The burgeoning field of data analysis is expanding at an incredible pace due to the proliferation of data collection in almost every area of science. The enormous data sets now routinely encountered in the sciences provide an incentive to develop mathematical techniques and computational algorithms that help synthesize, interpret and give meaning to the data in the context of its scientific setting. A specific aim of this book is to integrate standard scientific computing methods with data analysis. By doing so, it brings together, in a self-consistent fashion, the key ideas from: * statistics, * time-frequency analysis, and * low-dimensional reductions The blend of these ideas provides meaningful insight into the data sets one is faced with in every scientific subject today, including those generated from complex dynamical systems. This is a particularly exciting field and much of the final part of the book is driven by intuitive examples from it, showing how the three areas can be used in combination to give critical insight into the fundamental workings of various problems. Data-Driven Modeling and Scientific Computation is a survey of practical numerical solution techniques for ordinary and partial differential equations as well as algorithms for data manipulation and analysis. Emphasis is on the implementation of numerical schemes to practical problems in the engineering, biological and physical sciences. An accessible introductory-to-advanced text, this book fully integrates MATLAB and its versatile and high-level programming functionality, while bringing together computational and data skills for both undergraduate and graduate students in scientific computing.

Numerical Methods in Environmental Data Analysis introduces environmental scientists to the numerical methods available to help answer research questions through data analysis. One challenge in data analysis is misrepresentation of datasets that are relevant directly or indirectly to the research. This book illustrates new ways of screening dataset or images for maximum utilization, introducing environmental modeling, numerical methods, and computations techniques in data analysis. Throughout the book, the author includes case studies that provide guidance on how to translate research questions into appropriate models. Individuals working with data sets or images generated from environmental monitoring centers or satellites will find this book to be a concise guide for analyzing and interpreting their data.

Ethical Practice of Statistics and Data Science is intended to prepare people to fully assume their responsibilities to practice statistics and data science ethically. Aimed at early career professionals, practitioners, and mentors or supervisors of practitioners, the book supports the ethical practice of statistics and data science, with an emphasis on how to earn the designation of, and recognize, "the ethical practitioner". The book features 47 case studies, each mapped to the Data Science Ethics Checklist (DSEC); Data Ethics Framework (DEFW); the American Statistical Association (ASA) Ethical Guidelines for Statistical Practice; and the Association of Computing Machinery (ACM) Code of Ethics. It is necessary reading for students enrolled in any data intensive program, including undergraduate or graduate degrees in (bio-)statistics, business/analytics, or data science. Managers, leaders, supervisors, and mentors who lead data-intensive teams in government, industry, or academia would also benefit greatly from this book. This is a companion volume to Ethical Reasoning For A Data-Centered World, also published by Ethics International Press (2022). These are the first and only books to be based on, and to provide guidance to, the ASA and ACM Ethical Guidelines/Code of Ethics.

The American Statistical Association (ASA) and the Association of Computing Machinery (ACM) have longstanding ethical practice standards that are explicitly intended to be utilized by all who use statistical practices or computing, or both. Since statistics and computing are critical in any data-centered activity, these practice standards are essential to instruction in the uses of statistical practices or computing across disciplines. Ethical Reasoning for a Data-Centered World is aimed at any undergraduate or graduate students utilizing data. Whether the career goal is research, teaching, business, government, or a combination, this book presents a method for understanding and prioritizing ethical statistics, computing, and data science - featuring the ASA and ACM practice standards. To facilitate engagement, integration with prior learning, and authenticity, the material is organized around seven tasks: Planning/Designing; Data collection; Analysis; Interpretation; Reporting; Documenting; and Engaging in Team Work. This book is a companion volume to Ethical Practice of Statistics and Data Science, also published by Ethics International Press (2022). These are the first and only books to be based on, and to provide guidance to, the American Statistical Association (ASA) and Association of Computing Machinery (ACM) ethical guideline documents.

Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre.

The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality explores the way in which data continues to dominate budgets, along with the varying efforts made across a variety of business enablement projects, including applications, web and mobile computing, big data analytics, and traditional data integration. The book teaches readers how to use proven methods and accelerators to break through data obstacles to provide faster, higher quality delivery of mission critical programs. Drawing upon years of practical experience, and using numerous examples and an easy to understand playbook, Lowell Fryman, Gregory Lampshire, and Dan Meers discuss a simple, proven approach to the execution of multiple data oriented activities. In addition, they present a clear set of methods to provide reliable governance, controls, risk, and exposure management for enterprise data and the programs that rely upon it. In addition, they discuss a cost-effective approach to providing sustainable governance and quality outcomes that enhance project delivery, while also ensuring ongoing controls. Example activities, templates, outputs, resources, and roles are explored, along with different organizational models in common use today and the ways they can be mapped to leverage playbook data governance throughout the organization.

