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Course Outline
Introduction
- 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
Troubleshooting
Summary and Next Steps
Requirements
- An understanding of data science concepts
- Python programming experience
Audience
- Data analysts
- Data scientists
14 Hours
Testimonials (3)
I genuinely enjoyed the lots of labs and practices.
Vivian Feng - Destination Canada
Course - Data Analysis with SQL, Python and Spotfire
L'adaptation parfaite à mon besoin
Thomas - AXA Wealth Services
Course - TIBCO for Developers
Spora dawka naprawdę praktycznej wiedzy