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Duration 14 hours
Course Outline
Introduction to Google Colab for Visualization
- Overview of Google Colab
- Setup of Google Colab
- Exploring the Google Colab interface
Getting Started with Data Visualization
- The role of data visualization
- Introduction to Python visualization libraries
Basic Plotting with Matplotlib
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Creating simple plots
- Line plots
- Bar charts
- Pie charts
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Modifying plot appearances
- Titles, labels, and legends
- Colors, styles, and themes
Advanced Plotting with Matplotlib
- Subplots and composite plots
- Managing annotations
- Saving and exporting visualizations
Introduction to Seaborn
- Overview of Seaborn
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Generating statistical plots
- Distribution plots
- Regression plots
- Categorical plots
Customizing Seaborn Plots
- Aesthetics and themes
- Advanced customization techniques
- Integrating Seaborn with Matplotlib
Handling and Visualizing Real-world Datasets
- Importing datasets
- Data cleaning and preparation
- Visualizing complex data structures
Collaborative Visualization Projects
- Sharing and co-authoring notebooks
- Real-time collaboration tools
- Best practices for team projects
Tips and Best Practices
- Effective visualization strategies
- Avoiding common visualization errors
- Improving visual impact and clarity
Summary and Next Steps
Requirements
- Foundational understanding of Python programming
- Basic grasp of core data concepts
Target Audience
- Data scientists
- Data professionals
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