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Data Quality in Southeast Asia: Analysis of Official Statistics and Their Institutional Framework as a Basis for Capacity Building and Policy Making in the ASEAN
by Manuel StagarsThis book explores the reliability of official statisticaldata in the ASEAN (the Association of Southeast Asian Nations), and thebenefits of a better vocabulary to discuss the quality of publicly availabledata to address the needs of all users. It introduces a rigorous method todisaggregate and rate data quality into principal factors containing a total often dimensions, which serves as the basis for a discussion on the opportunitiesand challenges for data quality, capacity building programs and data policy in SoutheastAsia. Tools to standardize and monitor statistical capacity and data qualityare presented, as well as methods and data sources to analyse data quality. Thebook analyses data quality in Indonesia, Malaysia, Singapore, the Philippines,Thailand, Vietnam, Brunei, Laos, Cambodia, and Myanmar, before concluding withthoughts on Open Data and the ASEAN Economic Community (AEC).
Data Science and Analytics Strategy: An Emergent Design Approach (Chapman & Hall/CRC Data Science Series)
by Kailash Awati Alexander ScrivenThis book describes how to establish data science and analytics capabilities in organisations using Emergent Design, an evolutionary approach that increases the chances of successful outcomes while minimising upfront investment. Based on their experiences and those of a number of data leaders, the authors provide actionable advice on data technologies, processes, and governance structures so that readers can make choices that are appropriate to their organisational contexts and requirements. The book blends academic research on organisational change and data science processes with real-world stories from experienced data analytics leaders, focusing on the practical aspects of setting up a data capability. In addition to a detailed coverage of capability, culture, and technology choices, a unique feature of the book is its treatment of emerging issues such as data ethics and algorithmic fairness. Data Science and Analytics Strategy: An Emergent Design Approach has been written for professionals who are looking to build data science and analytics capabilities within their organisations as well as those who wish to expand their knowledge and advance their careers in the data space. Providing deep insights into the intersection between data science and business, this guide will help professionals understand how to help their organisations reap the benefits offered by data. Most importantly, readers will learn how to build a fit-for-purpose data science capability in a manner that avoids the most common pitfalls.
Data Science and Social Research II: Methods, Technologies and Applications (Studies in Classification, Data Analysis, and Knowledge Organization)
by Paolo Mariani Mariangela ZengaThe peer-reviewed contributions gathered in this book address methods, software and applications of statistics and data science in the social sciences. The data revolution in social science research has not only produced new business models, but has also provided policymakers with better decision-making support tools. In this volume, statisticians, computer scientists and experts on social research discuss the opportunities and challenges of the social data revolution in order to pave the way for addressing new research problems. The respective contributions focus on complex social systems and current methodological advances in extracting social knowledge from large data sets, as well as modern social research on human behavior and society using large data sets. Moreover, they analyze integrated systems designed to take advantage of new social data sources, and discuss quality-related issues. The papers were originally presented at the 2nd International Conference on Data Science and Social Research, held in Milan, Italy, on February 4-5, 2019.
Data Science: Create Teams That Ask the Right Questions and Deliver Real Value
by Doug RoseLearn how to build a data science team within your organization rather than hiring from the outside. Teach your team to ask the right questions to gain actionable insights into your business. Most organizations still focus on objectives and deliverables. Instead, a data science team is exploratory. They use the scientific method to ask interesting questions and run small experiments. Your team needs to see if the data illuminate their questions. Then, they have to use critical thinking techniques to justify their insights and reasoning. They should pivot their efforts to keep their insights aligned with business value. Finally, your team needs to deliver these insights as a compelling story. Insight!: How to Build Data Science Teams that Deliver Real Business Value shows that the most important thing you can do now is help your team think about data. Management coach Doug Rose walks you through the process of creating and managing effective data science teams. You will learn how to find the right people inside your organization and equip them with the right mindset. The book has three overarching concepts: You should mine your own company for talent. You can't change your organization by hiring a few data science superheroes. You should form small, agile-like data teams that focus on delivering valuable insights early and often. You can make real changes to your organization by telling compelling data stories. These stories are the best way to communicate your insights about your customers, challenges, and industry. What Your Will Learn: Create data science teams from existing talent in your organization to cost-efficiently extract maximum business value from your organization's data Understand key data science terms and concepts Follow practical guidance to create and integrate an effective data science team with key roles and the responsibilities for each team member Utilize the data science life cycle (DSLC) to model essential processes and practices for delivering value Use sprints and storytelling to help your team stay on track and adapt to new knowledge Who This Book Is For Data science project managers and team leaders. The secondary readership is data scientists, DBAs, analysts, senior management, HR managers, and performance specialists.
