A comprehensive guide to automated statistical data cleaning The production of clean data is a complex and time-consuming process that requires both technical know-how and statistical expertise. Statistical Data Cleaning brings together a wide range of techniques for cleaning textual, numeric or categorical data. This book examines technical data cleaning methods relating to data representation and data structure. A prominent role is given to statistical data validation, data cleaning based on predefined restrictions, and data cleaning strategy. Key features: Focuses on the automation of data cleaning methods, including both theory and applications written in R. Enables the reader to design data cleaning processes for either one-off analytical purposes or for setting up production systems that clean data on a regular basis. Explores statistical techniques for solving issues such as incompleteness, contradictions and outliers, integration of data cleaning components and quality monitoring. Supported by an accompanying website featuring data and R code. This book enables data scientists and statistical analysts working with data to deepen their understanding of data cleaning as well as to upgrade their practical data cleaning skills. It can also be used as material for a course in data cleaning and analyses.

Whether you have some experience with Tableau software or are just getting started, this manual goes beyond the basics to help you build compelling, interactive data visualization applications. Author Ryan Sleeper, one of the worldâ??s most qualified Tableau consultants, complements his web posts and instructional videos with this guide to give you a firm understanding of how to use Tableau to find valuable insights in data. Over five sections, Sleeperâ??recognized as a Tableau Zen Master, Tableau Public Visualization of the Year author, and Tableau Iron Viz Championâ??provides visualization tips, tutorials, and strategies to help you avoid the pitfalls and take your Tableau knowledge to the next level. Practical Tableau sections include: Fundamentals: get started with Tableau from the beginning Chart types: use step-by-step tutorials to build a variety of charts in Tableau Tips and tricks: learn innovative uses of parameters, color theory, how to make your Tableau workbooks run efficiently, and more Framework: explore the INSIGHT framework, a proprietary process for building Tableau dashboards Storytelling: learn tangible tactics for storytelling with data, including specific and actionable tips you can implement immediately

This book provides a complete and modern guide to web scraping, using Python as the programming language, without glossing over important details or best practices. Written with a data science audience in mind, the book explores both scraping and the larger context of web technologies in which it operates, to ensure full understanding. The authors recommend web scraping as a powerful tool for any data scientist’s arsenal, as many data science projects start by obtaining an appropriate data set. Starting with a brief overview on scraping and real-life use cases, the authors explore the core concepts of HTTP, HTML, and CSS to provide a solid foundation. Along with a quick Python primer, they cover Selenium for JavaScript-heavy sites, and web crawling in detail. The book finishes with a recap of best practices and a collection of examples that bring together everything you've learned and illustrate various data science use cases. What You'll Learn Leverage well-established best practices and commonly-used Python packages Handle today's web, including JavaScript, cookies, and common web scraping mitigation techniques Understand the managerial and legal concerns regarding web scraping Who This Book is For A data science oriented audience that is probably already familiar with Python or another programming language or analytical toolkit (R, SAS, SPSS, etc). Students or instructors in university courses may also benefit. Readers unfamiliar with Python will appreciate a quick Python primer in chapter 1 to catch up with the basics and provide pointers to other guides as well.

Thanks to approaches such as continuous integration and continuous delivery, companies that once introduced new products every six months are now shipping software several times a day. Reaching the market quickly is vital today, but rapid updates are impractical unless they provide genuine customer value. With this ebook, you’ll learn how online controlled experiments can help you gain customer feedback quickly so you can maintain a speedy release cycle. Using examples from Google, LinkedIn, and other organizations, Adil Aijaz, Trevor Stuart, and Henry Jewkes from Split Software explain basic concepts and show you how to build a scalable experimentation platform for conducting full-stack, comprehensive, and continuous tests. You’ll learn practical tips on best practices and common pitfalls you’re likely to face along the way. This ebook is ideal for engineers, data scientists, and product managers. Build an experimentation platform that includes a robust targeting engine, a telemetry system, a statistics engine, and a management console Dive deep into types of metrics, as well as metric frameworks, including Google’s HEART framework and LinkedIn’s 3-tiered framework Learn best practices for an building experimentation platform, such as A/A testing, power measuring, and an optimal ramp strategy Understand common pitfalls: how users are assigned across variants and control, how data is interpreted, and how metrics impact is understood

