Python Data Science Handbook

Plant remains can preserve a critical part of history of life on Earth. While telling the fascinating evolutionary story of plants and vegetation across the last 500 million years, this book also crucially offers non-specialists a practical guide to studying, dealing with and interpreting plant fossils. It shows how various techniques can be used to reveal the secrets of plant fossils and how to identify common types, such as compressions and impressions. Incorporating the concepts of evolutionary floras, this second edition includes revised data on all main plant groups, the latest approaches to naming plant fossils using fossil-taxa and techniques such as tomography. With extensive illustrations of plant fossils and living plants, the book encourages readers to think of fossils as once-living organisms. It is written for students on introductory or intermediate courses in palaeobotany, palaeontology, plant evolutionary biology and plant science, and for amateurs interested in studyin

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

Real-world data sets are messy and complicated. Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis. Offering detailed, step-by-step instructions, it covers collection of many different types of data including web files, APIs, and maps; data cleaning; data formatting; the integration of different sources into a comprehensive data set; and storage using third-party tools to facilitate access and shareability, from Google Docs to GitHub. Assuming no prior knowledge of R and Python, the author introduces programming concepts gradually, using real data sets that provide the reader with practical, functional experience.

Real-world data sets are messy and complicated. Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis. Offering detailed, step-by-step instructions, it covers collection of many different types of data including web files, APIs, and maps; data cleaning; data formatting; the integration of different sources into a comprehensive data set; and storage using third-party tools to facilitate access and shareability, from Google Docs to GitHub. Assuming no prior knowledge of R and Python, the author introduces programming concepts gradually, using real data sets that provide the reader with practical, functional experience.

Presenting a global and interdisciplinary approach to plant ecology, this much-awaited new edition of the book Plants and Vegetation integrates classical themes with the latest ideas, models, and data. Keddy draws on extensive teaching experience to bring the field to life, guiding students through essential concepts with numerous real-world examples and full-colour illustrations throughout. The chapters begin by presenting the wider picture of the origin of plants and their impact on the Earth, before exploring the search for global patterns in plants and vegetation. Chapters on resources, stress, competition, herbivory, and mutualism explore causation, and a concluding chapter on conservation addresses the concern that one-third of all plant species are at risk of extinction. The scope of this edition is broadened further by a new chapter on population ecology, along with extensive examples including South African deserts, the Guyana Highlands of South America, Himalayan forests and

Public perceptions of political ethics are at the heart of current political debate. Drawing on original data, this book is the first general account of popular understandings of political ethics in contemporary British politics. It offers new insights into how citizens understand political ethics and integrity and how they form judgments of their leaders. By locating these insights against the backdrop of contemporary British political ethics, the book shows how current institutional preoccupations with standards of conduct all too often miss the mark. While the use of official resources is the primary focus of much regulation, politicians' consistency, frankness and sincerity, which citizens tend to see in terms of right and wrong, are treated as 'normal politics'. The authors suggest that new approaches may need to be adopted if public confidence in politicians' integrity is to be restored.

Is the science of moral cognition usefully modelled on aspects of Universal Grammar? Are human beings born with an innate 'moral grammar' that causes them to analyse human action in terms of its moral structure, with just as little awareness as they analyse human speech in terms of its grammatical structure? Questions like these have been at the forefront of moral psychology ever since John Mikhail revived them in his influential work on the linguistic analogy and its implications for jurisprudence and moral theory. In this seminal book, Mikhail offers a careful and sustained analysis of the moral grammar hypothesis, showing how some of John Rawls' original ideas about the linguistic analogy, together with famous thought experiments like the trolley problem, can be used to improve our understanding of moral and legal judgement.

Scientists have a choice concerning what role they should play in political debates and policy formation, particularly in terms of how they present their research. This book is about understanding this choice, what considerations are important to think about when deciding, and the consequences of such choices for the individual scientist and the broader scientific enterprise. Rather than prescribing what course of action each scientist ought to take, the book aims to identify a range of options for individual scientists to consider in making their own judgments about how they would like to position themselves in relation to policy and politics. Using examples from a range of scientific controversies and thought-provoking analogies from other walks of life, The Honest Broker challenges us all - scientists, politicians and citizens - to think carefully about how best science can contribute to policy-making and a healthy democracy.

