armed with the data, Oster finds that the conventional wisdom doesn't always hold up. She debunks myths and offers non-judgemental ways to consider our options in light of the facts. Cribsheet is a thinking parent's guide that empowers us to make better, less fraught decisions - and stay sane in the years before preschool. *Now you can navigate the primary school years with Emily Oster too, in her new book The Family Firm, out now*Trade ReviewShows that in the hectic haze of parenthood an economist's perspective can prove surprisingly clarifying * Economist *She has crunched all the statistics on breastfeeding, potty training, working mothers and playgroups and discovered there is no optimal set of choices that will produce the perfect child. Most parents say they want happy, well-adjusted, robust kids and there are myriad ways to achieve those results. She's right -- Alice Thomson * The Times *It couldn't be more relevant ... steers clear of recommendations and cast-iron guarantees, instead promising to arm parents with information to make the decisions that are right for them * Daily Telegraph *A huge relief from the scare stories ... Cribsheet is not another call for the end of helicopter parenting or snowplow parenting or whatever kind of parenting is lighting up social media today, and it's not a call to overthrow medical wisdom; it's a call for parenting with context, and it's freeing * Washington Post *Both refreshing and useful. With so many parenting theories driving us all a bit batty, this is the type of book that we need to help calm things down. * LA Times *The Guilt-Free, Data-Driven Guide to Parenting.... uses science and stats to cut through the confusion of raising a family...Smart, relatable, and funny * Bloomberg *PRAISE FOR EMILY OSTER * - *I am so grateful for her work -- Amy SchumerA savior for whipsawed mothers ... Oster shows how data, a scary word, can be a humanizing force ... Enriching this analytical brilliance is the common sense and empathy that come from being a mother herself -- Steven Pinker * TIME's 100 Most Influential People of 2022 *

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Book SynopsisA lively, practical, first-of-its-kind guide to understanding cold, clinical data and harnessing it to tell a persuasive story.__________How many hours' worth of songs are on your Spotify Wrapped this year?How much is your commute time really worth?How do you work out how likely you are to get Covid based on the official statistics?How do your viewing hours track against the most popular shows on Netflix?Whether you're interested in global problems like climate change, running a business, or just grasping how few people have washed their hands between visiting the bathroom and touching you, this book will help math-lovers and math-haters alike translate the numbers that illuminate our world.Until very recently, most languages had no words for numbers greater than five - anything from six to infinity was known as 'lots'. While the numbers in our world have become increasingly complex, our brains are stuck in the past. Yet the ability to communicate and understand numbers has never mattered more. How can we more effectively translate numbers and stats - so fundamental to the next big idea - to make data come to life?Drawing on years of research into making ideas stick, Chip Heath and Karla Starr outline six critical principles that will give anyone the tools to communicate numbers with more transparency and meaning. Using concepts such as simplicity, concreteness and familiarity, they show us how to transform hard numbers into their most engaging form, allowing us to bring more data, more naturally, into decisions in our schools, our workplaces and our society.Trade ReviewConcise, breezy and pragmatic. * Wall Street Journal *A unique popular math book... [that] delivers a painless, ingenious education in how to communicate statistics and numbers to people who find them confusing... Packed with tables, anecdotes, and amusing facts, the narrative makes math accessible.... Astute advice for businesspeople and educators. * Kirkus Review *

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Book Synopsis''Eye-opening. An essential read for any football fan'' Jamie CarragherxGenius takes a tour of the world's most pioneering football clubs, uncovering how elite teams are using xG to win more matches. Any club not adopting this model will be left behind.The concept of Expected Goals or xG has changed how we understand football. Every fan will have heard of xG, many will understand what it is, but few will know exactly how it's being used by football teams to improve their chances of winning matches.xGenius explores the interplay between analysis, tactics and decision-making. It seeks to put the sport of football under the microscope with the aim of getting closer to the ultimate truth of what makes players, managers and teams successful. What, ultimately, wins matches.The book looks at the model which every club should adopt, one which has allowed teams like Liverpool, Arsenal, Brighton and Brentford to punch well above their fi

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Book SynopsisShortlisted for the William Hill Sports Book of the Year Award 2022Football has always measured success by what you win, but only in the last twenty years have clubs started to think about how you win. Data has now suffused almost every aspect of how football is played, coached, scouted and consumed. But it's not the algorithms or new metrics that have made this change, it's the people behind them.This is the story of modern football's great data revolution and the group of curious, entrepreneurial personalities who zealously believed in its potential to transform the game. Central to this cast is Chris Anderson, an academic with no experience in football, who saw data as an opportunity to fundamentally change a sport that did not think it could be changed. His aim: to infiltrate the strange, insular world of professional football by establishing a club whose entire DNA could be built around data.Expected Goals charts his remarkable journey into the heart of the modern game and reveals how clubs across the world, from Liverpool to Leipzig and Brentford to Bayern Munich, began to see how data could help them unearth new players, define radical tactics and plot their path to glory.

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Book SynopsisTHE NEW YORK TIMES BESTSELLERAN ECONOMIST BOOK OF THE YEARA NEW STATESMAN BOOK OF THE YEAR''This book is about a whole new way of studying the mind ... Endlessly fascinating'' Steven Pinker''A whirlwind tour of the modern human psyche'' EconomistEverybody lies, to friends, lovers, doctors, pollsters and to themselves. In Internet searches, however, people confess the truth.Insightful, funny and always surprising, Everybody Lies explores how this huge collection of data, unprecedented in human history, could just be the most important ever collected. It offers astonishing insights into the human psyche, revealing the biases deeply embedded within us, the questions we''re afraid to ask that might be essential to our well-being, and the information we can use to change our culture for the better.Trade ReviewTime and again my preconceptions about my country and my species were turned upside-down by Stephens-Davidowitz's discoveries -- Steven Pinker, author of The Better Angels of Our NatureAbsorbing and impassioned ... as an introduction to our fascinating new universe of data, Everybody Lies is hard to beat * Financial Times *Everybody Lies is an astoundingly clever and mischievous exploration of what big data tells us about everyday life. Seth Stephens-Davidowitz is as good a data storyteller as I have ever met -- Steven Levitt, co-author, FreakonomicsMove over Freakonomics. Move over Moneyball. This brilliant book is the best demonstration yet of how big data plus cleverness can illuminate and then move the world. Read it and you'll see life in a new way -- Lawrence Summers, President Emeritus of Harvard UniversityA whirlwind tour of the modern human psyche … The empirical findings in Everybody Lies are so intriguing that the book would be a page-turner even if it were structured as a mere laundry list * Economist *Everybody Lies relies on big data to rip the veneer of what we like to think of as our civilized selves. A book that is fascinating, shocking, sometimes horrifying, but above all, revealing -- Tim Wu, author of The Attention MerchantsFreakonomics on steroids - this book shows how big data can give us surprising new answers to important and interesting questions. Seth Stephens-Davidowitz brings data analysis alive in a crisp, witty manner, providing a terrific introduction to how big data is shaping social science -- Raj Chetty, Professor of Economics at Stanford UniversityA sobering guide to how much of ourselves we're putting online and what private companies might do with that information -- Helen Lewis * New Statesman 'Books of the Year' *Everybody Lies is a spirited and enthralling examination of the data of our lives. Drawing on a wide variety of revelatory sources, Seth Stephens-Davidowitz will make you cringe, chuckle, and wince at the people you thought we were -- Christian Rudder, author of DataclysmA tour de force - a well-written and entertaining journey through big data that, along the way, happens to put forward an important new perspective on human behaviour itself -- Peter Orszag, Managing Director, LazardBrimming with intriguing anecdotes and counterintuitive facts, Stephens-Davidowitz does his level best to help usher in a new age of human understanding, one digital data point at a time -- Fortune, Best New Business BooksStephens-Davidowitz, a former data scientist at Google, has spent the last four years poring over Internet search data . . . What he found is that Internet search data might be the Holy Grail when it comes to understanding the true nature of humanity * New York Post *It’s a wonderful book, but I would say that, wouldn’t I? -- Danny DoyleStephens-Davidowitz censures academics and other researchers for ignoring the largest data set ever collected, and he is probably not overstating it when he claims that the continuing study of these searches “will radically expand our understanding of mankind”. This undemanding book is a useful first step towards that knowledge’ -- Oliver Thring * Sunday Times *Seth Stephens-Davidowitz in his book “Everybody Lies,” tackles the discrepancy between the ideal version of ourselves we present to the world via social media and the confessions that we would never post there -- Judy Ketteler * International New York Times *

