Understanding Well-being Data

by Susan Oman
Understanding Well-being Data
Author: Susan Oman
Publisher: Springer Nature
Total Pages: 405
Release: 2021
Genre: Cultural policy
ISBN: 3030729370
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'Following the data' is a now-familiar phrase in Covid-19 policy communications. Well-being data are pivotal in decisions that affect our life chances, livelihoods and quality of life. They are increasingly valuable to companies with their eyes on profit, organisations looking to make a social impact, and governments focussed on societal problems. This book follows well-being data back centuries, showing they have long been used to track the health and wealth of society. It questions assumptions that have underpinned over 200 years of social science, statistical and policy work. Understanding Well-being Data is a readable, introductory book with real-life examples. Understanding the contexts of data and decision-making are critical for policy, practice and research that aims to do good, or at least avoid harm. Through its comprehensive survey and critical lens, this book provides tools to promote better understanding of the power and potential of well-being data for society, and the limits of their application.

Data Analysis

by Peter J. Huber
Data Analysis
Author: Peter J. Huber
Publisher: John Wiley & Sons
Total Pages: 267
Release: 2012-01-09
Genre: Mathematics
ISBN: 1118018265
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This book explores the many provocative questions concerning the fundamentals of data analysis. It is based on the time-tested experience of one of the gurus of the subject matter. Why should one study data analysis? How should it be taught? What techniques work best, and for whom? How valid are the results? How much data should be tested? Which machine languages should be used, if used at all? Emphasis on apprenticeship (through hands-on case studies) and anecdotes (through real-life applications) are the tools that Peter J. Huber uses in this volume. Concern with specific statistical techniques is not of immediate value; rather, questions of strategy – when to use which technique – are employed. Central to the discussion is an understanding of the significance of massive (or robust) data sets, the implementation of languages, and the use of models. Each is sprinkled with an ample number of examples and case studies. Personal practices, various pitfalls, and existing controversies are presented when applicable. The book serves as an excellent philosophical and historical companion to any present-day text in data analysis, robust statistics, data mining, statistical learning, or computational statistics.

On Being a Data Skeptic

by Cathy O'Neil
On Being a Data Skeptic
Author: Cathy O'Neil
Publisher: "O'Reilly Media, Inc."
Total Pages: 26
Release: 2013-09-30
Genre: Computers
ISBN: 149194725X
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"Data is here, it's growing, and it's powerful." Author Cathy O'Neil argues that the right approach to data is skeptical, not cynical––it understands that, while powerful, data science tools often fail. Data is nuanced, and "a really excellent skeptic puts the term 'science' into 'data science.'" The big data revolution shouldn't be dismissed as hype, but current data science tools and models shouldn't be hailed as the end-all-be-all, either.

Storytelling with Data

by Cole Nussbaumer Knaflic
Storytelling with Data
Author: Cole Nussbaumer Knaflic
Publisher: John Wiley & Sons
Total Pages: 284
Release: 2015-10-09
Genre: Mathematics
ISBN: 1119002265
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Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it!

Data Feminism

by Catherine D'Ignazio
Data Feminism
Author: Catherine D'Ignazio
Publisher: MIT Press
Total Pages: 328
Release: 2020-03-31
Genre: Social Science
ISBN: 0262358530
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A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

Intelligent Data Sensing and Processing for Health and Well-being Applications

by Miguel Antonio Wister Ovando
Intelligent Data Sensing and Processing for Health and Well-being Applications
Author: Miguel Antonio Wister Ovando
Publisher: Academic Press
Total Pages: 316
Release: 2018-07-26
Genre: Science
ISBN: 0128123206
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Intelligent Data Sensing and Processing for Health and Well-being Applications uniquely combines full exploration of the latest technologies for sensor-collected intelligence with detailed coverage of real-case applications for healthcare and well-being at home and in the workplace. Forward-thinking in its approach, the book presents concepts and technologies needed for the implementation of today's mobile, pervasive and ubiquitous systems, and for tomorrow's IoT and cyber-physical systems. Users will find a detailed overview of the fundamental concepts of gathering, processing and analyzing data from devices disseminated in the environment, as well as the latest proposals for collecting, processing and abstraction of data-sets. In addition, the book addresses algorithms, methods and technologies for diagnosis and informed decision-making for healthcare and well-being. Topics include emotional interface with ambient intelligence and emerging applications in detection and diagnosis of neurological diseases. Finally, the book explores the trends and challenges in an array of areas, such as applications for intelligent monitoring in the workplace for well-being, acquiring data traffic in cities to improve the assistance of first aiders, and applications for supporting the elderly at home. - Examines the latest applications and future directions for mobile data sensing in an array of health and well-being scenarios - Combines leading computing paradigms and technologies, development applications, empirical studies, and future trends in the multidisciplinary field of smart sensors, smart sensor networks, data analysis and machine intelligence methods - Features an analysis of security, privacy and ethical issues in smart sensor health and well-being applications - Equips readers interested in interdisciplinary projects in ubiquitous computing or pervasive computing and ambient intelligence with the latest trends and developments

State Data Profiles

by United States. Agricultural Stabilization and Conservation Service. Data Systems Division
State Data Profiles
Author: United States. Agricultural Stabilization and Conservation Service. Data Systems Division
Publisher:
Total Pages: 116
Release: 1973
Genre: Agricultural price supports
ISBN:
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Storytelling with Data

by Cole Nussbaumer Knaflic
Storytelling with Data
Author: Cole Nussbaumer Knaflic
Publisher: John Wiley & Sons
Total Pages: 454
Release: 2019-10-22
Genre: Computers
ISBN: 1119621496
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Influence action through data! This is not a book. It is a one-of-a-kind immersive learning experience through which you can become—or teach others to be—a powerful data storyteller. Let’s practice! helps you build confidence and credibility to create graphs and visualizations that make sense and weave them into action-inspiring stories. Expanding upon best seller storytelling with data’s foundational lessons, Let’s practice! delivers fresh content, a plethora of new examples, and over 100 hands-on exercises. Author and data storytelling maven Cole Nussbaumer Knaflic guides you along the path to hone core skills and become a well-practiced data communicator. Each chapter includes: ● Practice with Cole: exercises based on real-world examples first posed for you to consider and solve, followed by detailed step-by-step illustration and explanation ● Practice on your own: thought-provoking questions and even more exercises to be assigned or worked through individually, without prescribed solutions ● Practice at work: practical guidance and hands-on exercises for applying storytelling with data lessons on the job, including instruction on when and how to solicit useful feedback and refine for greater impact The lessons and exercises found within this comprehensive guide will empower you to master—or develop in others—data storytelling skills and transition your work from acceptable to exceptional. By investing in these skills for ourselves and our teams, we can all tell inspiring and influential data stories!

R for Data Science

by Hadley Wickham
R for Data Science
Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
Total Pages: 521
Release: 2016-12-12
Genre: Computers
ISBN: 1491910364
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Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results