An accessible text that explains fundamental concepts in business statistics that are often obscured by formulae and mathematical notation A Guide to Business Statistics offers a practical approach to statistics that covers the fundamental concepts in business and economics. The book maintains the level of rigor of a more conventional textbook in business statistics but uses a more stream­lined and intuitive approach. In short, A Guide to Business Statistics provides clarity to the typical statistics textbook cluttered with notation and formulae. The author—an expert in the field—offers concise and straightforward explanations to the core principles and techniques in business statistics. The concepts are intro­duced through examples, and the text is designed to be accessible to readers with a variety of backgrounds. To enhance learning, most of the mathematical formulae and notation appears in technical appendices at the end of each chapter. This important resource: • Offers a comprehensive guide to understanding business statistics targeting business and economics students and professionals • Introduces the concepts and techniques through concise and intuitive examples • Focuses on understanding by moving distracting formulae and mathematical notation to appendices • Offers intuition, insights, humor, and practical advice for students of business statistics • Features coverage of sampling techniques, descriptive statistics, probability, sampling distributions, confidence intervals, hypothesis tests, and regression Written for undergraduate business students, business and economics majors, teachers, and practitioners, A Guide to Business Statistics offers an accessible guide to the key concepts and fundamental principles in statistics. DAVID M. McEVOY, PhD, is an Associate Professor in the Economics Department at Appalachian State University in Boone NC. He has published over 20 peer-reviewed articles and is coeditor of two books. Dr. McEvoy is an award-winning educator who has taught undergraduate courses in business statistics for over 10 years. DAVID M. M c EVOY, P h D, is an Associate Professor in the Economics Department at Appalachian State University in Boone NC. He has published over 20 peer-reviewed articles and is coeditor of two books. Dr. McEvoy is an award-winning educator who has taught undergraduate courses in business statistics for over 10 years.An accessible text that explains fundamental concepts in business statistics that are often obscured by formulae and mathematical notation A Guide to Business Statistics offers a practical approach to statistics that covers the fundamental concepts in business and economics. The book maintains the level of rigor of a more conventional textbook in business statistics but uses a more streamlined and intuitive approach. In short, A Guide to Business Statistics provides clarity to the typical statistics textbook cluttered with notation and formulae. The author—an expert in the field—offers concise and straightforward explanations to the core principles and techniques in business statistics. The concepts are introduced through examples, and the text is designed to be accessible

Go beyond technique to master the difficult judgement calls of forecasting A variety of software can be used effectively to achieve accurate forecasting, but no software can replace the essential human component. You may be new to forecasting, or you may have mastered the statistical theory behind the software’s predictions, and even more advanced “power user” techniques for the software itself—but your forecasts will never reach peak accuracy unless you master the complex judgement calls that the software cannot make. Profit From Your Forecasting Software addresses the issues that arise regularly, and shows you how to make the correct decisions to get the most out of your software. Taking a non-mathematical approach to the various forecasting models, the discussion covers common everyday decisions such as model choice, forecast adjustment, product hierarchies, safety stock levels, model fit, testing, and much more. Clear explanations help you better understand seasonal indices, smoothing coefficients, mean absolute percentage error, and r-squared, and an exploration of psychological biases provides insight into the decision to override the software’s forecast. With a focus on choice, interpretation, and judgement, this book goes beyond the technical manuals to help you truly grasp the more intangible skills that lead to better accuracy. Explore the advantages and disadvantages of alternative forecasting methods in different situations Master the interpretation and evaluation of your software’s output Learn the subconscious biases that could affect your judgement toward intervention Find expert guidance on testing, planning, and configuration to help you get the most out of your software Relevant to sales forecasters, demand planners, and analysts across industries, Profit From Your Forecasting Software is the much sought-after “missing piece” in forecasting reference.