Data Stream Mining & Processing: Third International Conference, DSMP 2020, Lviv, Ukraine, August 21–25, 2020, Proceedings (Communications in Computer and Information Science #1158)
by Sergii Babichev Olena Vynokurova Dmytro PeleshkoThis book constitutes the proceedings of the third International Conference on Data Stream and Mining and Processing, DSMP 2020, held in Lviv, Ukraine*, in August 2020.The 36 full papers presented in this volume were carefully reviewed and selected from 134 submissions. The papers are organized in topical sections of hybrid systems of computational intelligence; machine vision and pattern recognition; dynamic data mining & data stream mining; big data & data science using intelligent approaches.*The conference was held virtually due to the COVID-19 pandemic.
Data Visualization: A Practical Introduction
by Kieran HealyAn accessible primer on how to create effective graphics from dataThis book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way.Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible.Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings.Provides hands-on instruction using R and ggplot2Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistentIncludes a library of data sets, code, and functions
Data Visualization: Principles and Practice, Second Edition
by Alexandru C. TeleaDesigning a complete visualization system involves many subtle decisions. When designing a complex, real-world visualization system, such decisions involve many types of constraints, such as performance, platform (in)dependence, available programming languages and styles, user-interface toolkits, input/output data format constraints, integration wi
Data and AI Driving Smart Cities (Studies in Big Data #128)
by Ursula Eicker Arturo Molina Pedro Ponce Troy McDaniel Therese Peffer Juana Isabel Mendez Garduno Edgard D. Musafiri Mimo Ramanunni Parakkal Menon Kathryn Kaspar Sadam HussainThis book illustrates how the advanced technology developed for smart cities requires increasing interaction with citizens to motivate and incentive them. Megacities' needs have been encouraging for the creation of smart cities in which the needs of inhabitants are collected using virtualization and digitalization systems. On the other hand, machine learning algorithms have been implemented to provide better solutions for diverse areas in smart cities, such as transportation and health. Besides, conventional electric grids have transformed into smart grids, improving energy quality. Gamification, serious games, machine learning, dynamic interfaces, and social networks are some elements integrated holistically to provide novel solutions to design and develop smart cities. Also, this book presents in a friendly way the concept of social devices that are incorporated into smart homes and buildings. This book is used to understand and design smart cities where citizens are strongly interconnected so the demand response time can be reduced.
Data and Applications Security and Privacy XXXI: 31st Annual IFIP WG 11.3 Conference, DBSec 2017, Philadelphia, PA, USA, July 19-21, 2017, Proceedings (Lecture Notes in Computer Science #10359)
by Giovanni Livraga and Sencun ZhuThis book constitutes the refereed proceedings of the 31st Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2017, held in Philadelphia, PA, USA, in July 2017.The 21 full papers and 9 short papers presented were carefully reviewed and selected from 59 submissions. The papers are organized in topical sections on access control, privacy, cloud security, secure storage in the cloud, secure systems, and security in networks and Web.
Data and Applications Security and Privacy XXXII: 32nd Annual IFIP WG 11.3 Conference, DBSec 2018, Bergamo, Italy, July 16–18, 2018, Proceedings (Lecture Notes in Computer Science #10980)
by Florian Kerschbaum Stefano ParaboschiThis book constitutes the refereed proceedings of the 32nd Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2018, held in Bergamo, Italy, in July 2018. The 16 full papers and 5 short papers presented were carefully reviewed and selected from 50 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections on administration, access control policies, privacy-preserving access and computation, integrity and user interaction, security analysis and private evaluation, fixing vulnerabilities, and networked systems.
Data and Applications Security and Privacy XXXIII: 33rd Annual IFIP WG 11.3 Conference, DBSec 2019, Charleston, SC, USA, July 15–17, 2019, Proceedings (Lecture Notes in Computer Science #11559)
by Simon N. FoleyThis book constitutes the refereed proceedings of the 33rd Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2019, held in Charleston, SC, USA, in July 2018.The 21 full papers presented were carefully reviewed and selected from 52 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections on attacks, mobile and Web security, privacy, security protocol practices, distributed systems, source code security, and malware.
