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Doing Data Science in R: An Introduction for Social Scientists
by Mark AndrewsThis approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis. Introducing the fundamentals of data science and R before moving into more advanced topics like Multilevel Models and Probabilistic Modelling with Stan, it builds knowledge and skills gradually. This book: Focuses on providing practical guidance for all aspects, helping readers get to grips with the tools, software, and statistical methods needed to provide the right type and level of analysis their data requires Explores the foundations of data science and breaks down the processes involved, focusing on the link between data science and practical social science skills Introduces R at the outset and includes extensive worked examples and R code every step of the way, ensuring students see the value of R and its connection to methods while providing hands-on practice in the software Provides examples and datasets from different disciplines and locations demonstrate the widespread relevance, possible applications, and impact of data science across the social sciences.
Doing Data Science in R: An Introduction for Social Scientists
by Mark AndrewsThis approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis. Introducing the fundamentals of data science and R before moving into more advanced topics like Multilevel Models and Probabilistic Modelling with Stan, it builds knowledge and skills gradually. This book: Focuses on providing practical guidance for all aspects, helping readers get to grips with the tools, software, and statistical methods needed to provide the right type and level of analysis their data requires Explores the foundations of data science and breaks down the processes involved, focusing on the link between data science and practical social science skills Introduces R at the outset and includes extensive worked examples and R code every step of the way, ensuring students see the value of R and its connection to methods while providing hands-on practice in the software Provides examples and datasets from different disciplines and locations demonstrate the widespread relevance, possible applications, and impact of data science across the social sciences.
Doing Data Science: Straight Talk from the Frontline
by Cathy O'Neil Rachel SchuttNow that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
Doing Math with Python: Use Programming to Explore Algebra, Statistics, Calculus, and More!
by Amit SahaDoing Math with Python shows you how to use Python to delve into high school–level math topics like statistics, geometry, probability, and calculus. You’ll start with simple projects, like a factoring program and a quadratic-equation solver, and then create more complex projects once you’ve gotten the hang of things.Along the way, you’ll discover new ways to explore math and gain valuable programming skills that you’ll use throughout your study of math and computer science. Learn how to:–Describe your data with statistics, and visualize it with line graphs, bar charts, and scatter plots–Explore set theory and probability with programs for coin flips, dicing, and other games of chance–Solve algebra problems using Python’s symbolic math functions–Draw geometric shapes and explore fractals like the Barnsley fern, the Sierpinski triangle, and the Mandelbrot set–Write programs to find derivatives and integrate functionsCreative coding challenges and applied examples help you see how you can put your new math and coding skills into practice. You’ll write an inequality solver, plot gravity’s effect on how far a bullet will travel, shuffle a deck of cards, estimate the area of a circle by throwing 100,000 "darts" at a board, explore the relationship between the Fibonacci sequence and the golden ratio, and more.Whether you’re interested in math but have yet to dip into programming or you’re a teacher looking to bring programming into the classroom, you’ll find that Python makes programming easy and practical. Let Python handle the grunt work while you focus on the math.Uses Python 3
Doing Meta-Analysis with R: A Hands-On Guide
by Mathias Harrer Pim Cuijpers Toshi A. Furukawa David D. EbertDoing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features• Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises• Describes statistical concepts clearly and concisely before applying them in R• Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book
Doing Physics with Scientific Notebook
by Joseph GallantThe goal of this book is to teach undergraduate students how to use Scientific Notebook (SNB) to solve physics problems. SNB software combines word processing and mathematics in standard notation with the power of symbolic computation. As its name implies, SNB can be used as a notebook in which students set up a math or science problem, write and solve equations, and analyze and discuss their results.Written by a physics teacher with over 20 years experience, this text includes topics that have educational value, fit within the typical physics curriculum, and show the benefits of using SNB.This easy-to-read text:Provides step-by-step instructions for using Scientific Notebook (SNB) to solve physics problems Features examples in almost every section to enhance the reader's understanding of the relevant physics and to provide detailed instructions on using SNB Follows the traditional physics curriculum, so it can be used to supplement teaching at all levels of undergraduate physics Includes many problems taken from the author's class notes and research Aimed at undergraduate physics and engineering students, this text teaches readers how to use SNB to solve some everyday physics problems.
