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Course Outline

Introduction

  • Overview of core data visualisation concepts
  • Visualisation techniques and tools

Getting Started

  • Installing 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, axes, and labels
  • Combining multiple plots
  • Creating bar charts, pie charts, and histograms

Building Complex Visualisations with Seaborn

  • Visualising Pandas DataFrames
  • Plotting bars and aggregates
  • Implementing KDE, box, and violin plots
  • Analysing statistical distributions

Making Visualisations Interactive with Bokeh

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

Visualising Geospatial Data with Folium

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

Troubleshooting

Summary and Next Steps

Requirements

  • An understanding of data science concepts
  • Python programming experience

Audience

  • Data analysts
  • Data scientists
 14 Hours

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