Supply chain professionals: master pioneering techniques for integrating demand and supply, and create demand forecasts that are far more accurate and useful! In Demand and Supply Integration, Dr. Mark Moon presents the specific design characteristics of a world-class demand forecasting management process, showing how to effectively integrate demand forecasting within a comprehensive Demand and Supply Integration (DSI) process. Writing for supply chain professionals in any business, government agency, or military procurement organization, Moon explains what DSI is, how it differs from approaches such as S&OP, and how to recognize the symptoms of failures to sufficiently integrate demand and supply. He outlines the key characteristics of successful DSI implementations, shows how to approach Demand Forecasting as a management process, and guides you through understanding, selecting, and applying the best available qualitative and quantitative forecasting techniques. You'll learn how to thoroughly reflect market intelligence in your forecasts; measure your forecasting performance; implement state-of-the-art demand forecasting systems; manage Demand Reviews, and much more.

If programming is magic then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. The expanded edition of this practical book not only introduces you web scraping, but also serves as a comprehensive guide to scraping almost every type of data from the modern web. Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server's response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you're likely to encounter. Parse complicated HTML pages Develop crawlers with the Scrapy framework Learn methods to store data you scrape Read and extract data from documents Clean and normalize badly formatted data Read and write natural languages Crawl through forms and logins Scrape JavaScript and crawl through APIs Use and write image-to-text software Avoid scraping traps and bot blockers Use scrapers to test your website

Learn advanced techniques and improve your audio visualization skills with Thinking Machine Audio Dreams (ThMAD). With this book, you can concentrate on advanced examples and usage patterns, including using shaders in a more profound way, and how to incorporate ThMAD into a tool chain using the professional sound server JACK. Advanced Audio Visualization Using ThMAD provides advanced techniques for generating graphics, improving performance, and providing readers with the skills needed to create more interesting visualizations. You will also learn professional setups with highly developed visual and aural art tool chains. What You'll Learn Use the ThMAD software for advanced setups in their personal and professional projects Gain a pragmatic introduction to using shaders Use JACK sound servers with ThMAD Control the timing ThMAD Work withadvanced configurations Who This Book Is For Artists and developers already familiar with ThMAD and looking to enhance their projects. In addition, readers primarily interested in using shaders or the Jack audio server for graphics generation can benefit from the book as well.

Explores mathematical statistics in its entirety—from the fundamentals to modern methods This book introduces readers to point estimation, confidence intervals, and statistical tests. Based on the general theory of linear models, it provides an in-depth overview of the following: analysis of variance (ANOVA) for models with fixed, random, and mixed effects; regression analysis is also first presented for linear models with fixed, random, and mixed effects before being expanded to nonlinear models; statistical multi-decision problems like statistical selection procedures (Bechhofer and Gupta) and sequential tests; and design of experiments from a mathematical-statistical point of view. Most analysis methods have been supplemented by formulae for minimal sample sizes. The chapters also contain exercises with hints for solutions. Translated from the successful German text, Mathematical Statistics requires knowledge of probability theory (combinatorics, probability distributions, functions and sequences of random variables), which is typically taught in the earlier semesters of scientific and mathematical study courses. It teaches readers all about statistical analysis and covers the design of experiments. The book also describes optimal allocation in the chapters on regression analysis. Additionally, it features a chapter devoted solely to experimental designs. Classroom-tested with exercises included Practice-oriented (taken from day-to-day statistical work of the authors) Includes further studies including design of experiments and sample sizing Presents and uses IBM SPSS Statistics 24 for practical calculations of data Mathematical Statistics is a recommended text for advanced students and practitioners of math, probability, and statistics.

Relational Research and Organisation Studies does not only present and discuss guidelines for practice at a onto-epistemological level but also presents and discusses concrete cases of research projects building on relational constructionist ideas. Furthermore, excerpts of data are presented and analyzed in order to explain the co-constructed processes of the inquiries more in detail.

