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Course Outline

Day 1:

  • Introduction to data visualization
  • The importance of effective visualization
  • Data visualization versus data mining
  • Human cognition in visual perception
  • Human-Machine Interface (HMI)
  • Common pitfalls in visualization

Day 2:

  • Exploring different types of curves
  • Drill-down curve techniques
  • 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 techniques
  • Combining categorical and numeric data

Day 4:

  • The multiple roles of data visualization
  • How data visualization can be misleading
  • Disguised and hidden trends
  • Case study: Student data analysis
  • Visual queries and region selection

Requirements

Participants should have some familiarity with plotting X-Y graphs, histograms, and scatter plots, as well as a general understanding of data trends and time series visualization.

 28 Hours

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