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Course Outline

Day 1:

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

Day 2:

  • Types of data curves
  • Drill-down curves
  • Visualizing categorical data
  • Multi-variable plots
  • Using data glyphs and icons for representation

Day 3:

  • Plotting Key Performance Indicators (KPIs)
  • Examples of R and X charts
  • What-if dashboards
  • Mixed parallel axes plots
  • Combining categorical and numeric data

Day 4:

  • Diverse roles in data visualization
  • How data visualization can be misleading
  • Identifying disguised and hidden trends
  • Case study: Analysis of student data
  • Visual queries and region selection techniques

Requirements

A basic understanding of data plotting techniques, including X-Y graphs, histograms, and scatter plots, along with a general grasp of data trends and time series visualization.

 28 Hours

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