Human-Centred Scientific Data Visualisation
Making Complex Data Accessible for Everyone
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Beschreibung
This textbook is a practical guide for researchers, educators, data analysts, and user experience (UX) designers who seek to transform raw, complex datasets into clear, accessible, and engaging visual narratives. Covering a wide range of chart types—including line plots, bar charts, box plots, scatterplots, histograms, pie and donut charts, spider plots, ridgeline plots, density plots, and advanced visualizations like treemaps, network graphs, and Sankey diagrams, it blends visualization theory with hands-on implementation in Python and R.
Combining the rigor of scientific communication with the principles of UX design, it offers practical techniques, real-world examples, and hands-on coding strategies in Python and R to create data visualizations that speak to both experts and non-experts. Whether you're visualizing data for policymakers, stakeholders, or the public, this book empowers you to build informative and aesthetically compelling visualizations that make data not only visible, but truly understandable.
This textbook is a practical guide for researchers, educators, data analysts, and user experience (UX) designers who seek to transform raw, complex datasets into clear, accessible, and engaging visual narratives. Covering a wide range of chart types—including line plots, bar charts, box plots, scatterplots, histograms, pie and donut charts, spider plots, ridgeline plots, density plots, and advanced visualizations like treemaps, network graphs, and Sankey diagrams, it blends visualization theory with hands-on implementation in Python and R.
Combining the rigor of scientific communication with the principles of UX design, it offers practical techniques, real-world examples, and hands-on coding strategies in Python and R to create data visualizations that speak to both experts and non-experts. Whether you're visualizing data for policymakers, stakeholders, or the public, this book empowers you to build informative and aesthetically compelling visualizations that make data not only visible, but truly understandable.
Helps users sidestep common design errors such аs overplotting, poor аxis scаling, аnd misleаding color grаdients Guides users on how to provide color-blind friendly pаlettes, reаdаble lаbels, аnd optimized contrаst Readers will learn to visualize data patterns clearly and effectively
Autor*in
Robertas Damaševičius
Themen in »Human-Centred Scientific Data Visualisation«
Data Visualization Visualization Design Digital Data Exploration Data Patterns Creating Accessible Visualizations