As an emerging discipline, data science broadly means different things across different areas. Exploring the relationship of data science with statistics, a well-established and principled data-analytic discipline, this book provides insights about commonalities in approach, and differences in emphasis.Featuring chapters from established authors in both disciplines, the book also presents a number of applications and accompanying papers.

As a result of the COVID-19 pandemic, medical statistics and public health data have become staples of newsfeeds worldwide, with infection rates, deaths, case fatality and the mysterious R figure featuring regularly. However, we don't all have the statistical background needed to translate this information into knowledge. In this lively account, Stephen Senn explains these statistical phenomena and demonstrates how statistics is essential to making rational decisions about medical care. The second edition has been thoroughly updated to cover developments of the last two decades and includes a new chapter on medical statistical challenges of COVID-19, along with additional material on infectious disease modelling and representation of women in clinical trials. Senn entertains with anecdotes, puzzles and paradoxes, while tackling big themes including: clinical trials and the development of medicines, life tables, vaccines and their risks or lack of them, smoking and lung cancer, and even the power of prayer.

Effectively and ethically leveraging people data to deliver real business value is what sets the best HR leaders and teams apart. Excellence in People Analytics provides business and human resources leaders with everything they need to know about creating value from people analytics. Written by two leading experts in the field, this practical guide outlines how to create sustainable business value with people analytics and develop a data-driven culture in HR. Most importantly, it allows HR professionals and business executives to translate their data into tangible actions to improve business performance. while navigating the rapidly evolving world of work. Full of practical tools and advice assembled around the Insight222 Nine Dimensions in People Analytics (R) model, this book demonstrates how to use people data to increase profits, improve staff retention and workplace productivity as well as develop individual employee experience. Featuring case studies from leading companies including Microsoft, HSBC, Syngenta, Capital One, Novartis, Bosch, Uber, Santander Brasil and American Eagle Outfitters (R), Excellence in People Analytics is essential reading for all HR professionals needing to unlock the potential in their people data and gain competitive advantage

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

Effectively and ethically leveraging people data to deliver real business value is what sets the best HR leaders and teams apart. Excellence in People Analytics provides business and human resources leaders with everything they need to know about creating value from people analytics. Written by two leading experts in the field, this practical guide outlines how to create sustainable business value with people analytics and develop a data-driven culture in HR. Most importantly, it allows HR professionals and business executives to translate their data into tangible actions to improve business performance. while navigating the rapidly evolving world of work. Full of practical tools and advice assembled around the Insight222 Nine Dimensions in People Analytics (R) model, this book demonstrates how to use people data to increase profits, improve staff retention and workplace productivity as well as develop individual employee experience. Featuring case studies from leading companies including Microsoft, HSBC, Syngenta, Capital One, Novartis, Bosch, Uber, Santander Brasil and American Eagle Outfitters (R), Excellence in People Analytics is essential reading for all HR professionals needing to unlock the potential in their people data and gain competitive advantage

Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to large-scale data.

Data Analysis in Molecular Biology and Evolution introduces biologists to DAMBE, a proprietary, user-friendly computer program for molecular data analysis. The unique combination of this book and software will allow biologists not only to understand the rationale behind a variety of computational tools in molecular biology and evolution, but also to gain instant access to these tools for use in their laboratories. Data Analysis in Molecular Biology and Evolution serves as an excellent resource for advanced level undergraduates or graduates as well as for professionals working in the field.