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Book SynopsisTrade Review"I very much recommend this book, not only to all that teach statistics to (under)graduate students, but also those that use statistics for their own research, that would like to value the work of others, or engage in debates using actual or perceived facts."---Gijs Dekkers, International Statsitical Review

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

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Book SynopsisNew and expanded edition.An International Bestseller - Over One Million Copies Sold!Shortlisted for the Financial Times/Goldman Sachs Business Book of the Year Award.Since Aristotle, we have fought to understand the causes behind everything. But this ideology is fading. In the age of big data, we can crunch an incomprehensible amount of information, providing us with invaluable insights about the what rather than the why.We''re just starting to reap the benefits: tracking vital signs to foresee deadly infections, predicting building fires, anticipating the best moment to buy a plane ticket, seeing inflation in real time and monitoring social media in order to identify trends. But there is a dark side to big data. Will it be machines, rather than people, that make the decisions? How do you regulate an algorithm? What will happen to privacy? Will individuals be punished for acts they have yet to commit? Trade Review'Just as water is wet in a way that individual water molecules aren't, big data can reveal information in a way that individual bits of data can't. Mayer-Schonberger and Cukier show us the surprising ways that enormous, complex and messy collections of data can be used to predict everything from shopping patterns to flu outbreaks' - Clay Shirky, author of Cognitive Surplus and Here Comes Everybody'Every decade, there are a handful of books that change the way you look at everything. This is one of those books. Society has begun to reckon the change that big data will bring. This book is an incredibly important start' - Lawrence Lessig, Roy L. Furman Professor of Law, Harvard Law School, and author of Remix and Free Culture'An optimistic and practical look at the big data revolution - just the thing to get your head around the big changes already underway and the bigger changes to come' - Cory Doctorow, Boing Boing'In Big Data, Mayer-Schonberger and Cukier break new ground in identifying how today's avalanche of information fundamentally shifts our basic understanding of the world. Argued boldly and written beautifully, the book clearly shows how companies can unlock value, how policymakers need to be on guard, and how everyone's cognitive models need to change' - Joi Ito, Director of the MIT Media Lab'This brilliant book cuts through the mystery and the hype surrounding big data. A must-read for anyone in business, information technology, public policy, intelligence, and medicine. And anyone else who is just plain curious about the future' - John Seely Brown, former Chief Scientist, Xerox Corp. and head of Xerox Palo Alto Research Centre'The book teems with great insights on the new ways of harnessing information, and offers a convincing vision of the future. It is essential reading for anyone who uses - or is affected by - big data' - Jeff Jonas, IBM Fellow & Chief Scientist, IBM Entity Analytics'Big Data is a must-read for anyone who wants to stay ahead of one of the key trends defining the future of business' - Marc Benioff, Chairman and CEO, salesforce.comAn excellent primer - Financial Times

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Book SynopsisDirty data is a problem that costs businesses thousands, if not millions, every year. In organisations large and small across the globe you will hear talk of data quality issues. What you will rarely hear about is the consequences or how to fix it.Between the Spreadsheets: Classifying and Fixing Dirty Data draws on classification expert Susan Walsh’s decade of experience in data classification to present a fool-proof method for cleaning and classifying your data. The book covers everything from the very basics of data classification to normalisation and taxonomies, and presents the author’s proven COAT methodology, helping ensure an organisation’s data is Consistent, Organised, Accurate and Trustworthy. A series of data horror stories outlines what can go wrong in managing data, and if it does, how it can be fixed. After reading this book, regardless of your level of experience, not only will you be able to work with your data more efficiently, but you will also understand the impact the work you do with it has, and how it affects the rest of the organisation.Written in an engaging and highly practical manner, Between the Spreadsheets gives readers of all levels a deep understanding of the dangers of dirty data and the confidence and skills to work more efficiently and effectively with it. Trade Review'If you are teaching data science then all your students should be made aware of this book. When it comes to organisations. I can’t see any reason for not making sure that anyone managing an Excel data base has a copy to refer to.... Excellent value for the price' -Martin White, Informer 'I gained many practical tips for using a spreadsheet to clean data, and alternate ways of approaching classification while reading this book - there is hope for cleaner data!' - Mary Silvia Whittaker, SLA Taxonomy'I have rarely found such a brilliant argument about the importance of COAT - the overall approach to the management of data. The author approaches all her topics with palpable humour and presents them in lively and attractive style. A relevant acquisition for business information departments or their equivalents in public libraries as much as putting it on the desks of the people dealing with all kinds of business data.'Elena Maceviciute, Swedish School of Library and Information ScienceTable of ContentsBetween the Spreadsheets: Classifying and Fixing Dirty Data

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

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Book SynopsisMathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science. As it applies to the curriculum, research, and career opportunities, data science is a fast-growing field. Contributors from both academics and industry present their views on these opportunities and how to advantage them.

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Book SynopsisThis book offers a hands-on introduction and teaching resource for students, users, and teachers of Qualitative Comparative Analysis (QCA; Ragin, 1987, 2000, 2008b). Given its superior ability to model certain aspects of complexity, QCA has made inroadsinto virtually every social science discipline and beyond. Software solutions for QCA have also been developing at a fast pace. This book seeks to reduce the time and effort required when we first encounter the logic of not just a new method but also newsoftware. It offers a genuinely simple, intuitive, and hands-on resource for implementing the state-of-the-art protocol of QCA using R, the most advanced software environment for QCA. Our book has an applied and practical focus--Trade ReviewIn a relatively short, clear, and well-written textbook, the authors cover all the essentials of QCA. It includes all the current practices and developments that one needs to do a complete QCA analysis. I am using it in my QCA class and I think the students will like it. Gary Goertz, Kroc Institute for International Peace Studies, University of Notre DameThis book is simply a must for anyone aiming at exploiting 'hands on' the distinctive analytic leverage of QCA, via the vast possibilities of the R environment. Benoît Rihoux, University of Louvain and COMPASSS global network (compasss.org)Every now and then, a book comes along of which you think 'why wasn't this around when I was learning this stuff?'. Because sometimes, you just want to learn from the best. The authors have written an excellent guide for both first-time users and experienced QCA scholars: robust, powerful, and a must-read. Bart Cambré, Antwerp Management SchoolEasily accessible and loaded with a wealth of examples, this important book by Oana, Schneider, and Thomann provides an expert guide to the set-analytic perspective. The integration of concepts and research strategies with the R software package makes their approach particularly successful. I expect it will quickly become the standard introduction to QCA. Peer C. Fiss, University of Southern CaliforniaSocial scientists interested in QCA should not miss this book. Oana, Schneider, and Thomann offer an introduction to QCA that is friendly, up to date, and technically advanced. The volume covers set relations, calibration of “crisp” and “fuzzy” sets, necessary conditions, sufficient configurations, temporality, advanced diagnostics, and post-QCA tools. Each chapter contains intuitive examples, advanced tips, and detailed implementation instructions in R. Aníbal Pérez-Liñán , University of Notre DameTable of ContentsPart I. Getting started: Introduction: QCA in a nutshell; Part II. Before the analytic moment: 2. Calibrating and combining sets; Part III. During the analytic moment: 3. Necessary conditions; 4. Sufficient conditions; Part IV. After the analytic moment: 5. Rounding up solid a QCA; 6. Post-QCA tools; 7. Summary and outlook.

