Lecture 4

The seven steps of visualization design. Different ways to acquire data. The importance of parsing and filtering data. Mining data for exploratory data analysis. Choosing the right representation for the given data and the given task. Online repositories of charts for various purposes. Refining the visualization and supporting interactivity.

The dos and don’ts of basic charts (line charts, bar charts). Stacked bar charts and pie charts. Visualizing geographical data with dot distribution maps and choropleth maps. Visualizing geographical data with tile maps. Visualizing networks and trees with node-link diagrams and adjacency matrices. Visualizing multidimensional data with Chernoff faces, bubble plots, the scatter plot matrix, parallel coordinates, radar charts, radial histograms, small multiples and horizon charts. Using principal component analysis and multidimensional scaling for visual exploratory data analysis.

Visualizing uncertain and missing data. Advantages and disadvantages of interactivity. Using interactivity for data adjustments (framing, navigating, animating, sequencing and contributing) and presentation adjustments (focusing, annotating and orientating). Examples of interaction, animation and storytelling. Examples of available visualization tools (D3, Observable, Tableau and Processing).

Instructions for the second assignment

Information about the exam (in project form)