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Table of Contents
Part I. Foundations : What we talk about when we talk about visualization
The five qualities of great visualizations : The hockey stick chart : Truthful; Functional; Beautiful; Insightful; Enlightening
Part II. Truthful : The truth continuum : Dubious models ; The mind, that clumsy modeler : Why are we so often mistaken?; Mind bug 1. Patternicity; Mind bug 2. Storytelling; Mind bug 3. Confirmation ; Truth is neither absolute, nor relative : The skills of an educated person
Of conjectures and uncertainty : The scientific stance ; From curiosity to conjectures : Hypothesizing; An aside on variables; On studies; Doing experiments; About uncertainty
Part III. Functional : Basic principles of visualization : Visually encoding data ; Choosing graphic forms : A grain of salt; Practical tips about those tricky scales ; Organizing the display : Put your work to the test
Exploring data with simple charts : The norm and the exceptions : The mode, the median, and the mean; Weighing means; Range; Some fun with ranges
Visualizing distributions : The variance and the standard deviation : Standard deviation and standard scores; Presenting stories with frequency charts; How standardized scores mislead us; Peculiarities of the normal distribution ; Percentiles, quartiles, and the box plot : Manipulating data; Frequency over time
Revealing change : Trend, seasonality, and noise : Decomposing a time series; Visualizing indexes ; From ratios to logs ; How time series charts mislead : Communicating change
Seeing relationships : From association to correlation : Smoothers; Matrices and parallel coordinate plots ; Correlation for communication regression ; From correlation to causation : Data transformation
Mapping data : Projection ; Data on maps : Point data maps; Line maps ; The basics of choropleth maps : Classifying data; Using choropleth maps; Other kinds of data maps
Uncertainty and significance : Back to distributions : The standard error; Building confidence intervals; Disclosing uncertainty ; The best advice you'll ever get: ask ; How has stuff been measured?; Dispatches from the data jungle; Significance, size and power
Part IV. Practice : On creativity and innovation : The popularizers ; News applications ; Those enchanting Europeans ; Making science visual ; Culture data ; The artistic fringes
Epilogue : What lies ahead.
The five qualities of great visualizations : The hockey stick chart : Truthful; Functional; Beautiful; Insightful; Enlightening
Part II. Truthful : The truth continuum : Dubious models ; The mind, that clumsy modeler : Why are we so often mistaken?; Mind bug 1. Patternicity; Mind bug 2. Storytelling; Mind bug 3. Confirmation ; Truth is neither absolute, nor relative : The skills of an educated person
Of conjectures and uncertainty : The scientific stance ; From curiosity to conjectures : Hypothesizing; An aside on variables; On studies; Doing experiments; About uncertainty
Part III. Functional : Basic principles of visualization : Visually encoding data ; Choosing graphic forms : A grain of salt; Practical tips about those tricky scales ; Organizing the display : Put your work to the test
Exploring data with simple charts : The norm and the exceptions : The mode, the median, and the mean; Weighing means; Range; Some fun with ranges
Visualizing distributions : The variance and the standard deviation : Standard deviation and standard scores; Presenting stories with frequency charts; How standardized scores mislead us; Peculiarities of the normal distribution ; Percentiles, quartiles, and the box plot : Manipulating data; Frequency over time
Revealing change : Trend, seasonality, and noise : Decomposing a time series; Visualizing indexes ; From ratios to logs ; How time series charts mislead : Communicating change
Seeing relationships : From association to correlation : Smoothers; Matrices and parallel coordinate plots ; Correlation for communication regression ; From correlation to causation : Data transformation
Mapping data : Projection ; Data on maps : Point data maps; Line maps ; The basics of choropleth maps : Classifying data; Using choropleth maps; Other kinds of data maps
Uncertainty and significance : Back to distributions : The standard error; Building confidence intervals; Disclosing uncertainty ; The best advice you'll ever get: ask ; How has stuff been measured?; Dispatches from the data jungle; Significance, size and power
Part IV. Practice : On creativity and innovation : The popularizers ; News applications ; Those enchanting Europeans ; Making science visual ; Culture data ; The artistic fringes
Epilogue : What lies ahead.