Data and Applications Security and Privacy XXXVI: 36th Annual IFIP WG 11.3 Conference, DBSec 2022, Newark, NJ, USA, July 18–20, 2022, Proceedings (Lecture Notes in Computer Science #13383)
by Shamik Sural Haibing LuThis book constitutes the refereed proceedings of the 36th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2022, held in Newark, NJ, USA, in July 2022.The 12 full papers and 6 short papers presented were carefully reviewed and selected from 33 submissions. The conference covers research in data and applications security and privacy.
Data and Applications Security and Privacy XXXVII: 37th Annual IFIP WG 11.3 Conference, DBSec 2023, Sophia-Antipolis, France, July 19–21, 2023, Proceedings (Lecture Notes in Computer Science #13942)
by Vijayalakshmi Atluri Anna Lisa FerraraThis volume LNCS 13942 constitutes the refereed proceedings of the 37th Annual IFIP WG 11.3 Conference, DBSec 2023, in Sophia-Antipolis, France, July 19–21, 2023. The 19 full papers presented together with 5 short papers were carefully reviewed and selected from 56 submissions. The conference focuses on secure data sharing; access control and vulnerability assessment; machine learning; and mobile applications.
Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World
by Bruce SchneierYou are under surveillance right now.<P><P> Your cell phone provider tracks your location and knows who’s with you. Your online and in-store purchasing patterns are recorded, and reveal if you're unemployed, sick, or pregnant. Your e-mails and texts expose your intimate and casual friends. Google knows what you’re thinking because it saves your private searches. Facebook can determine your sexual orientation without you ever mentioning it.<P> The powers that surveil us do more than simply store this information. Corporations use surveillance to manipulate not only the news articles and advertisements we each see, but also the prices we’re offered. Governments use surveillance to discriminate, censor, chill free speech, and put people in danger worldwide. And both sides share this information with each other or, even worse, lose it to cybercriminals in huge data breaches.<P> Much of this is voluntary: we cooperate with corporate surveillance because it promises us convenience, and we submit to government surveillance because it promises us protection. The result is a mass surveillance society of our own making. But have we given up more than we’ve gained? In Data and Goliath, security expert Bruce Schneier offers another path, one that values both security and privacy. He shows us exactly what we can do to reform our government surveillance programs and shake up surveillance-based business models, while also providing tips for you to protect your privacy every day. You'll never look at your phone, your computer, your credit cards, or even your car in the same way again.
Data and Society: A Critical Introduction
by Anne Beaulieu Sabina LeonelliData and Society: A Critical Introduction investigates the growing importance of data as a technological, social, economic and scientific resource. It explains how data practices have come to underpin all aspects of human life and explores what this means for those directly involved in handling data. The book fosters informed debate over the role of data in contemporary society explains the significance of data as evidence beyond the "Big Data" hype spans the technical, sociological, philosophical and ethical dimensions of data provides guidance on how to use data responsibly includes data stories that provide concrete cases and discussion questions. Grounded in examples spanning genetics, sport and digital innovation, this book fosters insight into the deep interrelations between technical, social and ethical aspects of data work.
Data and Society: A Critical Introduction
by Anne Beaulieu Sabina LeonelliData and Society: A Critical Introduction investigates the growing importance of data as a technological, social, economic and scientific resource. It explains how data practices have come to underpin all aspects of human life and explores what this means for those directly involved in handling data. The book fosters informed debate over the role of data in contemporary society explains the significance of data as evidence beyond the "Big Data" hype spans the technical, sociological, philosophical and ethical dimensions of data provides guidance on how to use data responsibly includes data stories that provide concrete cases and discussion questions. Grounded in examples spanning genetics, sport and digital innovation, this book fosters insight into the deep interrelations between technical, social and ethical aspects of data work.