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Book Synopsis''A necessary book for our times. But also just great fun'' Saul Perlmutter, Nobel LaureateThe world is awash in bullshit, and we''re drowning in it. Politicians are unconstrained by facts. Science is conducted by press release. Start-up culture elevates hype to high art. These days, calling bullshit is a noble act.Based on a popular course at the University of Washington, Calling Bullshit gives us the tools to see through the obfuscations, deliberate and careless, that dominate every realm of our lives. In this lively guide, biologist Carl Bergstrom and statistician Jevin West show that calling bullshit is crucial to a properly functioning social group, whether it be a circle of friends, a community of researchers, or the citizens of a nation. Through six rules of thumb, they help us recognize bullshit whenever and wherever we encounter it - even within ourselves - and explain it to a crystal-loving aunt or casually racist grandfather.Trade ReviewEssential reading. Even if you feel you can trudge through verbal bullsh!t easily enough, this book will give you the tools to swim through numerical snake-oil. . . -- Simon Ings * The Telegraph *A modern classic that is troubling in some places, sobering in others, and enlightening from beginning to end. . . Bergstrom and West leave the reader feeling a very particular kind of smarter: the empowered kind. . . It works anywhere, for anyone: the academic, the citizen-scientist, citizen-skeptic, and citizen-curious * Wired *A helpful guide to navigating a world full of doubtful claims based on spurious data. Using clever anecdotes, nods to online culture and allusions to ancient philosophy, the book tells ordinary readers how to spot nonsense-even if they are not numerical whizzes * The Economist *Each of us now swims through deception so pervasive that we no longer realize it's there. Calling Bullshit presents a master class in how to spot it, how to resist it, and how to keep it from succeeding -- Paul Romer, Nobel LaureateIf I could make this critical handbook's contents required curriculum for every high school student (thus replacing trigonometry), then I would do so. I highly recommend Calling Bullshit for our modern existence in the age of misinformation -- Cathy O’Neil, author of Weapons of Math DestructionThe information landscape is strewn with quantitative cowflop; read this book if you want to know where not to step -- Jordan Ellenberg, author of How Not to be WrongI laughed, I cried -- to read Bergstrom and West's great examples of 'bullshit.' This is a gripping read for anybody who cares about how we are fooled (and how not to be), and the connection to numeracy and science. But it's also just great fun. This is a necessary book for our times -- Saul Perlmutter, Nobel Laureate

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Book SynopsisBernard Marr is one of the leading voices in Technology and Innovation. A futurist and strategic performance consultant, he has advised many of the world's best-known organizations on their business and data strategies. A frequent keynote speaker, he also writes on the topic of data and analytics for various publications including Forbes and the Huffington Post. Bernard Marr is also the author of Data Strategy (2021) and The Intelligence Revolution (2020) published by Kogan Page.Trade Review"Without a doubt human capability (talent + leadership + organization + HR) increasingly delivers value to all stakeholders. This excellent book provides business and HR leaders the information required to improve decision making. Bernard's insights on analytics and AI will be the keys for progress." * Dave Ulrich, Rensis Likert Professor, Ross School of Business, University of Michigan Partner, The RBL Group *"If anyone was going to publish a book about the impact of the latest technology developments such as AI on the field of HR and People Analytics my bets were on Bernard Marr. And you won't be disappointed. The book offers a deep dive into the world of data of every kind, every possible use case, honest overview of technology and important considerations. It has never been more critical to educate ourselves about it." * Maja Luckos, VP, Employee Success, Salesforce *"This book propelled me into a world of possibilities for HR leaders in embracing the 'intelligence revolution' to shape people strategies that add value to their organizations and their people. It's enlightened me to the power of AI-enabled HR and how I might use it, and it's made me want to learn more. This is a must read for all HR leaders." * Linda Sleath, Group HR Director, Topps Tiles Plc. *"Data-Driven HR strikes a nice balance between exploring emerging trends in people analytics while primarily serving as a practical guide to HR professionals at any stage of their data journey. The second edition seamlessly weaves AI into a narrative that's easy to engage with and is packed full of examples that bring the theories to life." * Mark Ferrie, People Analytics Director, Meta *"Data-Driven HR is a terrific overview of the enormous world of people analytics and AI. For people trying to understand this important space, this book shows you the way." * Josh Bersin, Global Industry Analyst and CEO of The Josh Bersin Company *"Data, analytics and AI provides to elevate HR from its traditional role as a support function to one of a strategic partner creating value for the enterprise, its customers and its employees. There's a well-thumbed copy of the first edition of Data Driven HR on my bookshelf, and in this timely update Marr, one of the most knowledgeable people on the topic, explains how data and AI can enable HR to drive better decision making about people, deliver an enhanced service to employees; and make HR processes more efficient." * David Green, Managing Partner at Insight222, co-author of Excellence in People Analytics, and host of the Digital HR Leaders podcast. *"Bernard Marr has once again delivered an indispensable guide to harnessing the power of data, analytics and AI in HR. This updated edition thoroughly captures the latest innovations shaping human resources while still being accessible for HR professionals at any level. Through compelling examples and clear frameworks, Marr demonstrates how to drive business value through evidence-based talent practices. This is a must-read playbook for any HR leader looking to build capabilities in data-driven decision-making." * Professor Max Blumberg, PhD, University of Leeds *"This is a great guide for HR professionals who are grappling with the transition to becoming data led. It's easy to read, and with real examples and case studies across the employee lifecycle, it's also a pragmatic resource to have in your HR toolkit." * Ashish Sinha Korn Ferry Head of People Analytics, AI & Strategy EMEA Practice Leader *"AI is transforming the world of work and our personal lives. With a people-centric approach, Bernard Marr demystifies data driven AI enabled HR with context, thought provoking insights and examples of AI at the time this book was written. We all have a role to play when it comes to this rapidly evolving space as the output of AI will be a reflection of our culture and values. Staying on top of leading practices, lessons learned, emerging regulations and standards is critical so we can unlock AI's potential and value add to the business, our customers and employees while minimizing risk. This book sets the foundation so we can do just that!" * Terilyn Juarez Monroe, Terilyn Juarez Monroe, Chief People Officer *"Data-Driven HR is an indispensable resource for Career Services professionals looking to equip their students with cutting-edge strategies in today's competitive job market. This comprehensive book offers invaluable insights into recruitment and candidate selection, employer branding, pinpointing the most effective recruitment channels, and harnessing AI-enhanced automation to identify and assess the best candidates for businesses. It's a game-changer for career advisors committed to empowering their students with the knowledge and skills needed to excel in the evolving world of talent acquisition and HR." * Dr. Amber Wigmore Álvarez, Associate Professor, IE Business School and IE University *Table of Contents Chapter - 00: Preface; Section - ONE: Data, Analytics and AI in HR; Chapter - 01: How data and AI are transforming HR; Chapter - 02: How data and AI have come to revolutionise HR; Chapter - 03: The Data, Analytics and AI tools available to HR; Section - TWO: Data-Driven and AI-enabled HR in Practice; Chapter - 04: Better HR insights and decision-making; Chapter - 05: Recruitment and candidate selection; Chapter - 06: Employee Onboarding; Chapter - 07: Performance Monitoring and Management; Chapter - 08: Employee Training and Development; Chapter - 09: Performance monitoring and management; Chapter - 10: Identify the use cases; Chapter - 11: Building skills and aligning culture; Section - THREE: Making data-driven and AI enabled HR happen; Chapter - 12: Identifying the use cases for your organization; Chapter - 13: The future of HR

