Topological data analysis (TDA) has emerged recently as a viable tool for analyzing complex data, and the area has grown substantially both in its methodologies and applicability. Providing a computational and algorithmic foundation for techniques in TDA, this comprehensive, self-contained text introduces students and researchers in mathematics and computer science to the current state of the field. The book features a description of mathematical objects and constructs behind recent advances, the algorithms involved, computational considerations, as well as examples of topological structures or ideas that can be used in applications. It provides a thorough treatment of persistent homology together with various extensions - like zigzag persistence and multiparameter persistence - and their applications to different types of data, like point clouds, triangulations, or graph data. Other important topics covered include discrete Morse theory, the Mapper structure, optimal generating cycles, as well as recent advances in embedding TDA within machine learning frameworks.

This title should enable clinicians and managers of stroke services to judge their effectiveness and efficiency. It may be useful for auditing the National Service Frameworks for older people and stroke patients. The work provides generic tools for collecting and interpreting data and assessing cost effectiveness, that can be applied to any chronic desease. This tool is practical for both running stroke services and doing comparative research on any chronic disease.

In an age where customer opinion and feedback can have an immediate, major effect upon the success of a business or organization, marketers must have the ability to analyze unstructured data in everything from social media and internet reviews to customer surveys and phone logs. Practical Text Analytics is an essential daily reference resource, providing real-world guidance on the effective application of text analytics. The book presents the analysis process so that it is immediately understood by the marketing professionals who must use it, so they can apply proven concepts and methods correctly and with confidence. By decoding industry terminology and demonstrating practical application of data models once reserved for experts, Practical Text Analytics shows marketers how to frame the right questions, identify key themes and find hidden meaning from unstructured data. Readers will learn to develop powerful new marketing strategies to elevate customer experience, solidify brand value and elevate reputation. Online resources include self-test questions, chapter review Q&A and an Instructor's Manual with text sources and instructions.

Climate predictions - and the computer models behind them - play a key role in shaping public opinion and our response to the climate crisis. Some people interpret these predictions as 'prophecies of doom' and some others dismiss them as mere speculation, but the vast majority are only vaguely aware of the science behind them. This book gives a balanced view of the strengths and limitations of climate modeling. It covers historical developments, current challenges, and future trends in the field. The accessible discussion of climate modeling only requires a basic knowledge of science. Uncertainties in climate predictions and their implications for assessing climate risk are analyzed, as are the computational challenges faced by future models. The book concludes by highlighting the dangers of climate 'doomism', while also making clear the value of predictive models, and the severe and very real risks posed by anthropogenic climate change.

This book gives an up-to-date overview of methodological and data-analytical issues of cross-cultural studies. Written by leading experts in the field, it presents the most important tools for doing cross-cultural research and outlines design considerations, methods, and analytical techniques that can improve ecological validity and help researchers to avoid pitfalls in cross-cultural psychology. By focusing on the relevant research questions that can be tackled with particular methods, it provides practical guidance on how to translate conceptual questions into decisions on study design and statistical techniques. Featuring examples from cognitive and educational assessment, personality, health, and intercultural communication and management, and illustrating key techniques in feature boxes, this concise and accessible guide is essential reading for researchers, graduate students, and professionals who work with culture-comparative data.

This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It provides guidance on how to think about scientific questions, and leads readers through the history of the field and its fundamental connections to neuroscience. The author discusses many applications to beautiful problems in the natural sciences, in physics, chemistry, and biomedicine. Examples include the search for exotic particles and dark matter in experimental physics, the prediction of molecular properties and reaction outcomes in chemistry, and the prediction of protein structures and the diagnostic analysis of biomedical images in the natural sciences. The text is accompanied by a full set of exercises at different difficulty levels and encourages out-of-the-box thinking.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

A comprehensive guide to data analysis techniques for physical scientists, providing a valuable resource for advanced undergraduate and graduate students, as well as seasoned researchers. The book begins with an extensive discussion of the foundational concepts and methods of probability and statistics under both the frequentist and Bayesian interpretations of probability. It next presents basic concepts and techniques used for measurements of particle production cross-sections, correlation functions, and particle identification. Much attention is devoted to notions of statistical and systematic errors, beginning with intuitive discussions and progressively introducing the more formal concepts of confidence intervals, credible range, and hypothesis testing. The book also includes an in-depth discussion of the methods used to unfold or correct data for instrumental effects associated with measurement and process noise as well as particle and event losses, before ending with a presentation of elementary Monte Carlo techniques.

This is the first comprehensive overview of the 'science of science,' an emerging interdisciplinary field that relies on big data to unveil the reproducible patterns that govern individual scientific careers and the workings of science. It explores the roots of scientific impact, the role of productivity and creativity, when and what kind of collaborations are effective, the impact of failure and success in a scientific career, and what metrics can tell us about the fundamental workings of science. The book relies on data to draw actionable insights, which can be applied by individuals to further their career or decision makers to enhance the role of science in society. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists and graduate students, policymakers, and administrators with an interest in the wider scientific enterprise.

