Lecture 5

Results of the fourth assignment

Fifth assignment: create a single visualization for the given data

The data for the fifth assignment 

Information about the required Python libraries for the hands-on lessons

Examples of (un)trustworthy visualizations. Visualizations that lie by using dubious data, such as unrepresentative data and missing data. Using non-comparable data in comparisons. Using absolute instead of cumulative data (and vice versa). Using absolute instead of relative data on maps. Examples of ignoring conventions (unequal intervals, pie charts that do not add up to 100%) and abusing scales (bar charts with truncated axis, aspect ratio bias, dual axes, improper scaling of areas and pictograms). Examples of misrepresenting data by using unnecessary 3-D visualizations. Examples of improper categorization and oversimplification. Examples of cherry-picking data in order to hide (unfavorable) data or conceal existing patterns. Examples of visualizations suggesting patterns that are not there. Examples of misrepresenting or concealing uncertainty. Examples of erroneous interpretation of visualizations due to confirmation bias. 

Example of (in)accessible visualizations: redesign of diversity of aging, plots in Excel, smoothed line charts, slope graphs, connected scatter plots, the importance of notations. Adding alt text to plots. Guidelines for creating accessible visualizations.

A notebook with a simple test of the Plotly library

A notebook with a simple test of the Dash library