Research in the domains of learning analytics and educational data mining has prototyped an approach where methodologies from data science and machine learning are used to gain insights into the learning process by using large amounts of data. As many training and academic institutions are maturing in their data-driven decision making, useful, scalable, and interesting trends are emerging. Organizations can benefit from sharing information on those efforts. Applying Data Science and Learning Analytics Throughout a Learner's Lifespan examines novel and emerging applications of data science and sister disciplines for gaining insights from data to inform interventions into learners' journeys and interactions with academic institutions. Data is collected at various times and places throughout a learner's lifecycle, and the learners and the institution should benefit from the insights and knowledge gained from this data. Covering topics such as learning analytics dashboards, text network analysis, and employment recruitment, this book is an indispensable resource for educators, computer scientists, faculty of higher education, government officials, educational administration, students of higher education, pre-service teachers, business professionals, researchers, and academicians.

This book provides a comprehensive introduction by an extraordinary range of experts to the recent and rapidly developing field of learning analytics. Some of the finest current thinkers about ways to interpret and benefit from the increasing amount of evidence from learners' experiences have taken time to explain their methods, describe examples, and point out new underpinnings for the field. Together, they show how this new field has the potential to dramatically increase learner success through deeper understanding of the academic, social-emotional, motivational, identity and meta-cognitive context each learner uniquely brings. Learning analytics is much more than "analyzing learning data"-it is about deeply understanding what learning activities work well, for whom, and when. Learning Analytics in Education provides an essential framework, as well as guidance and examples, for a wide range of professionals interested in the future of learning. If you are already involved in learning analytics, or otherwise trying to use an increasing density of evidence to understand learners' progress, these leading thinkers in the field may give you new insights. If you are engaged in teaching at any level, or training future teachers/faculty for this new, increasingly technology-enhanced learning world, and want some sense of the potential opportunities (and pitfalls) of what technology can bring to your teaching and students, these forward-thinking leaders can spark your imagination. If you are involved in research around uses of technology, improving learning measurements, better ways to use evidence to improve learning, or in more deeply understanding human learning itself, you will find additional ideas and insights from some of the best thinkers in the field here. If you are involved in making administrative or policy decisions about learning, you will find new ideas (and dilemmas) coming your way from inevitable changes in how we design and deliver instruction, how we measure the outcomes, and how we provide feedback to students, teachers, developers, administrators, and policy-makers. For all these players, the trick will be to get the most out of all the new developments to efficiently and effectively improve learning performance, without getting distracted by "shiny" technologies that are disconnected from how human learning and development actually work.

The ""Handbook of Research on Electronic Surveys and Measurements"" is the comprehensive reference source for innovative knowledge on electronic surveys. This commanding handbook of research provides complete coverage of the challenges associated with the use of the Internet to develop online surveys, administer Web-based instruments, and conduct computer-mediated assessments. Many internationally renowned experts in the field of electronic surveys and measurements have contributed to this comprehensive publication, and each chapter contains multiple references to published works in the field. The ""Handbook of Research on Electronic Surveys and Measurements"" is the only work with cutting edge descriptions of the design, implementation, and use of electronic surveys, and also includes discussions on the challenges associated with online data collection and profiles of selected online measures. This combination of how-to information about online research coupled with profiles of specific measures makes it an indispensable reference for every library.

The Data Journalism Handbook: Towards a Critical Data Practice provides a rich and panoramic introduction to data journalism, combining both critical reflection and practical insight. It offers a diverse collection of perspectives on how data journalism is done around the world and the broader consequences of datafication in the news, serving as both a textbook and a sourcebook for this emerging field. With more than 50 chapters from leading researchers and practitioners of data journalism, it explores the work needed to render technologies and data productive for journalistic purposes. It also gives a "behind the scenes" look at the social lives of data sets, data infrastructures, and data stories in newsrooms, media organizations, start-ups, civil society organizations and beyond. The book includes sections on "doing issues with data," "assembling data," "working with data," "experiencing data," "investigating data, platforms and algorithms," "organizing data journalism," "learning data journalism together" and "situating data journalism."

Patterns can be any number of items that occur repeatedly, whether in the behaviour of animals, humans, traffic, or even in the appearance of a design. As technologies continue to advance, recognizing, mimicking, and responding to all types of patterns becomes more precise. Pattern Recognition and Classification in Time Series Data focuses on intelligent methods and techniques for recognizing and storing dynamic patterns. Emphasizing topics related to artificial intelligence, pattern management, and algorithm development, in addition to practical examples and applications, this publication is an essential reference source for graduate students, researchers, and professionals in a variety of computer-related disciplines.