Data and the City (Regions and Cities)
by Rob Kitchin Tracey P. Lauriault Gavin McArdleThere is a long history of governments, businesses, science and citizens producing and utilizing data in order to monitor, regulate, profit from and make sense of the urban world. Recently, we have entered the age of big data, and now many aspects of everyday urban life are being captured as data and city management is mediated through data-driven technologies. Data and the City is the first edited collection to provide an interdisciplinary analysis of how this new era of urban big data is reshaping how we come to know and govern cities, and the implications of such a transformation. This book looks at the creation of real-time cities and data-driven urbanism and considers the relationships at play. By taking a philosophical, political, practical and technical approach to urban data, the authors analyse the ways in which data is produced and framed within socio-technical systems. They then examine the constellation of existing and emerging urban data technologies. The volume concludes by considering the social and political ramifications of data-driven urbanism, questioning whom it serves and for what ends. This book, the companion volume to 2016’s Code and the City, offers the first critical reflection on the relationship between data, data practices and the city, and how we come to know and understand cities through data. It will be crucial reading for those who wish to understand and conceptualize urban big data, data-driven urbanism and the development of smart cities.
Data for All
by John K. ThompsonDo you know what happens to your personal data when you are browsing, buying, or using apps? Discover how your data is harvested and exploited, and what you can do to access, delete, and monetize it.Data for All empowers everyone—from tech experts to the general public—to control how third parties use personal data. Read this eye-opening book to learn: The types of data you generate with every action, every day Where your data is stored, who controls it, and how much money they make from it How you can manage access and monetization of your own data Restricting data access to only companies and organizations you want to support The history of how we think about data, and why that is changing The new data ecosystem being built right now for your benefit The data you generate every day is the lifeblood of many large companies—and they make billions of dollars using it. In Data for All, bestselling author John K. Thompson outlines how this one-sided data economy is about to undergo a dramatic change. Thompson pulls back the curtain to reveal the true nature of data ownership, and how you can turn your data from a revenue stream for companies into a financial asset for your benefit. Foreword by Thomas H. Davenport. About the Technology Do you know what happens to your personal data when you&’re browsing and buying? New global laws are turning the tide on companies who make billions from your clicks, searches, and likes. This eye-opening book provides an inspiring vision of how you can take back control of the data you generate every day. About the Book Data for All gives you a step-by-step plan to transform your relationship with data and start earning a &“data dividend&”—hundreds or thousands of dollars paid out simply for your online activities. You&’ll learn how to oversee who accesses your data, how much different types of data are worth, and how to keep private details private. What&’s Inside The types of data you generate with every action, every day How you can manage access and monetization of your own data The history of how we think about data, and why that is changing The new data ecosystem being built right now for your benefit About the Reader For anyone who is curious or concerned about how their data is used. No technical knowledge required. About the Author John K. Thompson is an international technology executive with over 37 years of experience in the fields of data, advanced analytics, and artificial intelligence. Table of Contents 1 A history of data 2 How data works today 3 You and your data 4 Trust 5 Privacy 6 Moving from Open Data to Our Data 7 Derived data, synthetic data, and analytics 8 Looking forward: What&’s next for our data?
Data for Social Good: Non-Profit Sector Data Projects
by Jane Farmer Anthony McCosker Kath Albury Amir AryaniThis open access book provides practical guidance for non-profits and community sector organisations about how to get started with data analytics projects using their own organisations’ datasets and open public data. The book shares best practices on collaborative social data projects and methodology. For researchers, the work offers a playbook for partnering with community organisations in data projects for public good and gives worked examples of projects of various sizes and complexity.
Data for the Public Good: How Data Can Help Citizens and Government
by Alex HowardAs we move into an era of unprecedented volumes of data and computing power, the benefits aren't for business alone. Data can help citizens access government, hold it accountable and build new services to help themselves. Simply making data available is not sufficient. The use of data for the public good is being driven by a distributed community of media, nonprofits, academics and civic advocates.This report from O'Reilly Radar highlights the principles of data in the public good, and surveys areas where data is already being used to great effect, covering: Consumer financeTransit dataGovernment transparencyData journalismAid and developmentCrisis and emergency responseHealthcare
Data in Society: Challenging Statistics in an Age of Globalisation
by Jeff Evans, Sally Ruane and Humphrey SouthallStatistical data and evidence-based claims are increasingly central to our everyday lives. Critically examining ‘Big Data’, this book charts the recent explosion in sources of data, including those precipitated by global developments and technological change. It sets out changes and controversies related to data harvesting and construction, dissemination and data analytics by a range of private, governmental and social organisations in multiple settings. Analysing the power of data to shape political debate, the presentation of ideas to us by the media, and issues surrounding data ownership and access, the authors suggest how data can be used to uncover injustices and to advance social progress.