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Book SynopsisWhen you know the cause of an event, you can affect its outcome. This accessible introduction to causal inference shows you how to determine causality and estimate effects using statistics and machine learning.In Causal Inference for Data Science you will learn how to: Model reality using causal graphs Estimate causal effects using statistical and machine learning techniques Determine when to use A/B tests, causal inference, and machine learning Explain and assess objectives, assumptions, risks, and limitations Determine if you have enough variables for your analysis It''s possible to predict events without knowing what causes them. Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes. Causal Inference for Data Science shows you how to build data science tools that can identify the root cause of trends and events. You''ll learn how to interpret historical data, understand customer behaviors, and empower management to apply optimal decisions.

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Book SynopsisA year of HBR''s essential thinking on tech—all in one place.Generative AI, biometrics, spatial computing, electric vehicles—new technologies like these are reshaping organizations at the hybrid office, on factory floors, and in the C-suite. What should you and your company be doing now to take advantage of the new opportunities these technologies are creating—and avoid falling victim to disruption?The Year in Tech 2025: The Insights You Need from Harvard Business Review will help you understand what the latest and most important tech innovations mean for your organization and how you can use them to compete and win in today’s turbulent business environment.Business is changing. Will you adapt or be left behind?Get up to speed and deepen your understanding of the topics that are shaping your company''s future with the Insights You Need from Harvard Business Review series. Featuring HBR''s smartest thinki

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Book Synopsis'A superb romcom, with relatable and loveable characters that literally burst from the pages. With laugh-out-loud moments, woven between a heartwarming and hopeful story, it charmed me from the first page'Carmel HarringtonTrue love is only just a swipe away? Right?Iris lives by numbers. The only thing missing from her perfectly calibrated life is a partner - and not for lack of trying. After decades of disappointment, Iris practically has a PhD in online dating. But something still eludes her: that unquantifiable spark.Kim is too busy being the life of the party to look for love. Her terrible dates make great stories for her friends and co-workers, as long as she's not caught by her tyrannical boss, Iris.Connie, Kim's recently widowed mum, is single for the first time since the 1970s. The dating game has changed a lot since her day . . .Sick of being let down, Iris takes matters into her own hands - using her analytical skills to create the first real formula for love. With Kim and Connie on board, they launch Analyzed, a dating app like no other.As Analyzed takes the world by storm, are the three women in over their heads? Is love really just a numbers game?'A laugh on every page, entirely empathetic characters and a warm heart in the middle of the story. I found this book highly entertaining and totally satisfying' Liz NugentPraise for Claudia Carroll:'Brilliantly funny' Sun'An immensely talented writer' Sinéad Moriarty'Brilliant' Bella

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

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Book SynopsisMike Grigsby, based in Orlando, Florida, has more than 30 years' experience in the field of marketing analytics. He was formerly vice president of customer insights and advanced analytics at Brierley and Partners and of strategic business analysis and advanced analytics at Targetbase and has also held leadership positions at Hewlett-Packard and Gap. Previously an adjunct professor at the University of Texas at Dallas, he taught analytics at both graduate and undergraduate levels. He is the author of Advanced Customer Analytics, also published by Kogan Page.Trade Review"In Marketing Analytics, Mike Grigsby takes passionate marketing strategists on a practical, real-life journey for solving common marketing challenges. By combining the concepts and knowledge areas of statistics, marketing strategy and consumer behaviour, Grigsby recommends scientific and innovative solutions to common marketing problems in the current business environment. I highly recommend reading this book as it adds a completely new dimension to marketing science." * Kristina Domazetoska, Project Manager and Implementation Consultant at Insala – Talent Development and Mentoring Solutions *"Grigsby's book is the right blend of theory applied to the real-world large-scale data problems of marketing. It's exactly the book I wish I'd had when I started out in this field." * Jeff Weiner, Senior Director, Analytics, One10 *Table of Contents Section - 00: Introduction; Section - PART ONE: How can marketing analytics help you?; Chapter - 01: Overview of statistics; Chapter - 02: Consumer behaviour and marketing strategy; Chapter - 03: What is an insight?; Section - PART TWO: Dependent variable techniques; Chapter - 04: Modelling demand and elasticity; Chapter - 05: Polynomial distributed lags; Chapter - 06: Using Poisson regression; Chapter - 07: Logistic regression and market basket analysis; Chapter - 08: Survival modelling and lifetime value; Chapter - 09: Panel regression and same store sales; Chapter - 10: Introduction to forecasting; Section - PART THREE: Interrelationship techniques; Chapter - 11: Simultaneous equations; Chapter - 12: Principal components and factor analysis; Chapter - 13: Segmentation overview; Chapter - 14: Tools of segmentation; Section - PART FOUR: Focus on media and loyalty; Chapter - 15: Modelling marcom value; Chapter - 16: Media mix modelling; Chapter - 17: Overview of loyalty; Chapter - 18: Loyalty with SEM; Chapter - 19: The customer loyalty journey; Section - PART FIVE: More important topics for everyday marketing; Chapter - 20: Statistical testing; Chapter - 21: Introduction to Big Data; Chapter - 22: Conclusion - The finale; Chapter - 23: References; Chapter - 24: Further reading;