To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. With this third edition of SAS for Forecasting Time Series, intermediate-to-advanced SAS users—such as statisticians, economists, and data scientists—can now match the most sophisticated forecasting methods to the most current SAS applications. Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. From the well-known ARIMA models to unobserved components, methods that span the range from simple to complex are discussed and illustrated. Many of the newer methods are variations on the basic ARIMA structures. Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods: ARIMA models Vector autoregressive models Exponential smoothing models Unobserved component and state-space models Seasonal adjustment Spectral analysis Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition: The ARIMA procedure The AUTOREG procedure The VARMAX procedure The ESM procedure The UCM and SSM procedures The X13 procedure The SPECTRA procedure SAS Forecast Studio Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs. This book is part of the SAS Press program.

Comprising six thematic sections, The Routledge Companion to Trust provides a comprehensive survey of trust research. With contributions from international experts, this volume examines the major topics and emerging areas within the field, including essays on the foundations, levels and theories of trust.

Python Web Scraping Cookbook is your comprehensive guide to building efficient and functional web scraping tools using Python. With practical recipes, you'll learn to overcome the challenges of dynamic content, captcha, and irregular web structures while deploying scalable solutions. What this Book will help me do Master the use of Python libraries like BeautifulSoup and Scrapy for scraping data. Perfect techniques for handling JavaScript-heavy sites using Selenium. Learn to overcome web scraping challenges, such as captchas and rate-limiting. Design scalable scraping pipelines with cloud deployment in AWS. Understand web data extraction techniques with XPath, CSS selectors, and more. Author(s) Michael Heydt is a seasoned software engineer and technical author with a focus on data engineering and cloud solutions. Having worked with Python extensively, he brings real-world insights into web scraping. His practical approach simplifies complex concepts. Who is it for? This book is perfect for Python developers and data enthusiasts keen to master web scraping techniques. If you're a programmer with insights into Python scripting and wish to scrape, analyze, and utilize web data efficiently, this book is for you.

A much-needed introduction to the field of discrete-valued time series, with a focus on count-data time series Time series analysis is an essential tool in a wide array of fields, including business, economics, computer science, epidemiology, finance, manufacturing and meteorology, to name just a few. Despite growing interest in discrete-valued time series—especially those arising from counting specific objects or events at specified times—most books on time series give short shrift to that increasingly important subject area. This book seeks to rectify that state of affairs by providing a much needed introduction to discrete-valued time series, with particular focus on count-data time series. The main focus of this book is on modeling. Throughout numerous examples are provided illustrating models currently used in discrete-valued time series applications. Statistical process control, including various control charts (such as cumulative sum control charts), and performance evaluation are treated at length. Classic approaches like ARMA models and the Box-Jenkins program are also featured with the basics of these approaches summarized in an Appendix. In addition, data examples, with all relevant R code, are available on a companion website. Provides a balanced presentation of theory and practice, exploring both categorical and integer-valued series Covers common models for time series of counts as well as for categorical time series, and works out their most important stochastic properties Addresses statistical approaches for analyzing discrete-valued time series and illustrates their implementation with numerous data examples Covers classical approaches such as ARMA models, Box-Jenkins program and how to generate functions Includes dataset examples with all necessary R code provided on a companion website An Introduction to Discrete-Valued Time Series is a valuable working resource for researchers and practitioners in a broad range of fields, including statistics, data science, machine learning, and engineering. It will also be of interest to postgraduate students in statistics, mathematics and economics.

This volume deals with two complementary topics. On one hand the book deals with the problem of determining the the probability distribution of a positive compound random variable, a problem which appears in the banking and insurance industries, in many areas of operational research and in reliability problems in the engineering sciences. On the other hand, the methodology proposed to solve such problems, which is based on an application of the maximum entropy method to invert the Laplace transform of the distributions, can be applied to many other problems. The book contains applications to a large variety of problems, including the problem of dependence of the sample data used to estimate empirically the Laplace transform of the random variable. Contents Introduction Frequency models Individual severity models Some detailed examples Some traditional approaches to the aggregation problem Laplace transforms and fractional moment problems The standard maximum entropy method Extensions of the method of maximum entropy Superresolution in maxentropic Laplace transform inversion Sample data dependence Disentangling frequencies and decompounding losses Computations using the maxentropic density Review of statistical procedures

