Data Visualization
Schema della sezione
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The lectures on Data Visualization start on Friday, November 20. We will continue with the established schedule, that is Monday and Fridays from 9:00 to 11:00. All lectures will be online only. You can access the Teams here.
All students that were members of the Information Retrieval Team are also members of the Data Visualization Team. If a mistake was made and this is not the case, let me know.
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Course introduction
Foundations
- What is data visualization
- Why visualize data
- Historical visualizations
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Foundations File PDF
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Foundations
- The three principles of good visualization design
Data abstraction- Dataset types
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Data abstraction
- Attribute types and semantics
Task abstraction- Goals and tasks
- Actions and targets
Human visual perception- Motivation
- Memory
- Visual encoding
- Channel accuracy
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Human visual perception
- Channel discriminability
- Channel salience (pop-out)
- Channel separability
- Grouping
- Visual order
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Human visual perception
- Color use: color maps, semantics of color, considerations for color blind people, the importance of size and contrast
- Color use: color maps, semantics of color, considerations for color blind people, the importance of size and contrast
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Human visual perception
- Color use: importance of background and surrounding color, choosing color
Visualization design- The seven steps of visualization design
- Basic charts: line, bar and pie charts, dot and choropleth maps
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Visualization design
- Basic charts: tile maps, node-link diagrams and adjacency matrices
- Visualizing multidimensional data
- Visualizing uncertain data
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Visualization design
- Visualizing missing data
- Using interactivity for data adjustments and presentation adjustments
- Examples of interaction, animation and storytelling
- Visualization tools (D3, Tableau)
Examples of (un)trustworthy visualizations- Visualizations using dubious data
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Reviewing the results of the third assignment
Information about the exam
Examples of (un)trustworthy visualizations
- Ignoring conventions
- Abusing scales
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Python libraries needed for hands-on lessons (Plotly and Dash)
Examples of (un)trustworthy visualizations
- Improper categorization
- Oversimplifying
- Ignoring uncertainty
- Confirmation bias
Examples of (in)accessible visualizations -
Test Plotly File IPYNB
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Test Dash File IPYNB
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How to create accessible visualizations
Visualizing COVID-19
Hands-on example in Python
- The Plotly library
- Recreating the interactive Gapminder bubble chart
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Plotly Basics File IPYNB
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Hands-on example in Python
- Adding animation to the Gapminder bubble chart
- The Dash library
- Using dropdowns to select axes of the chart
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