1.1 Overview V ARIOWIN 2.2 is a collection of four Windows (TM) programs - Prevar2D, Vari02D with PCF, Model, and Grid Display - that are used for spatial data analysis and variogram modeling of irregularly spaced data in two dimensions. Prevar2D builds a pair comparison file (PCF), that is, a binary file containing pairs of data sorted in terms of increasing distance. Pair comparison files can be built from subsets in order to reduce memory requirements. Vari02D with PCF is used for spatial data analysis of 2D data. It uses an ASCII data file and a binary pair comparison file produced by Prevar2D. Features implemented in Vari02D with PCF include: * the possibility to characterize the spatial continuity of one variable or the joined spatial continuity of two variables, * variogram surfaces for identifying directions of anisotropies, * directional variograms calculated along any direction, * several measures of spatial continuity. Not only the variogram but also the standardized variogram, the covariance, the correlogram, and the madogram are used to measure spatial continuity. * h-scatterplots to assess the meaning of these measures, * the identification and localization of pairs of data adversely affecting the measure of spatial continuity. Once identified, these pairs can be masked from the calculation interactively. * variogram clouds for identifying pairs of data values having the most influence on the measure of spatial continuity. Those pairs can also be located on the sample map.

This book has won the CHOICE Outstanding Academic Title award 2014. A century of education and education reform along with the last three decades of high-stakes testing and accountability reveals a disturbing paradox: Education has a steadfast commitment to testing and grading despite decades of research, theory, and philosophy that reveal the corrosive consequences of both testing and grading within an education system designed to support human agency and democratic principles. This edited volume brings together a collection of essays that confronts the failure of testing and grading and then offers practical and detailed examinations of implementing at the macro and micro levels of education teaching and learning free of the weight of testing and grading. The book explores the historical failure of testing and grading; the theoretical and philosophical arguments against testing and grading; the negative influence of testing and grading on social justice, race, class, and gender; and the role of testing and grading in perpetuating a deficit perspective of children, learning, race, and class. The chapters fall under two broad sections: Part I: "Degrading Learning, Detesting Education: The Failure of High-Stake Accountability in Education" includes essays on the historical, theoretical, and philosophical arguments against testing and grading; Part II: "De-Grading and De-Testing in a Time of High-Stakes Education Reform" presents practical experiments in de-testing and de-grading classrooms for authentic learning experiences.

This textbook is a complete rewrite, and expansion of Hugh Rollinson's highly successful 1993 book Using Geochemical Data: Evaluation, Presentation, Interpretation. Rollinson and Pease's new book covers the explosion in geochemical thinking over the past three decades, as new instruments and techniques have come online. It provides a comprehensive overview of how modern geochemical data are used in the understanding of geological and petrological processes. It covers major element, trace element, and radiogenic and stable isotope geochemistry. It explains the potential of many geochemical techniques, provides examples of their application, and emphasizes how to interpret the resulting data. Additional topics covered include the critical statistical analysis of geochemical data, current geochemical techniques, effective display of geochemical data, and the application of data in problem solving and identifying petrogenetic processes within a geological context. It will be invaluable for all graduate students, researchers, and professionals using geochemical techniques.

The importance of data analytics is well known, but how can you get end users to engage with analytics and business intelligence (BI) when adoption of new technology can be frustratingly slow or may not happen at all? Avoid wasting time on dashboards and reports that no one uses with this practical guide to increasing analytics adoption by focusing on people and process, not technology. Pulling together agile, UX and change management principles, Delivering Data Analytics outlines a step-by-step, technology agnostic process designed to shift the organizational data culture and gain buy-in from users and stakeholders at every stage of the project. This book outlines how to succeed and build trust with stakeholders amid the politics, ambiguity and lack of engagement in business. With case studies, templates, checklists and scripts based on the author's considerable experience in analytics and data visualisation, this book covers the full cycle from requirements gathering and data assessment to training and launch. Ensure lasting adoption, trust and, most importantly, actionable business value with this roadmap to creating user-centric analytics projects.

Minds Alive explores the enduring role and intrinsic value of libraries, archives, and public institutions in the digital age. Featuring international contributors, this volume delves into libraries and archives as institutions and institutional partners, the professional responsibilities of librarians and archivists, and the ways in which librarians and archivists continue to respond to the networked age, digital culture, and digitization. The endless possibilities and robust importance of libraries and archives are at the heart of this optimistic collection. Topics include transformations in the networked digital age; Indigenous issues and challenges in custodianship, ownership, and access; the importance of the harmonization of memory institutions today; and the overarching significance of libraries and archives in the public sphere. Libraries and archives - at once public institutions providing both communal and private havens of discovery - are being repurposed and transformed in intercultural contexts. Only by keeping pace with users' changing needs can they continue to provide the richest resources for an informed citizenry.