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Book SynopsisMartin J. Eppler, PhD, is a chaired professor of communications management at St. Gallen University, one of Europe's top 10 business schools, where he is the director of a global MBA program. He is the author of 22 books, including the getabstract international business book of the year winner Meet up! (Cambridge University Press). He is a 10 times MBA course of the year winner and received numerous best paper awards for his research on communication issues in management. His research has been featured in The Guardian, Businessweek, the Harvard Business Review, Inc. magazine, the MIT Technology Review. He is an advisor to organizations such as the European Central Bank, the United Nations, IATA, Tawuniya, Porsche, Swiss Re, or Nike. He has been a guest professor at Georgiatech in the US, Cambridge University in the UK, Simon Fraser University in Canada, Aalto University in Finland, and CUFE in China, as well as Pacifico in Peru. For the last 15 years, he has been edTrade Review"Technology requires human communication to make data impactful. The insights of this book show how to bring data to the people." Christoph Keller, CEO IBM Switzerland "A clear, concise guide for business leaders on how to make sense of data, intelligently explore or question findings, and not get bamboozled by experts." Dave Gray, founder, XPLANE, and author of The Connected Company and of Gamestorming . "This powerful book shows how managers can better understand and communicate data and analytics to seize business opportunities and increase competitiveness in an increasingly data driven world." Dr. Andreas Schönenberger, CEO Sanitas and former CEO of Google Switzerland "This book is your best investment to continuously grow your data mastery. Realistic use cases are combined with meticulously selected tools. Powerful." Valérie Saintot, Division Head, European Central Bank "No matter what industry you're in, you're in a data industry. And this is the guide to get more value out of your data." Dr. Thomas Wellauer, Chairman of the Board of the Swiss Stock Exchange, SIX Group "Great entrepreneurs and innovators are also great storytellers, and data has the power to either bring the narrative to life or kill it by boring, excluding or confusing your audience. How To Talk About Data builds much-needed bridges over common traps we can all fall into." Dr. Sarah Lubik, Director of Entrepreneurship, Simon Fraser University, multiple board member, founder, executive director of the Chang Institute for Entrepreneurship (Vancouver), elected as innovation leader by the Canadian government "The beauty of the book How To Talk About Data is putting communication at the center, enabling us to have better data and analytics conversations. It's revealing the often unforeseen efforts and invisible barriers to truly becoming a data-led organization and gives actionable advice how to address this for sustainable change." Bianca Scheffler, Head of Data Culture & Innovation, Swiss ReManagement Ltd. "An indispensable handbook for everyone leading authentic and inclusive conversations about data analytics." Benjamin Wiederkehr, founder, partner, and Managing Director of Interactive Things Inc., Colorado, USA "An efficient refresher on how to communicate through data and on which mistakes to avoid and an effective tool for any manager looking to up their data literacy game." Christian Koblmiller-Kampmueller, Chief Data Officer, International Committee of the Red CrossTable of ContentsPART I: The Manager as Data User: Making Sense of Data 1. Overcoming Analytics Anxiety 2. Making sense of statistics: Achieving a global view of data 3. Predicting outcomes: Modelling the world with data 4. Understanding relationships: Probing for the when, how, and why 5. Differences that make a difference: How to segment the world statistically (and what machine learning looks like) PART II: The Manager as Data Facilitator: Conveying Data-based Insights 6. The Five Magic Ingredients of Data Storytelling 7. The Data Storytelling Canvas 8. How to visually DESIGN your Data: A Manager’s Chart Guide 9. How to Work with Software in front of others 10. Effective Analytics Q & A Sessions PART III: The Manager as Collaboration Partner: Making Analytics Work 11. A Typology of common cooperation challenges in Analytics 12. A Recipe for Better Analytics Collaboration Conclusion: Future proof Analytics

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Book SynopsisJordan Morrow is known as the "Godfather of Data Literacy", having helped pioneer the field by building one of the world's first data literacy programs. He is the founder and CEO of Bodhi Data, served as the Chair of the Advisory Board for The Data Literacy Project and has helped companies and organizations around the world, including the United Nations, build and understand data literacy. Morrow is the author of three books: Be Data Literate, Be Data Driven, and Be Data Analytical, all published by Kogan Page. He is based near Salt Lake City, Utah.Trade Review"Jordan Morrow has done it again. This flows on perfectly from his first book, Be Data Literate, and creates a realistic and achievable pathway for organizations to become data driven. And, of course, at the center of it is people-a man after my own data heart! I hope that wasn't a spoiler alert..." * Susan Walsh, The Classification Guru and author of Between the Spreadsheets *"Understanding the basics of data science and AI is key to succeeding today. This book provides key insights into preparing better for a more data-driven future. A fascinating read for anyone looking to stay on top of how data science is revolutionizing just about everything. Jordan Morrow has done an amazing job taking a complex topic and synthesizing it for all to understand. Highly recommended." * Manuj Aggarwal, Founder and Chief Innovation Officer, TetraNoodle *"Jordan Morrow delivers a great follow-up to his first book, Be Data Literate. The phrase 'data driven' has evolved into a meaningless term and hollow buzzword through misuse and misunderstanding. With his new book, Jordan helps us understand what it actually means and why it matters. He then offers a simple, practical approach to help companies get there. For organizations and leaders who know there is value in their data but have struggled to unleash it, this is the book for you." * Brian Ferris, Chief Data, Analytics and Technology Officer, Loyalty NZ *"One of the pioneers in the field of data." * Jimmy Rex, investor, author and podcast host of The Jimmy Rex Show *"Jordan Morrow is a true authority on data literacy and the opportunity for organizations that want to become data driven. In this book, he covers all the critical ingredients organizations need to become truly data driven in a world that's been transformed by the Covid-19 pandemic. His second book is a great guide for leaders and practitioners alike and provides the necessary tools for transforming your business into a data-driven powerhouse." * Eva Murray, Lead Evangelist EMEA, Snowflake *"Jordan Morrow has followed his brilliant first book, Be Data Literate, with this masterpiece. It's a comprehensive and insightful book in which he shares his passion and experience in the data space to give everyone the power to harness the true power of data." * Bernard Marr, best-selling author, futurist, business and technology adviser *"This book aims high, with strong data culture principles and data literacy foundations, while maintaining significant focus on the strategy that guides data initiatives to successful completion and mission success. Failure is not an option in this data-intensive world. Be Data Driven is an excellent launchpad and mission guidebook for your organization's data-driven journey." * Kirk Borne, Chief Science Officer, DataPrime *"Jordan Morrow writes brilliantly, engages with the reader and, most crucially, demystifies the world of data and analytics. It all makes sense, even for those not familiar with the subject or struggling to understand. This book is a terrific start and for those passionate about business and success, essential!" * Mike Roe, CEO, Tensense.ai *Table of Contents Section - ONE: Foundational; Chapter - 01: A data-driven world; Chapter - 02: The impact of Covid-19 on organizations and data; Chapter - 03: Technologies advancing data and analytics, and the need for the human element; Chapter - 04: What is a data-driven organization?; Section - TWO: Gaps; Chapter - 05: Foundational skills gaps; Chapter - 06: Pillars of an organizational data strategy; Chapter - 07: The gap in leadership; Chapter - 08: The biggest hurdle: culture; Section - THREE: Building your data-driven organization; Chapter - 09: Decide your outcome; Chapter - 10: Build your strategy; Chapter - 11: Be data driven—start your journey!; Chapter - 12: References

