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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
- Familiarity with data science concepts.
- Experience with Python programming.
Audience
- Data analysts.
- Data scientists.
14 Hours
Testimonials (2)
workshops, practical examples
Martin Stuparek - Orange Slovensko, a.s.
Course - Monitoring with Grafana
The content is very helpful, and the trainer makes it more easier to understand