Doing Qualitative Data Analysis with NVivo (Springer Texts in Social Sciences)
by Dimitri MortelmansThis open access textbook provides an introduction to the software program NVivo, the most widely used qualitative analysis program. It is a versatile program with an extensive range of accessible analysis tools, flexibly deployable in the diversity of qualitative analysis approaches. Qualitative analysis is almost standard practice today with the help of a software program. Yet there are many misunderstandings about qualitative software. They support the qualitative researcher but never take over their manual and theoretical work. An in-depth understanding of the possibilities of a qualitative software program helps to free up time for the analysis itself. The possibilities of NVivo in this book are approached from a researcher's perspective. That is precisely why gaining efficiency in using the software tools gets a prominent place in the chapters. The author examines basic skills, such as managing data, working with memos and coding qualitative data. This includes textual data (such as transcripts from interviews and focus groups) and audiovisual material (sound, video and images). The book also discusses more advanced analysis tools, such as case coding, queries, AI tools, matrices and models (maps). This textbook is intended for all users of NVivo, both early career researchers and more advanced analysts, who want to further discover the secrets of this software package along the way.
Doing Quantitative Research in Education with IBM SPSS Statistics
by Daniel MuijsThis essential guide for education students and researchers explains how to use quantitative methods for analysing educational data using IBM SPSS Statistics. By using datasets from real-life educational research, it demonstrates key statistical techniques that you will need to know, explaining how each procedure can by run on IBM SPSS Statistics. Datasets discussed in the book are downloadable, allowing you to hone your skills as you read. In this third edition, explanations have been updated with figures and screenshots from SPSS version 28, alongside a range of new research examples and updated further reading. Daniel Muijs is Dean of the Faculty of Education and Society at Academica University of Applied Sciences in Amsterdam.
Doing Quantitative Research in Education with IBM SPSS Statistics
by Daniel MuijsThis essential guide for education students and researchers explains how to use quantitative methods for analysing educational data using IBM SPSS Statistics. By using datasets from real-life educational research, it demonstrates key statistical techniques that you will need to know, explaining how each procedure can by run on IBM SPSS Statistics. Datasets discussed in the book are downloadable, allowing you to hone your skills as you read. In this third edition, explanations have been updated with figures and screenshots from SPSS version 28, alongside a range of new research examples and updated further reading. Daniel Muijs is Dean of the Faculty of Education and Society at Academica University of Applied Sciences in Amsterdam.
Doing Research: A New Researcher’s Guide (Research in Mathematics Education)
by James Hiebert Stephen Hwang Jinfa Cai Charles Hohensee Anne K MorrisThis book is about scientific inquiry. Designed for early and mid-career researchers, it is a practical manual for conducting and communicating high-quality research in (mathematics) education. Based on the authors’ extensive experience as researchers, as mentors, and as members of the editorial team for the Journal for Research in Mathematics Education (JRME), this book directly speaks to researchers and their communities about each phase of the process for conceptualizing, conducting, and communicating high-quality research in (mathematics) education.In the late 2010s, both JRME and Educational Studies in Mathematics celebrated 50 years of publishing high-quality research in mathematics education. Many advances in the field have occurred since the establishment of these journals, and these anniversaries marked a milestone in research in mathematics education. Indeed, fifty years represents a small step for human history but a giant leap for mathematics education. The educational research community in general (and the mathematics education community in particular) has strongly advocated for original research, placing great emphasis on building knowledge and capacity in the field. Because it is an interdisciplinary field, mathematics education has integrated means and methods for scientific inquiry from multiple disciplines. Now that the field is gaining maturity, it is a good time to take a step back and systematically consider how mathematics education researchers can engage in significant, impactful scientific inquiry.
Doing Secondary Analysis (Contemporary Social Research)
by Angela Dale Sara Arber Michael ProcterOriginally published in 1988 Doing Secondary Analysis is a practical guide to the secondary analysis of large-scale survey data.At a time when funding for primary data collection was increasingly constrained, the secondary analysis of high-quality government surveys offered the social scientist an unrivalled opportunity. This volume provided a guide which moves through every stage of ‘doing secondary analysis’. The authors begin with the conceptualization of the research problem and examine all the practicalities of using both standard rectangular data and hierarchical data, and of deriving simple and complex variables. They also provide a lucid description of the hardware and software available to the secondary analyst at the time.This book successfully demonstrates the way in which secondary analysis can contribute both to the development of sociological theory and to social policy formation. The authors emphasize throughout that secondary analysis cannot be used as a short cut to quick ‘results’, but that as much care over defining the research problem and understanding the categories of data is needed as for any other kind of research.While Doing Secondary Analysis was invaluable to those about to embark upon social research, it also offered many challenges to more experienced researchers.