Dive into the world of regression analysis with this hands-on guide that covers everything you need to know about building effective regression models in R. You'll learn both the theoretical foundations and how to apply them using practical examples and R code. By the end, you'll be equipped to interpret regression results and use them to make meaningful predictions. What this Book will help me do Master the fundamentals of regression analysis, from simple linear to logistic regression. Gain expertise in R programming for implementing regression models and analyzing results. Develop skills in handling missing data, feature engineering, and exploratory data analysis. Understand how to identify, prevent, and address overfitting and underfitting issues in modeling. Apply regression techniques in real-world applications, including classification problems and advanced methods like Bagging and Boosting. Author(s) Giuseppe Ciaburro is an experienced data scientist and author with a passion for making complex technical topics accessible. With expertise in R programming and regression analysis, he has worked extensively in statistical modeling and data exploration. Giuseppe's writing combines clear explanations of theory with hands-on examples, ideal for learners and practitioners alike. Who is it for? This book is perfect for aspiring data scientists and analysts eager to understand and apply regression analysis using R. It's suited for readers with a foundational knowledge of statistics and basic R programming experience. Whether you're delving into data science or aiming to strengthen existing skills, this book offers practical insights to reach your goals.

Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.

This book provides an updated, concise summary of forecasting air travel demand methodology. It looks at air travel demand forecasting research and outlines the intellectual landscape of demand forecasting. The book also discusses what to do when facing different forecasting problems.

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

Learn how to leverage IBM SPSS Modeler for your data mining and predictive analytics needs in this comprehensive guide. With step-by-step instructions, you'll acquire the skills to import, clean, analyze, and model your data using this robust platform. By the end, you'll be equipped to uncover patterns and trends, enabling data-driven decision-making confidently. What this Book will help me do Understand the fundamentals of data mining and the visual programming interface of IBM SPSS Modeler. Prepare, clean, and preprocess data effectively for analysis and modeling. Build robust predictive models such as decision trees using best practices. Evaluate the performance of your analytical models to ensure accuracy and reliability. Export resulting analyses to apply insights to real-world data projects. Author(s) Keith McCormick and Jesus Salcedo are accomplished professionals in data analytics and statistical modeling. With extensive experience in consulting and teaching, they have guided many in mastering IBM SPSS Modeler through both hands-on workshops and written material. Their approachable teaching style and commitment to clarity ensure accessibility for learners. Who is it for? This book is designed for beginner users of IBM SPSS Modeler who wish to gain practical and actionable skills in data analytics. If you're a data enthusiast looking to explore predictive analytics or a professional eager to discover the insights hidden in your organizational data, this book is for you. A basic understanding of data mining concepts is advantageous but not required. This resource will set any novice on the path toward expert-level comprehension and application.

Discover hidden relationships among the variables in your data, and learn how to exploit these relationships. This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program. The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work. What You'll Learn Use Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your data Discover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data Work with feature weighting as regularized energy-based learning to rank variables according to their predictive power when there is too little data for traditional methods See how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data Plot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high Who This Book Is For Anyone interested in discovering and exploiting relationships among variables. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.

D3.js in Action, Second Edition is completely revised and updated for D3 v4 and ES6. It's a practical tutorial for creating interactive graphics and data-driven applications using D3. About the Technology Visualizing complex data is hard. Visualizing complex data on the web is darn near impossible without D3.js. D3 is a JavaScript library that provides a simple but powerful data visualization API over HTML, CSS, and SVG. Start with a structure, dataset, or algorithm; mix in D3; and you can programmatically generate static, animated, or interactive images that scale to any screen or browser. It's easy, and after a little practice, you'll be blown away by how beautiful your results can be! About the Book D3.js in Action, Second Edition is a completely updated revision of Manning's bestselling guide to data visualization with D3. You'll explore dozens of real-world examples in full-color, including force and network diagrams, workflow illustrations, geospatial constructions, and more! Along the way, you'll pick up best practices for building interactive graphics, animations, and live data representations. You'll also step through a fully interactive application created with D3 and React. What's Inside Rich full-color diagrams and illustrations Updated for D3 v4 and ES6 Reusable layouts and components Geospatial data visualizations Mixed-mode rendering About the Reader Suitable for web developers with HTML, CSS, and JavaScript skills. No specialized data science skills required. About the Author Elijah Meeks is a senior data visualization engineer at Netflix. Quotes From basic to complex, this book gives you the tools to create beautiful data visualizations. - Claudio Rodriguez, Cox Media Group The best reference for one of the most useful DataViz tools. - Jonathan Rioux, TD Insurance From toy examples to techniques for real projects. Shows how all the pieces fit together. - Scott McKissock, USAID A clever way to immerse yourself in the D3.js world. - Felipe Vildoso Castillo, University of Chile