Formal Models of Domestic Politics offers a unified and accessible approach to canonical and important new models of politics. Intended for political science and economics students who have already taken a course in game theory, this new edition retains the widely appreciated pedagogic approach of the first edition. Coverage has been expanded to include a new chapter on nondemocracy; new material on valance and issue ownership, dynamic veto and legislative bargaining, delegation to leaders by imperfectly informed politicians, and voter competence; and numerous additional exercises. Political economists, comparativists, and Americanists will all find models in the text central to their research interests. This leading graduate textbook assumes no mathematical knowledge beyond basic calculus, with an emphasis placed on clarity of presentation. Political scientists will appreciate the simplification of economic environments to focus on the political logic of models; economists will discov

'Chemical engineering is the field of applied science that employs physical, chemical, and biological rate processes for the betterment of humanity'. This opening sentence of Chapter 1 has been the underlying paradigm of chemical engineering. Chemical Engineering: An Introduction is designed to enable the student to explore the activities in which a modern chemical engineer is involved by focusing on mass and energy balances in liquid-phase processes. Problems explored include the design of a feedback level controller, membrane separation, hemodialysis, optimal design of a process with chemical reaction and separation, washout in a bioreactor, kinetic and mass transfer limits in a two-phase reactor, and the use of the membrane reactor to overcome equilibrium limits on conversion. Mathematics is employed as a language at the most elementary level. Professor Morton M. Denn incorporates design meaningfully; the design and analysis problems are realistic in format and scope.

If you accept evolutionary theory, can you also believe in God? Are human beings superior to other animals, or is this just a human prejudice? Does Darwin have implications for heated issues like euthanasia and animal rights? Does evolution tell us the purpose of life, or does it imply that life has no ultimate purpose? Does evolution tell us what is morally right and wrong, or does it imply that ultimately 'nothing' is right or wrong? In this fascinating and intriguing book, Steve Stewart-Williams addresses these and other fundamental philosophical questions raised by evolutionary theory and the exciting new field of evolutionary psychology. Drawing on biology, psychology and philosophy, he argues that Darwinian science supports a view of a godless universe devoid of ultimate purpose or moral structure, but that we can still live a good life and a happy life within the confines of this view.

Mesoscale weather systems are responsible for numerous natural disasters, such as damaging winds, blizzards and flash flooding. A fundamental understanding of the underlying dynamics involved in these weather systems is essential in forecasting their occurrence. This 2007 book provides a systematic approach to this subject. The opening chapters introduce the basic equations governing mesoscale weather systems and their approximations. The subsequent chapters cover four major areas of mesoscale dynamics: wave dynamics, moist convection, front dynamics and mesoscale modelling. This is an ideal book on the subject for researchers in meteorology and atmospheric science. With over 100 problems, and password-protected solutions available to instructors at www.cambridge.org/9780521808750, this book could also serve as a textbook for graduate students. Modelling projects, providing hands-on practice for building simple models of stratified fluid flow from a one-dimensional advection equation,

Kant is the central figure of modern philosophy. He sought to rebuild philosophy from the ground up, and he succeeded in permanently changing its problems and methods. This revised edition of the Prolegomena, which is the best introduction to the theoretical side of his philosophy, presents his thought clearly by paying careful attention to his original language. Also included are selections from the Critique of Pure Reason, which fill out and explicate some of Kant's central arguments (including famous sections of the Schematism and Analogies), and in which Kant himself explains his special terminology. The first reviews of the Critique, to which Kant responded in the Prolegomena, are included in this revised edition. The volume is completed by a historical and philosophical introduction, explanatory notes, a chronology, and a guide to further reading.