Doing Simple Math in Your Head
by W. J. HowardAlmost all adults suffer a little math anxiety, especially when it comes to everyday problems they think they should be able to figure out in their heads. Want to figure the six percent sales tax on a $34.50 item? A 15 percent tip for a $13.75 check? The carpeting needed for a 12½-by-17-foot room? No one learns how to do these mental calculations in school, where the emphasis is on paper-and-pencil techniques. With no math background required and no long list of rules to memorize, this book teaches average adults how to simplify their math problems, provides ample real-life practice problems and solutions, and gives grown-ups the necessary background in basic arithmetic to handle everyday problems quickly.
Doing Statistical Analysis: A Student’s Guide to Quantitative Research
by Christer ThraneDoing Statistical Analysis looks at three kinds of statistical research questions – descriptive, associational, and inferential – and shows students how to conduct statistical analyses and interpret the results. Keeping equations to a minimum, it uses a conversational style and relatable examples such as football, COVID-19, and tourism, to aid understanding. Each chapter contains practice exercises, and a section showing students how to reproduce the statistical results in the book using Stata and SPSS. Digital supplements consist of data sets in Stata, SPSS, and Excel, and a test bank for instructors. Its accessible approach means this is the ideal textbook for undergraduate students across the social and behavioral sciences needing to build their confidence with statistical analysis.
Doing Transitions in the Life Course: Processes and Practices (Life Course Research and Social Policies #16)
by Barbara Stauber Andreas Walther Richard A. SetterstenThis open access book provides a unique research perspective on life course transitions. Here, transitions are understood as social processes and practices. Leveraging the recent “practice turn” in the social sciences, the contributors analyze how life course transitions are “done.” This book introduces the concept of “doing transitions” and its implications for theories and methods. It presents fresh empirical research on “doing transitions” in different life phases (e.g., childhood, young adulthood, later life) and life domains (e.g., education, work, family, health, migration). It also emphasizes themes related to institutions and organizations, time and normativity, materialities (such as bodies, spaces, and artifacts), and the reproduction of social inequalities in education and welfare. In coupling this new perspective with empirical illustrations, this book is an indispensable resource for scholars from demography, sociology, psychology, social work and other scientific fields, as well as for students, counselors and practitioners, and policymakers.
Domain Conditions and Social Rationality
by Satish Kumar JainThis book primarily focuses on the domain conditions under which a number of important classes of binary social decision rules give rise to rational social preferences. One implication of the Arrow and Gibbard theorems is that every non-oligarchic social decision rule that satisfies the condition of independence of irrelevant alternatives, a requirement crucial for the unambiguity of social choices, and the weak Pareto criterion fails to generate quasi-transitive social preferences for some configurations of individual preferences. The problem is exemplified by the famous voting paradox associated with the majority rule. Thus, in the context of rules that do not give rise to transitive (quasi-transitive) social preferences for every configuration of individual preferences, an important problem is that of formulating Inada-type necessary and sufficient conditions for transitivity (quasi-transitivity). This book formulates conditions for transitivity and quasi-transitivity for several classes of social decision rules, including majority rules, non-minority rules, Pareto-inclusive non-minority rules, and social decision rules that are simple games. It also analyzes in detail the conditions for transitivity and quasi-transitivity under the method of the majority decision, and derives the maximally sufficient conditions for transitivity under the class of neutral and monotonic binary social decision rules and one of its subclasses. The book also presents characterizations of some of the classes of rules for which domain conditions have been derived. The material covered is relevant to anyone interested in studying the structure of voting rules, particularly those interested in social choice theory. Providing the necessary social choice theoretic concepts, definitions, propositions and theorems, the book is essentially self-contained. The treatment throughout is rigorous, and unlike most of the literature on domain conditions, care is taken regarding the number of individuals in the 'necessity' proofs. As such it is an invaluable resource for students of economics and political science, with takeaways for everyone – from first-year postgraduates to more advanced doctoral students and scholars.