Data, Methods and Theory in the Organizational Sciences: A New Synthesis (SIOP Organizational Frontiers Series)
by Kevin R. MurphyData, Methods and Theory in the Organizational Sciences explores the long-term evolution and changing relationships between data, methods, and theory in the organizational sciences. In the last 50 years, theory has come to dominate research and scholarship in these fields, yet the emergence of big data, as well as the increasing use of archival data sets and meta-analytic methods to test empirical hypotheses, has upset this order. This volume examines the evolving relationship between data, methods, and theory and suggests new ways of thinking about the role of each in the development and presentation of research in organizations. This volume utilizes the latest thinking from experts in a wide range of fields on the topics of data, methods, and theory and uses this knowledge to explore the ways in which behavior in organizations has been studied. This volume also argues that the current focus on theory is both unhealthy for the field and unsustainable, and it provides more successful ways theory can be used to support and structure research, and demonstrates the most effective techniques for analyzing and making sense of data. This is an essential resource for researchers, professionals, and educators who are looking to rethink their current approaches to research, and who are interested in creating more useful and more interpretable research in the organizational sciences.
Data-Based Child Advocacy: Using Statistical Indicators to Improve the Lives of Children (SpringerBriefs in Well-Being and Quality of Life Research #0)
by William P. O'HareThis book locates, organizes and summarizes information about the use of child indicators in an advocacy context. It provides a conceptual framework that allows readers to see a wide variety of work as part of a unified field. It provides a description of key concepts and illustrates these concepts by offering many examples from a range of countries and a wide variety of applications. It covers work from governments, non-governmental organization and academics. It describes such aspects as the use of data to educate and increase public awareness, as well as to monitor, set goals and evaluate programs serving children. A growing number of organizations and people are focusing on measuring and monitoring the well-being of children and these child well-being data are often employed in ways that go beyond what is typically considered scholarship. Many of these applications involve some type of advocacy activity. Yet, there is very little in the literature about the use of child indicators in an advocacy context. This book provides a framework for scholars in a variety of disciplines that will help them to structure their thinking about the use of such indicators in a public context.
Data-Centric Biology: A Philosophical Study
by Sabina LeonelliIn recent decades, there has been a major shift in the way researchers process and understand scientific data. Digital access to data has revolutionized ways of doing science in the biological and biomedical fields, leading to a data-intensive approach to research that uses innovative methods to produce, store, distribute, and interpret huge amounts of data. In Data-Centric Biology, Sabina Leonelli probes the implications of these advancements and confronts the questions they pose. Are we witnessing the rise of an entirely new scientific epistemology? If so, how does that alter the way we study and understand life—including ourselves? Leonelli is the first scholar to use a study of contemporary data-intensive science to provide a philosophical analysis of the epistemology of data. In analyzing the rise, internal dynamics, and potential impact of data-centric biology, she draws on scholarship across diverse fields of science and the humanities—as well as her own original empirical material—to pinpoint the conditions under which digitally available data can further our understanding of life. Bridging the divide between historians, sociologists, and philosophers of science, Data-Centric Biology offers a nuanced account of an issue that is of fundamental importance to our understanding of contemporary scientific practices.
Data-Driven HR: How to Use AI, Analytics and Data to Drive Performance
by Bernard MarrHow can HR professionals utilize and leverage their organization's data effectively, with the use of AI, for more talent attraction, better employee engagement and higher talent retention to ultimately drive performance?AI is now an integral part of being data-driven. With this updated edition of Data-Driven HR, practitioners can unlock business potential and success through data and analytics. Covering topics such as recruitment, employee engagement, performance management, wellbeing and training, HR practitioners can benefit from knowing how to really be data-driven through the use of data and AI. HR teams will learn how to identify business goals, scrutinize useful sources of data and gain rich and diverse insights from their vast amounts of data. This book brings guidance on how to manage challenges that come with data and AI, as well as how to responsibly and transparently use data to improve decision making. It also includes predictive analytics and how to place warning systems into databases for any potential workforce issues. Packed with practical advice, key takeaways and real-life examples, this is essential reading for all HR professionals looking to make a measurable difference in their organizations.