Despite businesses often being based on creating desirable experiences, products and services for consumers, many fail to consider the end user in their planning and development processes. This book is here to change that. User experience research, also known as UX research, focuses on understanding user behaviours, needs and motivations through a range of observational techniques, task analysis and other methodologies. User Research is a practical guide that shows readers how to use the vast array of user research methods available. Written by one of the UK's leading UX research professionals, readers can benefit from in-depth knowledge that explores the fundamentals of user research. Covering all the key research methods including face-to-face user testing, card sorting, surveys, A/B testing and many more, the book gives expert insight into the nuances, advantages and disadvantages of each, while also providing guidance on how to interpret, analyze and share the data once it has been obtained. Now in its second edition, User Research provides a new chapter on research operations and infrastructure as well as new material on combining user research methodologies.

View the Table of Contents. Read Chapter 1.

"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.

This volume focuses on the ethics of internet and social networking research exploring the challenges faced by researchers making use of social media and big data in their research. The internet, the world wide web and social media - indeed all forms of online communications - are attractive fields of research across a range of disciplines. They offer opportunities for methodological initiatives and innovations in research and easily accessed, massive amounts of primary and secondary data sources. This collection examines the new challenges posed by data generated online, explores how researchers are addressing those ethical challenges, and provides rich case studies of ethical decision making in the digital age.

THE NEW BOOK FROM THE BESTSELLING AUTHOR OF EVERYBODY LIES 'Don't Trust Your Gut is a tour de force - an intoxicating blend of analysis, humor, and humanity' DANIEL H. PINK 'Seth Stephens-Davidowitz is an expert on data-driven thinking, and this engaging book is full of surprising, useful insights for using the information at your fingertips to make better decisions' ADAM GRANT Big decisions are hard. We might consult friends and family, read advice online or turn to self-help books for guidance, but in the end we usually just do what feels right. But what if our gut is wrong? As economist and former Google data scientist Seth Stephens-Davidowitz argues, our gut is actually not that reliable - and data can prove this. In Don't Trust Your Gut, he unearths the startling conclusions that the right data can teach us about who we are and what will make our lives better. Over the past decade, scholars have mined enormous datasets to find remarkable new approaches to life's biggest self-help puzzles, from the boring careers that produce the most wealth, to old-school, data-backed relationship advice. While we often think we know how to better ourselves, the numbers, it turns out, disagree. Telling fascinating stories through the latest big data research, Stephens-Davidowitz reveals just how wrong we really are when it comes to improving our lives, and offers a new way of tackling our most consequential choices.

Traditionally, research impact has been measured by counting citations, and citation-based indicators, such as impact factors. But in the last few years there has been increasing pressure on research and higher education institutions to move beyond citation metrics, and look instead at different forms of impact - at real world impact.Scholarly impact expert Elaine Lasda brings together a cast of innovative contributors from a variety of sectors to look at how impact is measured in ways that go beyond citations in peer-reviewed journal articles. With case studies from publishers, museums, scientific centers and government agencies, the contributors show how using a different mix of traditional bibliometrics, newer altmetrics, and other new measures can provide vital information to support the mission and vision of their organizations. For librarians and information professionals, it is becoming increasingly more important to be able to provide expertise on research impact, influence, productivity and prestige. This exciting new book shows readers how to clarify the importance and relevance of organizational research output, and therefore increase their professional value. With the growing sophistication of research impact analysis, the need for "impact metric literacy" is rising, and this book is a helpful tool for those looking to improve their understanding of research impact.

The invited authors of this edited volume have been prolific in the arena of Real Data Analysis (RDA) as it applies to the social and behavioral sciences, especially in the disciplines of education and psychology. Combined, this brain trust represents 3,247 articles in refereed journals, 127 books published, US $45.3 Million in extramural research funding, 34 teaching and 92 research awards, serve(d) as Editor/Assistant Editor/Editorial Board Member for 95 peer reviewed journals, and provide( d) ad hoc reviews for 362 journals. Their enormous footprint on real data analysis is showcased for professors, researchers, educators, administrators, and graduate students in the second text in the AERA/SIG ES Quantitative Methods series.

Classification and regression trees (CART) is one of the several contemporary statistical techniques with good promise for research in many academic fields. There are very few books on CART, especially on applied CART. This book, as a good practical primer with a focus on applications, introduces the relatively new statistical technique of CART as a powerful analytical tool. The easy-to-understand (non-technical) language and illustrative graphs (tables) as well as the use of the popular statistical software program (SPSS) appeal to readers without strong statistical background. This book helps readers understand the foundation, the operation, and the interpretation of CART analysis, thus becoming knowledgeable consumers and skillful users of CART. The chapter on advanced CART procedures not yet well-discussed in the literature allows readers to effectively seek further empowerment of their research designs by extending the analytical power of CART to a whole new level. This highly practical book is specifically written for academic researchers, data analysts, and graduate students in many disciplines such as economics, social sciences, medical sciences, and sport sciences who do not have strong statistical background but still strive to take full advantage of CART as a powerful analytical tool for research in their fields.