Domain Decomposition Methods in Science and Engineering XXII
by Thomas Dickopf Martin J. Gander Laurence Halpern Rolf Krause Luca F. PavarinoThese are the proceedings of the 22nd International Conference on Domain Decomposition Methods, which was held in Lugano, Switzerland. With 172 participants from over 24 countries, this conference continued a long-standing tradition of internationally oriented meetings on Domain Decomposition Methods. The book features a well-balanced mix of established and new topics, such as the manifold theory of Schwarz Methods, Isogeometric Analysis, Discontinuous Galerkin Methods, exploitation of modern HPC architectures and industrial applications. As the conference program reflects, the growing capabilities in terms of theory and available hardware allow increasingly complex non-linear and multi-physics simulations, confirming the tremendous potential and flexibility of the domain decomposition concept.
Domain Decomposition Methods in Science and Engineering XXIII
by Chang-Ock Lee Xiao-Chuan Cai David E. Keyes Hyea Hyun Kim Axel Klawonn Eun-Jae Park Olof B. WidlundThis book is a collection of papers presented at the 23rd International Conference on Domain Decomposition Methods in Science and Engineering, held on Jeju Island, Korea on July 6-10, 2015. Domain decomposition methods solve boundary value problems by splitting them into smaller boundary value problems on subdomains and iterating to coordinate the solution between adjacent subdomains. Domain decomposition methods have considerable potential for a parallelization of the finite element methods, and serve a basis for distributed, parallel computations.
Domain Decomposition Methods in Science and Engineering XXIV (Lecture Notes in Computational Science and Engineering #125)
by Susanne C. Brenner Hyea Hyun Kim Olof B. Widlund Petter E. Bjørstad Lawrence Halpern Ralf Kornhuber Talal RahmanThese are the proceedings of the 24th International Conference on Domain Decomposition Methods in Science and Engineering, which was held in Svalbard, Norway in February 2017. Domain decomposition methods are iterative methods for solving the often very large systems of equations that arise when engineering problems are discretized, frequently using finite elements or other modern techniques. These methods are specifically designed to make effective use of massively parallel, high-performance computing systems. The book presents both theoretical and computational advances in this domain, reflecting the state of art in 2017.
Domain Generalization with Machine Learning in the NOvA Experiment (Springer Theses)
by Andrew T.C. SuttonThis thesis presents significant advances in the use of neural networks to study the properties of neutrinos. Machine learning tools like neural networks (NN) can be used to identify the particle types or determine their energies in detectors such as those used in the NOvA neutrino experiment, which studies changes in a beam of neutrinos as it propagates approximately 800 km through the earth. NOvA relies heavily on simulations of the physics processes and the detector response; these simulations work well, but do not match the real experiment perfectly. Thus, neural networks trained on simulated datasets must include systematic uncertainties that account for possible imperfections in the simulation. This thesis presents the first application in HEP of adversarial domain generalization to a regression neural network. Applying domain generalization to problems with large systematic variations will reduce the impact of uncertainties while avoiding the risk of falsely constraining the phase space. Reducing the impact of systematic uncertainties makes NOvA analysis more robust, and improves the significance of experimental results.
Domain-Specific Knowledge Graph Construction (SpringerBriefs in Computer Science)
by Mayank KejriwalThe vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as knowledge graphs. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This book will synthesize Knowledge Graph Construction over Web Data in an engaging and accessible manner. The book will describe a timely topic for both early -and mid-career researchers. Every year, more papers continue to be published on knowledge graph construction, especially for difficult Web domains. This work would serve as a useful reference, as well as an accessible but rigorous overview of this body of work. The book will present interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. This will allow the book to be marketed in multiple venues and conferences. The book will also appeal to practitioners in industry and data scientists since it will have chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations. The author has, and continues to, present on this topic at large and important conferences. He plans to make the powerpoint he presents available as a supplement to the work. This will draw a natural audience for the book. Some of the reviewers are unsure about his position in the community but that seems to be more a function of his age rather than his relative expertise. I agree with some of the reviewers that the title is a little complicated. I would recommend “Domain Specific Knowledge Graphs”.
Domain-Specific Languages in R: Advanced Statistical Programming
by Thomas MailundGain an accelerated introduction to domain-specific languages in R, including coverage of regular expressions. This compact, in-depth book shows you how DSLs are programming languages specialized for a particular purpose, as opposed to general purpose programming languages. Along the way, you’ll learn to specify tasks you want to do in a precise way and achieve programming goals within a domain-specific context. Domain-Specific Languages in R includes examples of DSLs including large data sets or matrix multiplication; pattern matching DSLs for application in computer vision; and DSLs for continuous time Markov chains and their applications in data science. After reading and using this book, you’ll understand how to write DSLs in R and have skills you can extrapolate to other programming languages.What You'll LearnProgram with domain-specific languages using RDiscover the components of DSLsCarry out large matrix expressions and multiplications Implement metaprogramming with DSLsParse and manipulate expressions Who This Book Is ForThose with prior programming experience. R knowledge is helpful but not required.
