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Course Outline

Day 1:

  • Understanding data visualization
  • Why it matters
  • Data visualization versus data mining
  • Human cognition
  • HMI
  • Common pitfalls

Day 2:

  • Types of curves
  • Drill-down curves
  • Plotting categorical data
  • Multi-variable plots
  • Data glyph and icon representation

Day 3:

  • Plotting KPIs alongside data
  • Examples of R and X charts
  • ‘What-if’ dashboards
  • Parallel axes mixing
  • Combining categorical and numeric data

Day 4:

  • Different roles in data visualization
  • How data visualization can be misleading
  • Disguised and hidden trends
  • Case study: Student data
  • Visual queries and region selection

Requirements

Participants should have some experience with plotting X-Y graphs, histograms, and scatter plots, along with a general understanding of data trends and time series graphing.

 28 Hours

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