This is the first comprehensive overview of the 'science of science,' an emerging interdisciplinary field that relies on big data to unveil the reproducible patterns that govern individual scientific careers and the workings of science. It explores the roots of scientific impact, the role of productivity and creativity, when and what kind of collaborations are effective, the impact of failure and success in a scientific career, and what metrics can tell us about the fundamental workings of science. The book relies on data to draw actionable insights, which can be applied by individuals to further their career or decision makers to enhance the role of science in society. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists and graduate students, policymakers, and administrators with an interest in the wider scientific enterprise.

Big Data and methods for analyzing large data sets such as machine learning have in recent times deeply transformed scientific practice in many fields. However, an epistemological study of these novel tools is still largely lacking. After a conceptual analysis of the notion of data and a brief introduction into the methodological dichotomy between inductivism and hypothetico-deductivism, several controversial theses regarding big data approaches are discussed. These include, whether correlation replaces causation, whether the end of theory is in sight and whether big data approaches constitute entirely novel scientific methodology. In this Element, I defend an inductivist view of big data research and argue that the type of induction employed by the most successful big data algorithms is variational induction in the tradition of Mill's methods. Based on this insight, the before-mentioned epistemological issues can be systematically addressed.

What is Big Data, and why should you care? Big data knows where you've been and who your friends are. It knows what you like and what makes you angry. It can predict what you'll buy, where you'll be the victim of crime and when you'll have a heart attack. Big data knows you better than you know yourself, or so it claims. But how well do you know big data? You've probably seen the phrase in newspaper headlines, at work in a marketing meeting, or on a fitness-tracking gadget. But can you understand it without being a Silicon Valley nerd who writes computer programs for fun? Yes. Yes, you can. Timandra Harkness writes comedy, not computer code. The only programmes she makes are on the radio. If you can read a newspaper you can read this book. Starting with the basics - what IS data? And what makes it big? - Timandra takes you on a whirlwind tour of how people are using big data today: from science to smart cities, business to politics, self-quantification to the Internet of Things. Finally, she asks the big questions about where it's taking us; is it too big for its boots, or does it think too small? Are you a data point or a human being? Will this book be full of rhetorical questions? No. It also contains puns, asides, unlikely stories and engaging people, inspiring feats and thought-provoking dilemmas. Leaving you armed and ready to decide what you think about one of the decade's big ideas: big data.

In the global race to reach the end of AIDS, why is the world slipping off track? The answer has to do with stigma, money, and data. Global funding for AIDS response is declining. Tough choices must be made: some people will win and some will lose. Global aid agencies and governments use health data to make these choices. While aid agencies prioritize a shrinking list of countries, many governments deny that sex workers, men who have sex with men, drug users, and transgender people exist. Since no data is gathered about their needs, life-saving services are not funded, and the lack of data reinforces the denial. The Uncounted cracks open this and other data paradoxes through interviews with global health leaders and activists, ethnographic research, analysis of gaps in mathematical models, and the author's experience as an activist and senior official. It shows what is counted, what is not, and why empowering communities to gather their own data could be key to ending AIDS.

In the global race to reach the end of AIDS, why is the world slipping off track? The answer has to do with stigma, money, and data. Global funding for AIDS response is declining. Tough choices must be made: some people will win and some will lose. Global aid agencies and governments use health data to make these choices. While aid agencies prioritize a shrinking list of countries, many governments deny that sex workers, men who have sex with men, drug users, and transgender people exist. Since no data is gathered about their needs, life-saving services are not funded, and the lack of data reinforces the denial. The Uncounted cracks open this and other data paradoxes through interviews with global health leaders and activists, ethnographic research, analysis of gaps in mathematical models, and the author's experience as an activist and senior official. It shows what is counted, what is not, and why empowering communities to gather their own data could be key to ending AIDS.

This book presents an accessible introduction to data-driven storytelling. Resulting from unique discussions between data visualization researchers and data journalists, it offers an integrated definition of the topic, presents vivid examples and patterns for data storytelling, and calls out key challenges and new opportunities for researchers and practitioners.

More students study management and organization studies than ever, the number of business schools worldwide continues to rise, and more management research is being published in a greater number of journals than could have been imagined twenty years ago. Dennis Tourish looks beneath the surface of this progress to expose a field in crisis and in need of radical reform. He identifies the ways in which management research has lost its way, including a remoteness from the practical problems that managers and employees face, a failure to replicate key research findings, poor writing, endless obscure theorizing, and an increasing number of research papers being retracted for fraud and other forms of malpractice. Tourish suggests fundamental changes to remedy these issues, enabling management research to become more robust, more interesting and more valuable to society. A must read for academics, practising managers, university administrators and policy makers within higher education.