Social media has put mass communication in the hands of normal people on an unprecedented scale, and has also given social scientists the tools necessary to listen to the voices of everyday people around the world. This book gives social scientists the skills necessary to leverage that opportunity, and transform social media's vast stream of information into social science data. The book combines the big data techniques of computer science with social science methodology. Intended as a text for advanced undergraduates, graduate students, and researchers in the social sciences, this book provides a methodological pathway for scholars who want to make use of this new and evolving source of data. It provides a framework for building one's own data collection and analysis infrastructure, a toolkit of content analysis, geographic analysis, and network analysis, and meditations on the ethical implications of social media data.

Social media has put mass communication in the hands of normal people on an unprecedented scale, and has also given social scientists the tools necessary to listen to the voices of everyday people around the world. This book gives social scientists the skills necessary to leverage that opportunity, and transform social media's vast stream of information into social science data. The book combines the big data techniques of computer science with social science methodology. Intended as a text for advanced undergraduates, graduate students, and researchers in the social sciences, this book provides a methodological pathway for scholars who want to make use of this new and evolving source of data. It provides a framework for building one's own data collection and analysis infrastructure, a toolkit of content analysis, geographic analysis, and network analysis, and meditations on the ethical implications of social media data.

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specif

This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendice

Learn to master basic programming tasks from scratch with real-life, scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-p

Specifically designed for linguists, this book provides an introduction to programming using Python for those with little to no experience of coding. Python is one of the most popular and widely-used programming languages as it's also available for free and runs on any operating system. All examples in the text involve language data and can be adapted or used directly for language research. The text focuses on key language-related issues: searching, text manipulation, text encoding and internet data, providing an excellent resource for language research. More experienced users of Python will also benefit from the advanced chapters on graphical user interfaces and functional programming.

Specifically designed for linguists, this book provides an introduction to programming using Python for those with little to no experience of coding. Python is one of the most popular and widely-used programming languages as it's also available for free and runs on any operating system. All examples in the text involve language data and can be adapted or used directly for language research. The text focuses on key language-related issues: searching, text manipulation, text encoding and internet data, providing an excellent resource for language research. More experienced users of Python will also benefit from the advanced chapters on graphical user interfaces and functional programming.

Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to quickly gain proficiency. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation, this textbook also discusses the use of IPython notebooks to build rich-media, shareable documents for scientific analysis. Including a final chapter introducing challenging topics such as floating-point precision and algorithm stability, and with extensive online resources to support advanced study, this textbook represents a targe

All social and policy researchers need to synthesize data into a visual representation. Producing good visualizations combines creativity and technique. This book teaches the techniques and basics to produce a variety of visualizations, allowing readers to communicate data and analyses in a creative and effective way. Visuals for tables, time series, maps, text, and networks are carefully explained and organized, showing how to choose the right plot for the type of data being analysed and displayed. Examples are drawn from public policy, public safety, education, political tweets, and public health. The presentation proceeds step by step, starting from the basics, in the programming languages R and Python so that readers learn the coding skills while simultaneously becoming familiar with the advantages and disadvantages of each visualization. No prior knowledge of either Python or R is required. Code for all the visualizations are available from the book's website.

All social and policy researchers need to synthesize data into a visual representation. Producing good visualizations combines creativity and technique. This book teaches the techniques and basics to produce a variety of visualizations, allowing readers to communicate data and analyses in a creative and effective way. Visuals for tables, time series, maps, text, and networks are carefully explained and organized, showing how to choose the right plot for the type of data being analysed and displayed. Examples are drawn from public policy, public safety, education, political tweets, and public health. The presentation proceeds step by step, starting from the basics, in the programming languages R and Python so that readers learn the coding skills while simultaneously becoming familiar with the advantages and disadvantages of each visualization. No prior knowledge of either Python or R is required. Code for all the visualizations are available from the book's website.

Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; found

Learn about the state-of-the-art at the interface between information theory and data science with this first unified treatment of the subject. Written by leading experts in a clear, tutorial style, and using consistent notation and definitions throughout, it shows how information-theoretic methods are being used in data acquisition, data representation, data analysis, and statistics and machine learning. Coverage is broad, with chapters on signal acquisition, data compression, compressive sensing, data communication, representation learning, emerging topics in statistics, and much more. Each chapter includes a topic overview, definition of the key problems, emerging and open problems, and an extensive reference list, allowing readers to develop in-depth knowledge and understanding. Providing a thorough survey of the current research area and cutting-edge trends, this is essential reading for graduate students and researchers working in information theory, signal processing, machine le

The Cambridge Handbook of International Prevention Science offers a comprehensive global overview on prevention science with the most up-to-date research from around the world. Over 100 scholars from 27 different countries (including Australia, Bhutan, Botswana, India, Israel, Mexico, Singapore, South Korea, Spain and Thailand) contributed to this volume, which covers a wide range of topics important to prevention science. It includes major sections on the foundations of prevention as well as examples of new initiatives in the field, detailing current prevention efforts across the five continents. A unique and innovative volume, The Cambridge Handbook of International Prevention Science is a valuable resource for established scholars, early professionals, students, practitioners and policy-makers.

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling techniques. This Handbook presents clearly explained modeling options for such situations, along with extensive example data analyses. The book explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling without relying on mathematical derivations. All data analyses are performed on real and publicly available data sets, which are revisited multiple times to show differing results using various modeling options. Common pitfalls, data issues, and interpretation of model results are also addressed. Programs in both R and SAS are made available for all results presented in the text so that readers can emulate and adapt analyses for

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling techniques. This Handbook presents clearly explained modeling options for such situations, along with extensive example data analyses. The book explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling without relying on mathematical derivations. All data analyses are performed on real and publicly available data sets, which are revisited multiple times to show differing results using various modeling options. Common pitfalls, data issues, and interpretation of model results are also addressed. Programs in both R and SAS are made available for all results presented in the text so that readers can emulate and adapt analyses for

This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.

The Cambridge Handbook of International Prevention Science offers a comprehensive global overview on prevention science with the most up-to-date research from around the world. Over 100 scholars from 27 different countries (including Australia, Bhutan, Botswana, India, Israel, Mexico, Singapore, South Korea, Spain and Thailand) contributed to this volume, which covers a wide range of topics important to prevention science. It includes major sections on the foundations of prevention as well as examples of new initiatives in the field, detailing current prevention efforts across the five continents. A unique and innovative volume, The Cambridge Handbook of International Prevention Science is a valuable resource for established scholars, early professionals, students, practitioners and policy-makers.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction

Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging brain function. Handbook of Functional MRI Data Analysis provides a comprehensive and practical introduction to the methods used for fMRI data analysis. Using minimal jargon, this book explains the concepts behind processing fMRI data, focusing on the techniques that are most commonly used in the field. This book provides background about the methods employed by common data analysis packages including FSL, SPM and AFNI. Some of the newest cutting-edge techniques, including pattern classification analysis, connectivity modeling and resting state network analysis, are also discussed. Readers of this book, whether newcomers to the field or experienced researchers, will obtain a deep and effective knowledge of how to employ fMRI analysis to ask scientific questions and become more sophisticated users of fMRI analysis software.

Laboratory experiments, survey experiments and field experiments occupy a central and growing place in the discipline of political science. The Cambridge Handbook of Experimental Political Science is the first text to provide a comprehensive overview of how experimental research is transforming the field. Some chapters explain and define core concepts in experimental design and analysis. Other chapters provide an intellectual history of the experimental movement. Throughout the book, leading scholars review groundbreaking research and explain, in personal terms, the growing influence of experimental political science. The Cambridge Handbook of Experimental Political Science provides a collection of insights that can be found nowhere else. Its topics are of interest not just to researchers who are conducting experiments today, but also to researchers who think that experiments can help them make new and important discoveries in political science and beyond.

