Course Outline

Introduction

  • Overview of core concepts in data visualization for government
  • Techniques and tools for effective visualization

Getting Started

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

Creating Line Plots and Graphs with Matplotlib

  • Developing basic line plots
  • Adding styles, axes, and labels to enhance clarity
  • Combining multiple plots for comprehensive data representation
  • Generating bar charts, pie charts, and histograms to visualize diverse datasets

Building Complex Visualizations with Seaborn

  • Visualizing Pandas DataFrame for government data analysis
  • Plotting bars and aggregates to summarize key information
  • Implementing KDE, Box, and Violin plots to analyze statistical distributions
  • Conducting in-depth statistical distribution analyses

Making Visualizations Interactive with Bokeh

  • Plotting with basic glyphs for dynamic data presentation
  • Creating layouts for multiple visualizations to support comprehensive analysis
  • Applying styling and visual attributes to enhance user experience
  • Adding interactivity (interactive legends, hover actions, and widgets) for enhanced engagement
  • Implementing linked selections to enable synchronized data exploration

Visualizing Geospatial Data with Folium

  • Plotting interactive maps for geospatial data analysis
  • Utilizing layers and tiles for detailed map customization
  • Adding markers and paths to highlight specific locations and routes

Troubleshooting

Summary and Next Steps

Requirements

  • An understanding of data science principles
  • Experience with Python programming

Audience for Government

  • Data Analysts
  • Data Scientists
 14 Hours

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