Section: Lecture 6 | 481SM - INFORMATION RETRIEVAL AND DATA VISUALIZATION 2022

Section outline

- Select activity Third assignment
  
  Third assignment File PDF
  
  Use visualization to answer two questions
- Select activity Third assignment (data)
  
  Third assignment (data) File CSV
- Select activity Required Python libraries
  
  Required Python libraries File PDF
  
  An installation guide to the Python libraries required for the hands-on lesson
- Select activity Test Plotly
  
  Test Plotly File IPYNB
  
  A notebook with a simple test of the Plotly library
- Select activity Test Dash
  
  Test Dash File IPYNB
  
  A notebook with a simple test of the Dash library
- Select activity Examples of (un)trustworthy visualizations
  
  Examples of (un)trustworthy visualizations File PDF
  
  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.