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Book SynopsisTHE INSTANT NEW YORK TIMES BESTSELLER 'Chart a child's path with less stress and more optimization for healthy habits and future success' Time From age 5 to 12, parenting decisions get more complicated and have lasting consequences. What's the right kind of school? Should they play a sport? When's the right time for a phone? Making these decisions is less about finding the specific answer and more about taking the right approach. Along with these bigger questions, Oster investigates how to navigate the complexity of day-to-day family logistics. The Family Firm is a smart and winning guide to how to think more clearly - and with less ambient stress - about the key decisions of these early years.Trade ReviewOster is a self-described data nerd, a delightful contrarian who dared question the status quo, shush the shamers and tell parents what made sense. * The New York Times Book Review *A targeted mini-MBA program designed to help moms and dads establish best practices for day-to-day operations ... Because this is an Oster book, there's data scattered everywhere - on the development of reading skills by age ... It's all presented in the breezy, skeptical style that's made Oster's work a must-read for parents who don't have the time to investigate Finnish studies about integrating extracurriculars into the school day. * Washington Post *A guide ... to chart a child's path with less stress and more optimization for healthy habits and future success. * TIME Magazine *Oster's prose flows well (as usual) lightly sprinkled with the dry wit that suffuses her other books. * Salon *Oster offers a plethora of rational guidance for parents of kids between pre-K and middle school in this eminently practical guide. * Publishers Weekly *Merging a business approach with her trademark empowering voice, Emily dispenses the stress-less advice you actually want. -- Audrey Goodson Kingo * Working Mother *With Oster's help, rather than fear this next stage of parenting, readers can embrace (and even enjoy) the challenge. * Booklist *Emily Oster dives into the data on parenting issues, cuts through the clutter, and gives families the bottom line to help them make better decisions. Her books on pregnancy and toddlers skyrocketed her to parenting-world fame, and now she's back, crunching the numbers on topics that keep parents with school-age kids up at night. * Good Morning America *Oster draws on her experience as a business school professor to suggest that economic reasoning - the art of making decision-making given constraints - can tell us a lot about how to make some of these hard decisions a little better ... Some careful, economics-inspired thinking can help reduce the anxiety, tension, and stress ... For that alone, The Family Firm is worth picking up -- Charles Fain Lehman * The Washington Free Beacon *Praise for Cribsheet: * - *She has crunched all the statistics on breastfeeding, potty training, working mothers and playgroups and discovered there is no optimal set of choices that will produce the perfect child. Most parents say they want happy, well-adjusted, robust kids and there are myriad ways to achieve those results. She's right -- Alice Thomson * The Times *It couldn't be more relevant ... steers clear of recommendations and cast-iron guarantees, instead promising to arm parents with information to make the decisions that are right for them * Daily Telegraph *Parenting can be fraught. Cribsheet aims to help parents do better. * Economist *A huge relief from the scare stories ... Cribsheet is not another call for the end of helicopter parenting or snowplow parenting or whatever kind of parenting is lighting up social media today, and it's not a call to overthrow medical wisdom; it's a call for parenting with context, and it's freeing * Washington Post *Both refreshing and useful. With so many parenting theories driving us all a bit batty, this is the type of book that we need to help calm things down. * LA Times *The Guilt-Free, Data-Driven Guide to Parenting ... uses science and stats to cut through the confusion of raising a family ... Smart, relatable, and funny * Bloomberg *Praise for Emily Oster: * - *A revelation -- Pandora SykesI am so grateful for her work -- Amy SchumerIn my household, [Emily Oster] is the all-knowing Aunt we have never met. Parenting would be a lot more stressful without these books. -- Adam Ozimek * Forbes *

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

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Book SynopsisAn unimaginably vast amount of data is now generated by our on-line lives and businesses, At the same time, our ability to store, manage, analyse, and exploit this data is becoming ever more sophisticated. This Very Short Introduction maps out the technology, and also the range of possibilities, challenges, and ethical questions it raises.Trade ReviewBig data is in the news, and this excellent very short introduction brings the reader up to speed and enables them to understand the various components and implications. * Paradigm Explorer *This is a very useful, concise introduction to the topic of big data. * Jonathan Cowie, Science Fact & Science Fiction Concatenation *A very short introduction to a very big subject ... arguably the most topical of this book series ... This very short introduction is perfect for anyone who is a little bit baffled by the very concept of big data. Holmes introduces the subject in a format that is both concise and manageable. * Jade Taylor-Salazar, E&T Magazine *Table of ContentsBYTE SIZE CHART; REFERENCES; FURTHER READING; INDEX

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Book SynopsisCombining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies.Trade ReviewThe book allows methods for dealing with large data to be explained in a logical process suitable for both undergraduate and post-graduate students ... With sport performance analysis evolving into deal with big data, the book forms a key bridge between mathematics and sport science * John Francis, University of Worcester *Table of ContentsI BASIC COMPUTATIONS AND VISUALIZATION; II DIFFERENTIAL AND PARTIAL DIFFERENTIAL EQUATIONS; III COMPUTATIONAL METHODS FOR DATA ANALYSIS; IV SCIENTIFIC APPLICATIONS

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

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

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Book Synopsis''A statistical national treasure'' Jeremy Vine, BBC Radio 2''Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force'' Popular ScienceDo busier hospitals have higher survival rates? How many trees are there on the planet? Why do old men have big ears? David Spiegelhalter reveals the answers to these and many other questions - questions that can only be addressed using statistical science.Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way statistical claims can be sensationalised, particularly in the media. In the age of big data, as data science becomes established as a discipline, a basic grasp of statistical literacy is more important than ever. In The Art of Statistics, David Spiegelhalter guides the reader through the essential principles we need in order to derive knowledge from data. Drawing on real world problems to introduce conceptual issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether serial killer Harold Shipman could have been caught earlier, and if screening for ovarian cancer is beneficial. ''Shines a light on how we can use the ever-growing deluge of data to improve our understanding of the world'' NatureTrade ReviewDavid Spiegelhalter is probably the greatest living statistical communicator; more than that, he's one of the great communicators in any field. This marvellous book will transform your relationship with the numbers that swirl all around us. Read it and learn. -- Tim HarfordThere is something in here for everyone ... A call to arms for greater societal data literacy ... Spiegelhalter's work serves as a reminder that there are passionate, self-aware statisticians who can argue eloquently that their discipline is needed now more than ever. * Financial Times *Shines a light on how we can use the ever-growing deluge of data to improve our understanding of the world . . . The Art of Statistics will serve students well. And it will be a boon for journalists eager to use statistics responsibly - along with anyone who wants to approach research and its reportage with healthy scepticism. * Nature *What David Spiegelhalter does here is provide a very thorough introductory grounding in statistics without making use of mathematical formulae. And it's remarkable. Spiegelhalter is warm and encouraging - it's a genuinely enjoyable read ... This book should be required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force. * Popular Science *The Art of Statistics is in the great educational tradition of its publishing imprint, Pelican Books: an attempt to get everyone up to speed with the practical uses of statistics, without pages of terrifying equations or Greek letters. In a series of spry, airy chapters, he succeeds fabulously ... Lucid and readable. In an age of scientific clickbait, 'big data' and personalised medicine, this is a book that nearly everyone would benefit from reading. * Spectator *Important and comprehensive -- Hannah Fry * New Yorker *This is an excellent book. Spiegelhalter is great at explaining difficult ideas . . . Yes, statistics can be difficult. But much less difficult if you read this book. * Evening Standard *Like the fictional investigator Sherlock Holmes, Spiegelhalter takes readers on a trail to challenge methodology and stats thrown at us by the media and others. But where other authors have attempted this and failed, he is inventive and clever in picking the right examples that spark the reader's interest to become active on their own. * Engineering and Technology *Do you trust headlines telling you . . . that bacon, ham and sausages carry the same cancer risk as cigarettes? No, nor do I. That is why we need a book like this that explains how such implausible nonsense arises in the first place. Written by a master of the subject . . . this book tells us to examine our assumptions. Bravo. * Standpoint *

