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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
Testimonials (1)
Real world knowledge from someone in the industry