Multidimensional Data Visualization

In the global race to reach the end of AIDS, why is the world slipping off track? The answer has to do with stigma, money, and data. Global funding for AIDS response is declining. Tough choices must be made: some people will win and some will lose. Global aid agencies and governments use health data to make these choices. While aid agencies prioritize a shrinking list of countries, many governments deny that sex workers, men who have sex with men, drug users, and transgender people exist. Since no data is gathered about their needs, life-saving services are not funded, and the lack of data reinforces the denial. The Uncounted cracks open this and other data paradoxes through interviews with global health leaders and activists, ethnographic research, analysis of gaps in mathematical models, and the author's experience as an activist and senior official. It shows what is counted, what is not, and why empowering communities to gather their own data could be key to ending AIDS.

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

In Digital Data Collection and Information Privacy Law, Mark Burdon argues for the reformulation of information privacy law to regulate new power consequences of ubiquitous data collection. Examining developing business models, based on collections of sensor data - with a focus on the 'smart home' - Burdon demonstrates the challenges that are arising for information privacy's control-model and its application of principled protections of personal information exchange. By reformulating information privacy's primary role of individual control as an interrupter of modulated power, Burdon provides a foundation for future law reform and calls for stronger information privacy law protections. This book should be read by anyone interested in the role of privacy in a world of ubiquitous and pervasive data collection.

Drawing on examples from across the social and behavioral sciences, Statistics with R: Solving Problems Using Real-World Data introduces foundational statistics concepts with beginner-friendly R progr

Qualitative Data Collection Tools is a new and unique supplementary text that will guide students and new researchers to design, develop, pilot, and employ qualitative tools in order to collect qualit

The SPSS Survival Manual throws a lifeline to students and researchers grappling with this powerful data analysis software. In her bestselling manual, Julie Pallant guides you through the entire research process, helping you choose the right data analysis technique for your project. From the formulation of research questions, to the design of the study and analysis of data, to reporting the results, Julie discusses basic through to advanced statistical techniques.She outlines each technique clearly, providing step by step procedures for performing your analysis, a detailed guide to interpreting data output and examples of how to present your results in a report. For both beginners and experienced users in psychology, sociology, health sciences, medicine, education, business and related disciplines, the SPSS Survival Manual is an essential text. Illustrated with screen grabs, examples of output and tips, it is supported by a website with sample data and guidelines on report writing.This

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix no

Social media data can be vast and understanding which data source works best for a research project, as well as the ethical considerations it requires and how to go about extracting and analysing this

The Vintage Recordings and Data A5 Journal from Galison features a series of analog images from the past such as a vinyl album, floppy disc, VHS tape, cassette tape, and many more. The journal has sle

The idea of a right to privacy, which arose in reaction to the rapid rise of newspapers, instant photography and the “paparazzi” of the 19th century, has evolved into a constitutional right in much of the developed world. It is enshrined in Hong Kong through Articles 28, 29, 30 and 39 of the Basic Law. Hong Kong stands proud as the first jurisdiction in Asia to enact legislation to safeguard personal data in the form of the Personal Data (Privacy) Ordinance, Cap 486 (“the Ordinance”) which came into force in 1996. At its centre are the six Data Protection Principles based on the 1980 OECD Guidelines. The office of the Privacy Commissioner for Personal Data was created under this legislation to provide oversight and ensure compliance. The Octopus scandal in mid-2010 eventually led to substantial changes being made to the Ordinance that were enacted in 2012 and 2013, the main amendments being the Direct Marketing provisions and the provision of legal assistance and representation to aggr

Biology has entered the age of Big Data. A technical revolution has transformed the field, and extracting meaningful information from large biological data sets is now a central methodological challenge. Algebraic topology is a well-established branch of pure mathematics that studies qualitative descriptors of the shape of geometric objects. It aims to reduce comparisons of shape to a comparison of algebraic invariants, such as numbers, which are typically easier to work with. Topological data analysis is a rapidly developing subfield that leverages the tools of algebraic topology to provide robust multiscale analysis of data sets. This book introduces the central ideas and techniques of topological data analysis and its specific applications to biology, including the evolution of viruses, bacteria and humans, genomics of cancer, and single cell characterization of developmental processes. Bridging two disciplines, the book is for researchers and graduate students in genomics and evolu

Seismic data must be interpreted using digital signal processing techniques in order to create accurate representations of petroleum reservoirs and the interior structure of the Earth. This book provides an advanced overview of digital signal processing (DSP) and its applications to exploration seismology using real-world examples. The book begins by introducing seismic theory, describing how to identify seismic events in terms of signals and noise, and how to convert seismic data into the language of DSP. Deterministic DSP is then covered, together with non-conventional sampling techniques. The final part covers statistical seismic signal processing via Wiener optimum filtering, deconvolution, linear-prediction filtering and seismic wavelet processing. With over sixty end-of-chapter exercises, seismic data sets and data processing MATLAB codes included, this is an ideal resource for electrical engineering students unfamiliar with seismic data, and for Earth Scientists and petroleum pr

The question of what types of data and evidence can be used is one of the most important topics in linguistics. This book is the first to comprehensively present the methodological problems associated with linguistic data and evidence. Its originality is twofold. First, the authors' approach accounts for a series of unexplained characteristics of linguistic theorising: the uncertainty and diversity of data, the role of evidence in the evaluation of hypotheses, and the problem solving strategies, as well as the emergence and resolution of inconsistencies. Second, the findings are obtained by the application of a new model of plausible argumentation which is also of relevance from a general argumentation theoretical point of view. All concepts and theses are systematically introduced and illustrated by a number of examples from different linguistic theories, and a detailed case-study section shows how the proposed model can be applied to specific linguistic problems.