Data has dramatically changed how our world works. Understanding and using data is now one of the most transferable and desirable skills out there - whether you're an entrepreneur wanting to boost your business, a job-seeker looking for that employable edge, or hoping to make the most of your current career. Learning how to work with data may seem intimidating or difficult - but don't worry, Confident Data Skills is here to help. This updated second edition takes you through the basics of data: from data mining and preparing and analyzing your data, to visualizing and communicating your insights, and now with exciting new content on neural networks and deep learning. Featuring in-depth international case studies from companies like Netflix, LinkedIn and Mike's Hard Lemonade Co., as well as easy-to understand language and inspiring advice and guidance, Confident Data Skills help you use your new-found data skills to give your career that cutting-edge boost. About the Confident series... From coding and web design to data, digital content and cyber security, the Confident books are the perfect beginner's resource for enhancing your professional life, whatever your career path..

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.

The approximation and the estimation of nonparametric functions by projections on an orthonormal basis of functions are useful in data analysis. This book presents series estimators defined by projections on bases of functions, they extend the estimators of densities to mixture models, deconvolution and inverse problems, to semi-parametric and nonparametric models for regressions, hazard functions and diffusions. They are estimated in the Hilbert spaces with respect to the distribution function of the regressors and their optimal rates of convergence are proved. Their mean square errors depend on the size of the basis which is consistently estimated by cross-validation. Wavelets estimators are defined and studied in the same models.The choice of the basis, with suitable parametrizations, and their estimation improve the existing methods and leads to applications to a wide class of models. The rates of convergence of the series estimators are the best among all nonparametric estimators with a great improvement in multidimensional models. Original methods are developed for the estimation in deconvolution and inverse problems. The asymptotic properties of test statistics based on the estimators are also established.

This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapter topics include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The book is augmented by a wealth of online material.

"Modeling with Data" fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.

Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date.

"Modeling with Data" will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

Distribution-free resampling methods-permutation tests, decision trees, and the bootstrap-are used today in virtually every research area. A Practitioner's Guide to Resampling for Data Analysis, Data Mining, and Modeling explains how to use the bootstrap to estimate the precision of sample-based estimates and to determine sample size, data permutations to test hypotheses, and the readily-interpreted decision tree to replace arcane regression methods. Highlights Each chapter contains dozens of thought provoking questions, along with applicable R and Stata code Methods are illustrated with examples from agriculture, audits, bird migration, clinical trials, epidemiology, image processing, immunology, medicine, microarrays and gene selection Lists of commercially available software for the bootstrap, decision trees, and permutation tests are incorporated in the text Access to APL, MATLAB, and SC code for many of the routines is provided on the author's website The text covers estimation, two-sample and k-sample univariate, and multivariate comparisons of means and variances, sample size determination, categorical data, multiple hypotheses, and model building Statistics practitioners will find the methods described in the text easy to learn and to apply in a broad range of subject areas from A for Accounting, Agriculture, Anthropology, Aquatic science, Archaeology, Astronomy, and Atmospheric science to V for Virology and Vocational Guidance, and Z for Zoology. Practitioners and research workers and in the biomedical, engineering and social sciences, as well as advanced students in biology, business, dentistry, medicine, psychology, public health, sociology, and statistics will find an easily-grasped guide to estimation, testing hypotheses and model building.