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Book Synopsis''I couldn''t imagine a better guidebook for making sense of a tragic and momentous time in our lives. Covid by Numbers is comprehensive yet concise, impeccably clear and always humane'' Tim HarfordHow many people have died because of COVID-19? Which countries have been hit hardest by the virus? What are the benefits and harms of different vaccines? How does COVID-19 compare to the Spanish flu? How have the lockdown measures affected the economy, mental health and crime?This year we have been bombarded by statistics - seven day rolling averages, rates of infection, excess deaths. Never have numbers been more central to our national conversation, and never has it been more important that we think about them clearly. In the media and in their Observer column, Professor Sir David Spiegelhalter and RSS Statistical Ambassador Anthony Masters have interpreted these statistics, offering a vital public service by giving us the tools we need to make sensTrade ReviewA clear and extremely readable guided tour of the pandemic... This book represents an extremely timely contribution... If journalists, politicians and the public were all provided with a copy then the debate would be vastly better informed, with much more light than heat -- Oliver Johnson * Guardian *Cuts through the noise and disinformation about the pandemic... In single well-evidenced sentences [Spiegelhalter] and Masters can pronounce on months-long conflicts... Reading it, it feels as though there are adults in the room -- Tom Whipple * The Times *Fascinating -- Jeremy VineA valuable overview of COVID-19 statistics and how to navigate them. Rather than just quoting numbers, Spiegelhalter and Masters discuss how to think about epidemic data... No doubt many books will be written on the COVID-19 pandemic... But if they want to get the statistics straight, their authors may want to read Covid by Numbers first -- Adam Kucharski * Lancet *I couldn't imagine a better guidebook for making sense of a tragic and momentous time in our lives. Covid by Numbers is comprehensive yet concise, impeccably clear and always humane -- Tim Harford, author of How To Make The World Add UpA clear, concise statistical journal of the plague year. If you want to understand the numbers behind the virus that stopped the world, you ought to read this book -- Tom Chivers, author of The RationalistsFantastic and wonderfully readable. A much needed antidote to the often murky and misinterpreted world of Covid data, explained in a straightforward and clear way - yet always remembering the humanity the data represents -- Dr Hannah Fry

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

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Book SynopsisNext-generation DNA and RNA sequencing has revolutionized biology and medicine. With sequencing costs continuously dropping and our ability to generate large datasets rising, data analysis becomes more important than ever. Next-Generation Sequencing Data Analysis walks readers through next-generation sequencing (NGS) data analysis step by step for a wide range of NGS applications. For each NGS application, this book covers topics from experimental design, sample processing, sequencing strategy formulation, to sequencing read quality control, data preprocessing, read mapping or assembly, and more advanced stages that are specific to each application. Major applications include: RNA-seq: Both bulk and single cell (separate chapters) Genotyping and variant discovery through whole genome/exome sequencing Clinical sequencing and detection of actionable variants De novo genome assembly Table of Contents 1. The Cellular System and The Code of Life. 2. DNA Sequence: the Genome Base. 3. RNA: the Transcribed Sequence. 4. Next-Generation Sequencing (NGS) Technologies: Ins and Outs. 5. Early-Stage Next-Generation Sequencing (NGS) Data Analysis: Common Steps. 6. Computing Needs for Next-Generation Sequencing (NGS) Data Management and Analysis. 7. Transcriptomics by Bulk RNA-Seq. 8. Transcriptomics by Single Cell RNA-Seq. 9. Small RNA Sequencing. 10. Genotyping and Variation Discovery by Whole Genome/Exome Sequencing. 11. Clinical Sequencing and Detection of Actionable Variants. 12. De Novo Genome Assembly with Long and/or Short Reads. 13. Mapping Protein-DNA Interactions with ChIP-Seq. 14. Epigenomics by DNA Methylation Sequencing. 15. Whole Metagenome Sequencing for Microbial Community Analysis. 16. What’s Next for Next-Generation Sequencing (NGS)?.

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Book SynopsisData Science students and practitioners want to find a forecast that works and don't want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject.This book is an accessible guide that doesn't require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.Features:Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of thesTrade Review"A well-structured text aimed at undergraduates pursuing a data science curriculum, or MBA students. The authors draw upon their vast combined experience in research and teaching to a variety of audiences to present the classical material on ARMA-based Box-Jenkins methodology without assuming a calculus background. Yet, their approach manages to be heuristic, while not sacrificing relevant theoretical detail that enriches understanding. The authors complement this material with chapters on multivariate models, and, refreshingly, a very enlightening discussion on neural networks. The exposition is lucid, well-organized, and copiously illustrated to reinforce comprehension of concepts. The companion R package (tswge) finds a niche in the growing list of time series toolboxes, by providing clean, straightforward functionality on such essentials as spectrum reconstruction and model factor tables to glean the structure of AR and MA polynomials."- Alex Trindade, Texas Tech University Table of Contents1. Working with Data Collected Over Time, 2. Exploring Time Series Data, 3. Statistical Basics for Time Series Analysis, 4. The Frequency Domain, 5. ARMA Models, 6. ARMA Fitting and Forecasting, 7. ARIMA, Seasonal,and ARCH/GARCH Models, 8. Time Series Regression, 9. Model Assessment, 10. Multivariate Time Series, 11. Deep Neural Network Based Time Series Models

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Book SynopsisThis book is a step-by-step data story for analyzing ordinal data from start to finish. The book is for researchers, statisticians and scientists who are working with datasets where the response is ordinal. This type of data is common in many disciplines, not just in surveys (as is often thought). For example, in the biological sciences, there is an interest in understanding and predicting the (growth) stage (of a plant or animal) based on a multitude of factors. Likewise, ordinal data is common in environmental sciences (for example, stage of a storm), chemical sciences (for example, type of reaction), physical sciences (for example, stage of damage when force is applied), medical sciences (for example, degree of pain) and social sciences (for example, demographic factors like social status categorized in brackets). There has been no complete text about how to model an ordinal response as a function of multiple numerical and categorical predictors. There has always been a reluctanc

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Book SynopsisTable of Contents1. Principles of data analysis 2. Analysis facilities of commercial packages 3. Microsoft Excel 4. Visualisation 5. Statistical windows 6. Player tracking data 7. Matlab 8. Statistical analysis 9. Reliability

