Course Outline


  • Overview of data visualization core concepts
  • Visualization techniques and tools

Getting Started

  • Installing the Python libraries (Matplotlib, Seaborn, Bokeh, and Folium)
  • Use cases and practical examples

Creating Line Plots and Graphs with Matplotlib

  • Creating basic line plots
  • Adding styles, axis, and labels
  • Combining multiple plots
  • Creating bar charts, pie charts and histograms

Building Complex Visualizations with Seaborn

  • Visualizing Pandas DataFrame
  • Plotting bars and aggregates
  • Implementing KDE, Box, and Violin plots
  • Analyzing statistical distributions

Making Visualizations Interactive with Bokeh

  • Plotting with basic glyphs
  • Creating layouts for multiple visualizations
  • Styling and visual attributes
  • Adding interactivity (interactive legends, hover actions, and widgets)
  • Implementing linked selections

Visualizing Geospatial Data with Folium

  • Plotting interactive maps
  • Using layers and tiles
  • Adding markers and paths


Summary and Next Steps


  • An understanding of data science concepts
  • Python programming experience


  • Data analysts
  • Data scientists
  14 Hours


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