Laboratory experiments, survey experiments and field experiments occupy a central and growing place in the discipline of political science. The Cambridge Handbook of Experimental Political Science is the first text to provide a comprehensive overview of how experimental research is transforming the field. Some chapters explain and define core concepts in experimental design and analysis. Other chapters provide an intellectual history of the experimental movement. Throughout the book, leading scholars review groundbreaking research and explain, in personal terms, the growing influence of experimental political science. The Cambridge Handbook of Experimental Political Science provides a collection of insights that can be found nowhere else. Its topics are of interest not just to researchers who are conducting experiments today, but also to researchers who think that experiments can help them make new and important discoveries in political science and beyond.

This handbook presents an indispensable compilation of fundamental facts and figures about the Earth. It brings together reliable physical, chemical, biological and historical data in a series of 145 easy to read tables, supplemented by maps, charts and colour plates. Eleven sections cover topics spanning the Earth's geosphere, hydrosphere, atmosphere and biosphere, with one section focusing on other bodies in the Solar System. Full references for the original data sources are provided to enable users to access further detail, and the appendix provides practical information on units and conversion factors. Compact and easy to use, this handy book provides a time-saving first point of reference for researchers, students and practitioners in the Earth and Environmental Sciences. It allows scientists easy access to basic information on topics outside their specialisation, and is also a convenient resource for non-scientists such as economists, policy makers and journalists.

This book illustrates a set of tools - story grammars, relational data models, and network models - that can be profitably used for the collection, organization, and analysis of narrative data in socio-historical research. A story grammar, or Subject-Action-Object and their modifiers, is the linguistic tool the author uses to structure narrative for the purpose of collecting event data. Relational database models make such complex data collection schemes practically feasible in a computer environment. Finally, network models are a statistical tool best suited to analyze this type of data. Driven by the metaphors of the journal (from … to) and the alchemy (words into numbers), the book leads the reader throughout a number of paths, from substantive to methodological issues, across time and disciplines: sociology, linguistics, literary criticism, history, statistics, computer science, philosophy, cognitive psychology, political science.

This book illustrates a set of tools - story grammars, relational data models, and network models - that can be profitably used for the collection, organization, and analysis of narrative data in socio-historical research. A story grammar, or Subject-Action-Object and their modifiers, is the linguistic tool the author uses to structure narrative for the purpose of collecting event data. Relational database models make such complex data collection schemes practically feasible in a computer environment. Finally, network models are a statistical tool best suited to analyze this type of data. Driven by the metaphors of the journal (from … to) and the alchemy (words into numbers), the book leads the reader throughout a number of paths, from substantive to methodological issues, across time and disciplines: sociology, linguistics, literary criticism, history, statistics, computer science, philosophy, cognitive psychology, political science.

The Romance languages and dialects constitute a treasure trove of linguistic data of profound interest and significance. Data from the Romance languages have contributed extensively to our current empirical and theoretical understanding of phonetics, phonology, morphology, syntax, semantics, pragmatics, sociolinguistics, and historical linguistics. Written by a team of world-renowned scholars, this Handbook explores what we can learn about linguistics from the study of Romance languages, and how the body of comparative and historical data taken from them can be applied to linguistic study. It also offers insights into the diatopic and diachronic variation exhibited by the Romance family of languages, of a kind unparalleled for any other Western languages. By asking what Romance languages can do for linguistics, this Handbook is essential reading for all linguists interested in the insights that a knowledge of the Romance evidence can provide for general issues in linguistic theory.

Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.

This collection explores the relevance of global trade law for data, big data and cross-border data flows. Contributing authors from different disciplines including law, economics and political science analyze developments at the World Trade Organization and in preferential trade venues by asking what future-oriented models for data governance are available and viable in the area of trade law and policy. The collection paints the broad picture of the interaction between digital technologies and trade regulation as well as provides in-depth analyses of critical to the data-driven economy issues, such as privacy and AI, and different countries' perspectives. This title is also available as Open Access on Cambridge Core.