The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you'll learn to take advantage of Spark's core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Unlike many Spark books written for data scientists, Spark in Action, Second Edition is designed for data engineers and software engineers who want to master data processing using Spark without having to learn a complex new ecosystem of languages and tools. You'll instead learn to apply your existing Java and SQL skills to take on practical, real-world challenges. Key Features * Lots of examples based in the Spark Java APIs using real-life dataset and scenarios * Examples based on Spark v2.3 Ingestion through files, databases, and streaming * Building custom ingestion process * Querying distributed datasets with Spark SQL For beginning to intermediate developers and data engineers comfortable programming in Java. No experience with functional programming, Scala, Spark, Hadoop, or big data is required. About the technology Spark is a powerful general-purpose analytics engine that can handle massive amounts of data distributed across clusters with thousands of servers. Optimized to run in memory, this impressive framework can process data up to 100x faster than most Hadoop-based systems. Author Bio An experienced consultant and entrepreneur passionate about all things data, Jean-Georges Perrin was the first IBM Champion in France, an honor he's now held for ten consecutive years. Jean-Georges has managed many teams of software and data engineers.

This book occupies a unique position in the field of statistical analysis in the behavioural and social sciences in that it targets learners who would benefit from learning more conceptually and less computationally about statistical procedures and the software packages that can be used to implement them. This book provides a comprehensive overview of this important research skill domain with an emphasis on visual support for learning and better understanding. The primary focus is on fundamental concepts, procedures and interpretations of statistical analyses within a single broad illustrative research context. The book covers a wide range of descriptive, correlational and inferential statistical procedures as well as more advanced procedures not typically covered in introductory and intermediate statistical texts. It is an ideal reference for postgraduate students as well as for researchers seeking to broaden their conceptual exposure to what is possible in statistical analysis.

Critical Theory and Qualitative Data Analysis in Education offers a path-breaking explanation of how critical theories can be used within the analysis of qualitative data to inform research processes, such as data collection, analysis, and interpretation. This contributed volume offers examples of qualitative data analysis techniques and exemplars of empirical studies that employ critical theory concepts in data analysis. By creating a clear and accessible bridge between data analysis and critical social theories, this book helps scholars and researchers effectively translate their research designs and findings to multiple audiences for more equitable outcomes and disruption of historical and contemporary inequality.

Learn how to analyse and manage evolutionary and sequential user behaviours in modern networks, and how to optimize network performance by using indirect reciprocity, evolutionary games, and sequential decision making. Understand the latest theory without the need to go through the details of traditional game theory. With practical management tools to regulate user behaviour, and simulations and experiments with real data sets, this is an ideal tool for graduate students and researchers working in networking, communications, and signal processing.

Classical probability theory and mathematical statistics appear sometimes too rigid for real life problems, especially while dealing with vague data or imprecise requirements. These problems have motivated many researchers to "soften" the classical theory. Some "softening" approaches utilize concepts and techniques developed in theories such as fuzzy sets theory, rough sets, possibility theory, theory of belief functions and imprecise probabilities, etc. Since interesting mathematical models and methods have been proposed in the frameworks of various theories, this text brings together experts representing different approaches used in soft probability, statistics and data analysis.

This book covers some recent developments in statistical inference. The author's main aim is to develop a theory of generalized p-values and generalized confidence intervals and to show how these concepts may be used to make exact statistical inferences in a variety of practical applications. In particular, they provide methods applicable in problems involving nuisance parameters such as those encountered in comparing two exponential distributions or in ANOVA without the assumption of equal error variances. The generalized procedures are shown to be more powerful in detecting significant experimental results and in avoiding misleading conclusions.

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"Auerbach and Silverstein write at a level that can be understood by beginners but is sophisticated enough for scholars...informative and interesting."
--"Choice"

Qualitative Data is meant for the novice researcher who needs guidance on what specifically to do when faced with a sea of information. It takes readers through the qualitative research process, beginning with an examination of the basic philosophy of qualitative research, and ending with planning and carrying out a qualitative research study. It provides an explicit, step-by-step procedure that will take the researcher from the raw text of interview data through data analysis and theory construction to the creation of a publishable work.

The volume provides actual examples based on the authors' own work, including two published pieces in the appendix, so that readers can follow examples for each step of the process, from the project's inception to its finished product. The volume also includes an appendix explaining how to implement these data analysis procedures using NVIVO, a qualitative data analysis program.