Large surveys are becoming increasingly available for public use, and researchers are often faced with the need to analyse complex survey data to address key scientific issues. For proper analysis it is also important to be aware of the different aspects of the design of complex surveys. Practical Methods for Design and Analysis of Complex Surveys features intermediate and advanced statistical techniques for use in designing and analysing complex surveys. This extensively updated edition features much new material, and detailed practical exercises with links to a Web site, helping instructors and enabling use for distance learning.
* Provides a comprehensive introduction to sampling and estimation in descriptive surveys, including design effect statistic and use of auxiliary data.
* Includes detailed coverage of complex survey analysis, including design-based ANOVA and logistic regression with GEE estimation.
* Contains much new material, including handling of non-sampling errors, and model-assisted estimation for domains.
* Features detailed real-li fe case studies, such as multilevel modeling in a multinational educational survey.
* Supported by a Web site containing software codes, real data sets, computerized exercises with solutions, and online training materials.

Practical Methods for Design and Analysis of Complex Surveys provides a useful practical resource for researchers and practitioners working in the planning, implementation or analysis of complex surveys and opinion polls, including business, educational, health, social, and socio-economic surveys and official statistics. In addition, the book is well suited for use on intermediate and advanced courses in survey sampling.

From the quality of the air we breathe to the national leaders we choose, data and statistics are a pervasive feature of daily life and daily news. But how do news, numbers and public opinion interact with each other - and with what impacts on society at large? Featuring an international roster of established and emerging scholars, this book is the first comprehensive collection of research into the little understood processes underpinning the uses/misuses of statistical information in journalism and their socio-psychological and political effects. Moving beyond the hype around "data journalism," News, Numbers and Public Opinion delves into a range of more latent, fundamental questions such as: * Is it true that most citizens and journalists do not have the necessary skills and resources to critically process and assess numbers? * How do/should journalists make sense of the increasingly data-driven world? * What strategies, formats and frames do journalists use to gather and represent different types of statistical data in their stories? * What are the socio-psychological and political effects of such data gathering and representation routines, formats and frames on the way people acquire knowledge and form attitudes? * What skills and resources do journalists and publics need to deal effectively with the influx of numbers into in daily work and life - and how can newsrooms and journalism schools meet that need? The book is a must-read for not only journalists, journalism and media scholars, statisticians and data scientists but also anybody interested in the interplay between journalism, statistics and society.

Business and medical professionals rely on large data sets to identify trends or other knowledge that can be gleaned from the collection of it. New technologies concentrate on data's management, but do not facilitate users' extraction of meaningful outcomes. Pattern and Data Analysis in Healthcare Settings investigates the approaches to shift computing from analysis on-demand to knowledge on-demand. By providing innovative tactics to apply data and pattern analysis, these practices are optimized into pragmatic sources of knowledge for healthcare professionals. This publication is an exhaustive source for policy makers, developers, business professionals, healthcare providers, and graduate students concerned with data retrieval and analysis.

The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD complements big data's immutable nature, and solves it with lazy evaluation, cacheable and type inference. It also addresses advanced topics in Spark, starting with the basics of Scala and the core Spark framework, and exploring Spark data frames, machine learning using Mllib, graph analytics using Graph X and real-time processing with Apache Kafka, AWS Kenisis, and Azure Event Hub. It then goes on to investigate Spark using PySpark and R. Focusing on the current big data stack, the book examines the interaction with current big data tools, with Spark being the core processing layer for all types of data. The book is intended for data engineers and scientists working on massive datasets and big data technologies in the cloud. In addition to industry professionals, it is helpful for aspiring data processing professionals and students working in big data processing and cloud computing environments.

THE NEW YORK TIMES BESTSELLER

AN ECONOMIST BOOK OF THE YEAR 2017

Insightful, surprising and with ground-breaking revelations about our society, Everybody Lies exposes the secrets embedded in our internet searches, with a foreword by bestselling author Steven Pinker

Everybody lies, to friends, lovers, doctors, pollsters - and to themselves. In Internet searches, however, people confess their secrets - about sexless marriages, mental health problems, even racist views. Seth Stephens-Davidowitz, an economist and former Google data scientist, shows that this could just be the most important dataset ever collected.

This huge database of secrets - unprecedented in human history - offers astonishing, even revolutionary, insights into humankind. Anxiety, for instance, does not increase after a terrorist attack. Crime levels drop when a violent film is released. And racist searches are no higher in Republican areas than in Democrat ones.

Stephens-Davidowitz reveals information we can use to change our culture, and the questions we're afraid to ask that might be essential to our health - both emotional and physical. Insightful, funny, and always surprising, Everybody Lies exposes the biases and secrets embedded deeply within us, at a time when things are harder to predict than ever.