This book, "Learning D3.js 5 Mapping", guides developers through the process of creating dynamic and interactive data visualizations. With a focus on D3.js, you'll learn to harness the power of JavaScript to create maps and graphical objects that inform and engage. What this Book will help me do Gain expertise in working with SVG geometric shapes to design compelling graphics. Learn techniques to manage, process, and use geographic data effectively. Master adding interactivity to visual maps to provide an immersive user experience. Understand how to optimize and manipulate geoJSON files using topoJSON. Learn to create varied map types, such as hexbins and globes, using D3.js and Canvas. Author(s) None Newton and Oscar Villarreal, among others, collaborated to author this guide. They are experienced in front-end development and data visualization, bringing a practical and hands-on approach to learning through this book. Their backgrounds ensure the book addresses common challenges faced during implementation, offering thoughtful solutions. Who is it for? "Learning D3.js 5 Mapping" is perfect for web developers familiar with HTML, CSS, and JavaScript who want to expand their expertise in data visualization and mapping. If you're looking to incorporate interactive charts or maps into your web applications, this book will provide practical guidance and solid fundamentals. No prior experience with D3.js is necessary.

“An indispensable reference for all researchers interested in the measurement of social welfare. . .” —François Bourguignon, Emeritus Professor at Paris School of Economics, Former Chief Economist of the World Bank. “. . .a detailed, insightful, and pedagogical presentation of the theoretical grounds of multidimensional well-being, inequality, and poverty measurement. Any student, researcher, and practitioner interested in the multidimensional approach should begin their journey into such a fascinating theme with this wonderful book.” —François Maniquet, Professor, Catholic University of Louvain, Belgium. A Review of the Multidimensional Approaches to the Measurement of Welfare, Inequality, and Poverty Analyzing Multidimensional Well-Being: A Quantitative Approach offers a comprehensive approach to the measurement of well-being that includes characteristics such as income, health, literacy, and housing. The author presents a systematic comparison of the alternative approaches to the measurement of multidimensional welfare, inequality, poverty, and vulnerability. The text contains real-life applications of some multidimensional aggregations (most of which have been designed by international organizations such as the United Nations Development Program and the Organization for Economic Co-operation and Development) that help to judge the performance of a country in the various dimensions of well-being. The text offers an evaluation of how well a society is doing with respect to achievements of all the individuals in the dimensions considered and clearly investigates how achievements in the dimensions can be evaluated from different perspectives. The author includes a detailed scrutiny of alternative techniques for setting weights to individual dimensional metrics and offers an extensive analysis into both the descriptive and welfare theoretical approaches to the concerned multi-attribute measurement and related issues. This important resource: • Contains a synthesis of multidimensional welfare, inequality, poverty, and vulnerability analysis • Examines aggregations of achievement levels in the concerned dimensions of well-being from various standpoints • Shows how to measure poverty using panel data instead of restricting attention to a single period and when we have imprecise information on dimensional achievements • Argues that multidimensional analysis is intrinsically different from marginal distributions-based analysis Written for students, teachers, researchers, and scholars, Analyzing Multidimensional Well-Being: A Quantitative Approach puts the focus on various approaches to the measurementof the many aspects of well-being and quality of life. Satya R. Chakravarty is a Professor of Economics at the Indian Statistical Institute, Kolkata, India. He is an Editor of Social Choice and Welfare and a member of the Editorial Board of Journal of Economic Inequality.