Your logical, linear guide to the fundamentals of data science programming Data science is exploding--in a good way--with a forecast of 1.7 megabytes of new information created every second for each h

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, an

Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic

The successful collection of data is a key challenge to obtaining reliable and valid results in applied linguistics research. Data Collection Research Methods in Applied Linguistics investigates how r

A Practical Approach to Data Structure and Algorithm with Programming in C discusses about how data structure and algorithm plays out with programming in C. This book comprises topics such as algorith

After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and mode

Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destructive testing of agricu

Gathering Social Network Data provides an important complement to existing books that focus on social network analysis, and offers more detailed coverage than is available in existing chapter-length t

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-cl

Big Data is everywhere. It shapes our lives in more ways than we know and understand. This comprehensive introduction unravels the complex terabytes that will continue to shape our lives in ways imagi

A guide to all aspects of experimental design and data analysis for fMRI experiments, completely revised and updated for the second edition. Functional magnetic resonance imaging (fMRI), which allows

Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of ver

One of the "six best books for data geeks" - Financial TimesWith over 200 images and extensive how-to and how-not-to examples, this new edition has everything students and scholars need to u

One of the "six best books for data geeks" - Financial Times With over 200 images and extensive how-to and how-not-to examples, this new edition has everything students and scholars need to understand

Technology entrepreneurship has been receiving growing importance as an effective instrument to promote national economic growth, both from empirical researchers and policymakers. India has emerged as the third largest base for high-tech start-ups in the world. Although there is a surge in start-up creation rates in India, little is known about factors required for these start-ups to survive, sustain and grow into large enterprises, particularly in the context of emerging economies like India. This book reviews the entrepreneurial, firm-specific and external environment-specific aspects that influence the key lifecycle stages of high-tech start-ups and identifies the key factors that influence each milestone. Existing literature in this subject has limited studies on the structure of the high-tech start-up sector and processes and strategies adapted by them. This book aims to address this gap, analyzing case studies and empirical data, and provides a multidimensional framework to under

Engaging and accessible, this book offers students a complete guide to using NVivo for qualitative data analysis. Drawing on their wealth of expertise, the authors offer detailed, practical advice tha

Engaging and accessible, this book offers students a complete guide to using NVivo for qualitative data analysis. Drawing on their wealth of expertise, the authors offer detailed, practical advice tha

Master business modeling and analysis techniques with Microsoft Excel 2019, and transform data into bottom-line results. Written by award-winning educator Wayne Winston, this hands-on, scenario-focuse

Volume II of this series discusses the technology used to implement a big data analysis capability within a service-oriented organization. It discusses the technical architecture necessary to implemen

Camera Power is the first book to tackle the policy questions raised by two ongoing revolutions in recording the police: copwatching and police-worn body cameras. Drawing on original research from over 200 jurisdictions and more than 100 interviews - with police leaders and officers, copwatchers, community members, civil rights and civil liberties experts, industry leaders, and technologists - Mary D. Fan offers a vision of the great potential and perils of the growing deluge of audiovisual big data. In contrast to the customary portrayal of big data mining as a threat to civil liberties, Camera Power describes how audiovisual big data analytics can better protect civil rights and liberties and prevent violence in police encounters. With compelling stories and coverage of the most important debates over privacy, public disclosure, proof, and police regulation, this book should be read by anyone interested in how technology is reshaping the relationship with our police.

The increased and widespread availability of large network data resources in recent years has resulted in a growing need for effective methods for their analysis. The challenge is to detect patterns that provide a better understanding of the data. However, this is not a straightforward task because of the size of the data sets and the computer power required for the analysis. The solution is to devise methods for approximately answering the questions posed, and these methods will vary depending on the data sets under scrutiny. This cutting-edge text introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical data sets, using a wide range of real-life examples. Bringing together leading experts, this text provides an ideal introduction to and insight into the interdisciplinary field of network da

Camera Power is the first book to tackle the policy questions raised by two ongoing revolutions in recording the police: copwatching and police-worn body cameras. Drawing on original research from over 200 jurisdictions and more than 100 interviews - with police leaders and officers, copwatchers, community members, civil rights and civil liberties experts, industry leaders, and technologists - Mary D. Fan offers a vision of the great potential and perils of the growing deluge of audiovisual big data. In contrast to the customary portrayal of big data mining as a threat to civil liberties, Camera Power describes how audiovisual big data analytics can better protect civil rights and liberties and prevent violence in police encounters. With compelling stories and coverage of the most important debates over privacy, public disclosure, proof, and police regulation, this book should be read by anyone interested in how technology is reshaping the relationship with our police.

This timely, expertly written monograph looks at the legal impact that the use of 'Big Data' will have on the provision - and substantive law - of insurance. Insurance companies are set to become some

Now updated to cover latest versions and featuring instructions for both Mac and Windows users, this book is still the go-to source of support for getting to grips with qualitative data anal

This book summarizes recent advances in the work on agenda-setting in a comparative perspective. The book first presents and explains the data-gathering effort undertaken within the Comparative Agenda

This book tries to fill a gap in the financial literature on legal data. It is a practitioner’s guide to this emerging legal data domain in the financial markets context, explaining what is meant by l