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Book SynopsisData Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems.Table of ContentsPreface. List of Contributors. SECTION I: INTRODUCTION - DATA DIVERSITY AND INTEGRATION. 1. Integrative Data Analysis and Visualization: Introduction to Critical Problems, Goals and Challenges (Francisco Azuaje and Joaquín Dopazo). 1.1 Data Analysis and Visualization: An Integrative Approach. 1.2 Critical Design and Implementation Factors. 1.3 Overview of Contributions. References. 2. Biological Databases: Infrastructure, Content and Integration (Allyson L. Williams, Paul J. Kersey, Manuela Pruess and Rolf Apweiler). 2.1 Introduction. 2.2 Data Integration. 2.3 Review of Molecular Biology Databases. 2.4 Conclusion. References. 3. Data and Predictive Model Integration: an Overview of Key Concepts, Problems and Solutions (Francisco Azuaje, Joaquín Dopazo and Haiying Wang). 3.1 Integrative Data Analysis and Visualization: Motivation and Approaches. 3.2 Integrating Informational Views and Complexity for Understanding Function. 3.3 Integrating Data Analysis Techniques for Supporting Functional Analysis. 3.4 Final Remarks. References. SECTION II: INTEGRATIVE DATA MINING AND VISUALIZATION -EMPHASIS ON COMBINATION OF MULTIPLE DATA TYPES. 4. Applications of Text Mining in Molecular Biology, from Name Recognition to Protein Interaction Maps (Martin Krallinger and Alfonso Valencia). 4.1 Introduction. 4.2 Introduction to Text Mining and NLP. 4.3 Databases and Resources for Biomedical Text Mining. 4.4 Text Mining and Protein-Protein Interactions. 4.5 Other Text-Mining Applications in Genomics. 4.6 The Future of NLP in Biomedicine. Acknowledgements. References. 5. Protein Interaction Prediction by Integrating Genomic Features and Protein Interaction Network Analysis (Long J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu, Falk Schubert and Mark Gerstein). 5.1 Introduction. 5.2 Genomic Features in Protein Interaction Predictions. 5.3 Machine Learning on Protein-Protein Interactions. 5.4 The Missing Value Problem. 5.5 Network Analysis of Protein Interactions. 5.6 Discussion. References. 6. Integration of Genomic and Phenotypic Data (Amanda Clare). 6.1 Phenotype. 6.2 Forward Genetics and QTL Analysis. 6.3 Reverse Genetics. 6.4 Prediction of Phenotype from Other Sources of Data. 6.5 Integrating Phenotype Data with Systems Biology. 6.6 Integration of Phenotype Data in Databases. 6.7 Conclusions. References. 7. Ontologies and Functional Genomics (Fátima Al-Shahrour and Joaquín Dopazo). 7.1 Information Mining in Genome-Wide Functional Analysis. 7.2 Sources of Information: Free Text Versus Curated Repositories. 7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics. 7.4 Using GO to Translate the Results of Functional Genomic Experiments into Biological Knowledge. 7.5 Statistical Approaches to Test Significant Biological Differences. 7.6 Using FatiGO to Find Significant Functional Associations in Clusters of Genes. 7.7 Other Tools. 7.8 Examples of Functional Analysis of Clusters of Genes. 7.9 Future Prospects. References. 8. The C. elegans Interactome: its Generation and Visualization (Alban Chesnau and Claude Sardet). 8.1 Introduction. 8.2 The ORFeome: the first step toward the interactome of C. elegans. 8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans Protein-Protein Interaction (Interactome) Network: Technical Aspects. 8.4 Visualization and Topology of Protein-Protein Interaction Networks. 8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale Genomics and Post-Genomics Data Sets. 8.6 Conclusion: From Interactions to Therapies. References. SECTION III: INTEGRATIVE DATA MINING AND VISUALIZATION - EMPHASIS ON COMBINATION OF MULTIPLE PREDICTION MODELS AND METHODS. 9. Integrated Approaches for Bioinformatic Data Analysis and Visualization - Challenges, Opportunities and New Solutions (Steve R. Pettifer, James R. Sinnott and Teresa K. Attwood). 9.1 Introduction. 9.2 Sequence Analysis Methods and Databases. 9.3 A View Through a Portal. 9.4 Problems with Monolithic Approaches: One Size Does Not Fit All. 9.5 A Toolkit View. 9.6 Challenges and Opportunities. 9.7 Extending the Desktop Metaphor. 9.8 Conclusions. Acknowledgements. References. 10. Advances in Cluster Analysis of Microarray Data (Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal and Bart De Moor). 10.1 Introduction. 10.2 Some Preliminaries. 10.3 Hierarchical Clustering. 10.4 k-Means Clustering. 10.5 Self-Organizing Maps. 10.6 A Wish List for Clustering Algorithms. 10.7 The Self-Organizing Tree Algorithm. 10.8 Quality-Based Clustering Algorithms. 10.9 Mixture Models. 10.10 Biclustering Algorithms. 10.11 Assessing Cluster Quality. 10.12 Open Horizons. References. 11. Unsupervised Machine Learning to Support Functional Characterization of Genes: Emphasis on Cluster Description and Class Discovery (Olga G. Troyanskaya). 11.1 Functional Genomics: Goals and Data Sources. 11.2 Functional Annotation by Unsupervised Analysis of Gene Expression Microarray Data. 11.3 Integration of Diverse Functional Data For Accurate Gene Function Prediction. 11.4 MAGIC - General Probabilistic Integration of Diverse Genomic Data. 11.5 Conclusion. References. 12. Supervised Methods with Genomic Data: a Review and Cautionary View (Ramón Díaz-Uriarte). 12.1 Chapter Objectives. 12.2 Class Prediction and Class Comparison. 12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes. 12.4 Class Prediction and Prognostic Prediction. 12.5 ROC Curves for Evaluating Predictors and Differential Expression. 12.6 Caveats and Admonitions. 12.7 Final Note: Source Code Should be Available. Acknowledgements. References. 13. A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models (Pedro Larrañaga, Iñaki Inza and Jose L. Flores). 13.1 Introduction. 13.2 Genetic Networks. 13.3 Probabilistic Graphical Models. 13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models. 13.5 Conclusions. Acknowledgements. References. 14. Integrative Models for the Prediction and Understanding of Protein Structure Patterns (Inge Jonassen). 14.1 Introduction. 14.2 Structure Prediction. 14.3 Classifications of Structures. 14.4 Comparing Protein Structures 14.5 Methods for the Discovery of Structure Motifs. 14.6 Discussion and Conclusions. References. Index.

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Book SynopsisStatistical complex survey analysis is a means to analyse the results, and gain information about a large population based on a complex survey of a sample of that population. A complex survey is a sample survey that divides the population into subgroups and collecting information from clusters within each subgroup and combining the results.Trade Review"As in the previous edition, this book is a good resource for practitioners and cross-disciplinary researchers who use data from complex survey designs." (Journal of the American Statistical Association, March 2006) "The first edition of the book was one of the first books in the excellent Wiley U.K. series on Statistics in Practice." (Technometrics, May 2005)Table of ContentsPreface. 1. Introduction. 2. Basic Sampling Techniques. 2.1 Basic definitions. 2.2 The Province’91 population. 2.3 Simple random sampling and design effect. 2.4 Systematic sampling and intra-class correlation. 2.5 Selection with probability proportional to size. 3. Further Use of Auxiliary Information. 3.1 Stratified sampling. 3.2 Cluster sampling. 3.3 Model-assisted estimation. 3.4 Efficiency comparison using design effects. 4. Handling Nonsampling Errors. 4.1 Reweighting. 4.2 Imputation. 4.3 Chapter summary and further reading. 5. Linearization and Sample Reuse in Variance Estimation. 5.1 The Mini-Finland Health Survey. 5.2 Ratio estimators. 5.3 Linearization method. 5.4 Sample reuse methods. 5.5 Comparison of variance estimators. 5.6 The Occupational Health C are Survey. 5.7 Linearization method for covariance-matrix estimation. 5.8 Chapter summary and further reading. 6. Model-assisted Estimation for Domains. 6.1 Framework for domain estimation. 6.2 Estimator type and model choice. 6.3 Construction of estimators and model specification. 6.4 Further comparison of estimators. 6.5 Chapter summary and further reading. 7. Analysis of One-way and Two-way Tables. 7.1 Introductory example. 7.2 Simple goodness-of-fit test. 7.3 Preliminaries for tests for two-way tables. 7.4 Test of homogeneity. 7.5 Test of independence. 7.6 Chapter summary and further reading. 8. Multivariate Survey Analysis. 8.1 Range of methods. 8.2 Types of models and options for analysis. 8.3 Analysis of categorical data. 8.4 Logistic and linear regression. 8.5 Chapter summary and further reading. 9. More Detailed Case Studies. 9.1 Monitoring quality in a long-term transport survey. 9.2 Estimation of mean salary in a business survey. 9.3 Model selection in a socioeconomic survey. 9.4 Multi-level modelling in an educational survey. References. Author Index. Subject Index. Web Extension. In addition to the printed book, electronic materials supporting the use of the book can be found in the web extension.

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