Throughout the world, voters lack access to information about politicians, government performance, and public services. Efforts to remedy these informational deficits are numerous. Yet do informational campaigns influence voter behavior and increase democratic accountability? Through the first project of the Metaketa Initiative, sponsored by the Evidence in Governance and Politics (EGAP) research network, this book aims to address this substantive question and at the same time introduce a new model for cumulative learning that increases coordination among otherwise independent researcher teams. It presents the overall results (using meta-analysis) from six independently conducted but coordinated field experimental studies, the results from each individual study, and the findings from a related evaluation of whether practitioners utilize this information as expected. It also discusses lessons learned from EGAP's efforts to coordinate field experiments, increase replication of theoretically important studies across contexts, and increase the external validity of field experimental research.

This study explores issues of biomass energy use in relation to household welfare and it assesses Ethiopia's future energy security with a focus on long-term model of the energy sector, and institutional arrangements required for decentralized energy initiatives. Data from Ethiopian rural households reveal negative welfare effects associated with traditional biomass energy utilization, while increases in the opportunity cost of fuelwood collection is associated negatively with allocation of labour to agriculture and fuelwood use. It appears that investment on integrated energy source diversification improves sustainability and resilience, but increases production cost. Innovations that improve alternative sources reduce production cost, improve energy security, and thus serve as an engine of economic growth.

Leading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI.

Geometric and topological inference deals with the retrieval of information about a geometric object using only a finite set of possibly noisy sample points. It has connections to manifold learning and provides the mathematical and algorithmic foundations of the rapidly evolving field of topological data analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this self-contained book covers key aspects of the field, from data representation and combinatorial questions to manifold reconstruction and persistent homology. It can serve as a textbook for graduate students or researchers in mathematics, computer science and engineering interested in a geometric approach to data science.

Geometric and topological inference deals with the retrieval of information about a geometric object using only a finite set of possibly noisy sample points. It has connections to manifold learning and provides the mathematical and algorithmic foundations of the rapidly evolving field of topological data analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this self-contained book covers key aspects of the field, from data representation and combinatorial questions to manifold reconstruction and persistent homology. It can serve as a textbook for graduate students or researchers in mathematics, computer science and engineering interested in a geometric approach to data science.

A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors--noted experts in the field--highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniques A unique illustrative example that extends throughout all the chapters Exercises at the end of each chapter and larger projects at the end of each of the text's two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms.

An extensively revised and expanded third edition of the successful textbook on analysis and visualization of social networks integrating theory, applications, and professional software for performing network analysis (Pajek). The main structural concepts and their applications in social research are introduced with exercises. Pajek software and datasets are available, so readers can learn network analysis through application and case studies. In the end readers will have the knowledge, skills, and tools to apply social network analysis across different disciplines. A fundamental redesign of the menu structure and the capability to analyze much larger networks required a new edition. This edition presents several new operations including community detection, generalized main paths searches, new network indices, advanced visualization approaches, and instructions for installing Pajek under MacOSX. This third edition is up-to-date with Pajek version 5 and it introduces PajekXXL for very large networks and Pajek3XL for huge networks.

An extensively revised and expanded third edition of the successful textbook on analysis and visualization of social networks integrating theory, applications, and professional software for performing network analysis (Pajek). The main structural concepts and their applications in social research are introduced with exercises. Pajek software and datasets are available, so readers can learn network analysis through application and case studies. In the end readers will have the knowledge, skills, and tools to apply social network analysis across different disciplines. A fundamental redesign of the menu structure and the capability to analyze much larger networks required a new edition. This edition presents several new operations including community detection, generalized main paths searches, new network indices, advanced visualization approaches, and instructions for installing Pajek under MacOSX. This third edition is up-to-date with Pajek version 5 and it introduces PajekXXL for very large networks and Pajek3XL for huge networks.

Recent advances in sabermetrics have made it possible to assess the exact contribution of each player to the success of failure of his team. Using the simple metric Wins Above Average-the number of wins that the 2016 Red Sox, for example, added to their total because they had Mookie Betters in right field instead of an average player (5)-David Kaiser leads us on a fascinating tour through the history of major league baseball from 1901 through 2016, analyzing all the greatest players and teams of the past and showing exactly why they enjoyed the success that they did. Along the way, he identifies the 15 or 20 greatest players of every generation, using simple metrics that allow him to compare the impact of players from Ty Cobb through Ted Williams to Willie Mays, Rickey Henderson and Barry Bonds, and pitchers from Christy Mathewson to Roger Clemens. The book also says a great deal about short- and long-term strategies for organizational success. Along the way, Kaiser takes on a good many tenets of diamond faith.. The importance of pitching, he argues, has been vastly exaggerated since the beginning of baseball time, and great pitching has almost never been the key to a dynasty. Many Hall of Fame pitchers and some hitters as well, he finds, have reached Cooperstown almost entirely on the backs of their teammates. Accurate metrics also reveal that a few over-qualified players are still awaiting selection to Cooperstown. Last but hardly least, Kaiser shows that baseball is threatened by an unprecedented shortage of great players, and challenges MLB to do something about it.