Data Insights: New Ways to Visualize and Make Sense of Data, Second Edition offers multi-disciplinary perspectives and useful information about how visualizations can open your eyes to data. This thought-provoking book takes a conversational approach to presenting an overview of the subject, while also focusing on key details. It highlights the ideas and work of a variety of people who are actively contributing to this still emerging field. Case studies from business analytics, healthcare, games, security, and network monitoring, among others, portray what is going on in data visualization…mehr
Data Insights: New Ways to Visualize and Make Sense of Data, Second Edition offers multi-disciplinary perspectives and useful information about how visualizations can open your eyes to data. This thought-provoking book takes a conversational approach to presenting an overview of the subject, while also focusing on key details. It highlights the ideas and work of a variety of people who are actively contributing to this still emerging field. Case studies from business analytics, healthcare, games, security, and network monitoring, among others, portray what is going on in data visualization today. A diverse blend of original illustrations and real-world examples, both classical and cutting-edge, help fill in the picture. This book provides an approachable overview of important aspects of data visualization, and: Demonstrates, with a variety of case studies, how visualizations can foster a clearer and more comprehensive understanding of datä Answers the question, How can data visualization help me? with discussions of how it fits into a wide array of purposes and situations; Makes the case that data visualization is not just about technology; it also involves a deeply human process The second chapter of revised version of the book, the Human-Centered Design for Data Visualization focuses on two key areas affecting inclusion and diversity:· Debiasing your data and your visualizations· Neurodiversity and inclusion considerations for working with data and visualizations. Issues include: Color Blindness¿ Data Sonification; Multimodal data interfaces. These issues will be touched on throughout the book and will be brought up in the thought leaders interview sections. The book will explore the ways data analytics and visualization can decrease and decrease inequality.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hunter Whitney is a human-centered design (HCD) strategist, instructor, and author who brings a distinct UX design perspective to data visualization and analytics. He currently works at eSimplicity as a Principal HCD Strategist. He has advised corporations, start-ups, government agencies, and NGOs to help them achieve their goals through a thoughtful, strategic design approach to digital products and services. He contributed a chapter in the book, "Designing for Emerging Technologies: UX for Genomics, Robotics, and the Internet of Things?. His teaching experience includes being a classroom instructor for the courses - "Design Thinking and UX Strategy" and "Human-Centered Design for Data Visualization" for UC Berkeley Extension. He is also an instructor and curriculum advisor for data visualization and UX design programs with UC Davis on Coursera.
Inhaltsangabe
Introduction to the 2nd Edition: From Petabytes to Insights (What’s changed in years after the first edition?) Retrospective discussions with thought leaders about changes they’ve seen in the field and what they might signify Topics reviewed -o Democratizing of data -The mainstreaming and commodification of data o New Jobs and roles in working with datao The evolving relationship of AI and data visualizationo Changes in "data wrangling"o Evolving data visualization toolso Data Literacy and Data Communication Human-Centered Design for Data Visualization Views you can use o Exploring how data technologies affect your own life and work Examples - recommender systems, work monitoring, social media ecosystems and echo chambers Algorithmic job boardso Human-Centered Design Descriptions Connecting the visualization to the audience) Techniques for communicating datä Debiasing your data and your visualizations Neurodiversity and inclusion considerations for working with data and visualizationso Trade-offs between simplicity versus complexityo Color and language selectiono Using different approaches to show the information in different ways for a more complete pictureo Multimodal interfaces with case studies Measuring What Counts Turning abstract numbers into actionable insights Using Quantitative and Qualitative measures What data do you need? Considerations for data collection Data Blind spots - It’s the gaps in data that may be more important than what you have. Am I missing critical data? Design and Data - Considerations for what metrics go on your dashboard and reports UX Outcomeso Measuring impact and scaleo Metrics of successo Problem-Value Metricso Progress Metrics Goals of Data Visualization -1.) Exploratory Data Visualization "A More Beautiful Question" (Interrogating the data without coercion)o Describing the key differences between exploratory and explanatory visualizations and the spectrum in betweeno Case study updates and new ones of exploratory data analysis using interactive visualizationso Links to papers 2.) Explanatory Data Visualization Data Storytelling - presenting an accurate and compelling narrative of the datao The strengths, weaknesses, and caveats of data storytellingo Selected data storytelling techniqueso Case study updates and new oneo Links to papers showing data visualization that help provide overall clarity and context to data-intensive projects…even for people who are not experts in the field 3.) Extrapolatory Data Visualization Using data analysis to help make predictions and scenario testo Making well-informed projectionso Looking at potential outcome with different parameterso Visually showing uncertainty Potential and Perils Data and Ethics Data Governance Privacy versus convenience How will humans collaborate with data tech?Where does it go from here?
Introduction to the 2nd Edition: From Petabytes to Insights (What’s changed in years after the first edition?) Retrospective discussions with thought leaders about changes they’ve seen in the field and what they might signify Topics reviewed -o Democratizing of data -The mainstreaming and commodification of data o New Jobs and roles in working with datao The evolving relationship of AI and data visualizationo Changes in "data wrangling"o Evolving data visualization toolso Data Literacy and Data Communication Human-Centered Design for Data Visualization Views you can use o Exploring how data technologies affect your own life and work Examples - recommender systems, work monitoring, social media ecosystems and echo chambers Algorithmic job boardso Human-Centered Design Descriptions Connecting the visualization to the audience) Techniques for communicating datä Debiasing your data and your visualizations Neurodiversity and inclusion considerations for working with data and visualizationso Trade-offs between simplicity versus complexityo Color and language selectiono Using different approaches to show the information in different ways for a more complete pictureo Multimodal interfaces with case studies Measuring What Counts Turning abstract numbers into actionable insights Using Quantitative and Qualitative measures What data do you need? Considerations for data collection Data Blind spots - It’s the gaps in data that may be more important than what you have. Am I missing critical data? Design and Data - Considerations for what metrics go on your dashboard and reports UX Outcomeso Measuring impact and scaleo Metrics of successo Problem-Value Metricso Progress Metrics Goals of Data Visualization -1.) Exploratory Data Visualization "A More Beautiful Question" (Interrogating the data without coercion)o Describing the key differences between exploratory and explanatory visualizations and the spectrum in betweeno Case study updates and new ones of exploratory data analysis using interactive visualizationso Links to papers 2.) Explanatory Data Visualization Data Storytelling - presenting an accurate and compelling narrative of the datao The strengths, weaknesses, and caveats of data storytellingo Selected data storytelling techniqueso Case study updates and new oneo Links to papers showing data visualization that help provide overall clarity and context to data-intensive projects…even for people who are not experts in the field 3.) Extrapolatory Data Visualization Using data analysis to help make predictions and scenario testo Making well-informed projectionso Looking at potential outcome with different parameterso Visually showing uncertainty Potential and Perils Data and Ethics Data Governance Privacy versus convenience How will humans collaborate with data tech?Where does it go from here?
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