Domesticating Youth: Youth Bulges and their Socio-political Implications in Tajikistan
by Sophie RocheMost of the Muslim societies of the world have entered a demographic transition from high to low fertility, and this process is accompanied by an increase in youth vis-à-vis other age groups. Political scientists and historians have debated whether such a "youth bulge" increases the potential for conflict or whether it represents a chance to accumulate wealth and push forward social and technological developments. This book introduces the discussion about youth bulge into social anthropology using Tajikistan, a post-Soviet country that experienced civil war in the 1990s, which is in the middle of such a demographic transition. Sophie Roche develops a social anthropological approach to analyze demographic and political dynamics, and suggests a new way of thinking about social change in youth bulge societies.
Domination Games Played on Graphs (SpringerBriefs in Mathematics)
by Michael A. Henning Sandi Klavžar Boštjan Brešar Douglas F. RallThis concise monograph present the complete history of the domination game and its variants up to the most recent developments and will stimulate research on closely related topics, establishing a key reference for future developments. The crux of the discussion surrounds new methods and ideas that were developed within the theory, led by the imagination strategy, the Continuation Principle, and the discharging method of Bujtás, to prove results about domination game invariants. A toolbox of proof techniques is provided for the reader to obtain results on the domination game and its variants. Powerful proof methods such as the imagination strategy are presented. The Continuation Principle is developed, which provides a much-used monotonicity property of the game domination number. In addition, the reader is exposed to the discharging method of Bujtás. The power of this method was shown by improving the known upper bound, in terms of a graph's order, on the (ordinary) domination number of graphs with minimum degree between 5 and 50. The book is intended primarily for students in graph theory as well as established graph theorists and it can be enjoyed by anyone with a modicum of mathematical maturity.The authors include exact results for several families of graphs, present what is known about the domination game played on subgraphs and trees, and provide the reader with the computational complexity aspects of domination games. Versions of the games which involve only the “slow” player yield the Grundy domination numbers, which connect the topic of the book with some concepts from linear algebra such as zero-forcing sets and minimum rank. More than a dozen other related games on graphs and hypergraphs are presented in the book. In all these games there are problems waiting to be solved, so the area is rich for further research. The domination game belongs to the growing family of competitive optimization graph games. The game is played by two competitors who take turns adding a vertex to a set of chosen vertices. They collaboratively produce a special structure in the underlying host graph, namely a dominating set. The two players have complementary goals: one seeks to minimize the size of the chosen set while the other player tries to make it as large as possible. The game is not one that is either won or lost. Instead, if both players employ an optimal strategy that is consistent with their goals, the cardinality of the chosen set is a graphical invariant, called the game domination number of the graph. To demonstrate that this is indeed a graphical invariant, the game tree of a domination game played on a graph is presented for the first time in the literature.
Domination in Graphs: Volume 2: Advanced Topics (Chapman And Hall/crc Pure And Applied Mathematics Ser. #209)
by TeresaW. Haynes""Presents the latest in graph domination by leading researchers from around the world-furnishing known results, open research problems, and proof techniques. Maintains standardized terminology and notation throughout for greater accessibility. Covers recent developments in domination in graphs and digraphs, dominating functions, combinatorial problems on chessboards, and more.
Don't Be Afraid of Physics: Quantum Mechanics, Relativity and Cosmology for Everyone
by Ross Barrett Pier Paolo DelsantoWith the aid of entertaining short stories, anecdotes, lucid explanations and straight-forward figures, this book challenges the perception that the world of physics is inaccessible to the non-expert. Beginning with Neanderthal man, it traces the evolution of human reason and understanding from paradoxes and optical illusions to gravitational waves, black holes and dark energy. On the way, it provides insights into the mind-boggling advances at the frontiers of physics and cosmology. Unsolved problems and contradictions are highlighted, and contentious issues in modern physics are discussed in a non-dogmatic way in a language comprehensible to the non-scientist. It has something for everyone.