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Duration 14 hours
Course Outline
Introduction to Google Colab for Visualization
- Overview of Google Colab capabilities
- Initial setup of the Google Colab environment
- Exploring the Google Colab user interface
Foundations of Data Visualization
- The role and value of data visualization
- Introduction to Python-based visualization libraries
Fundamental Plotting with Matplotlib
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Generating basic plots
- Line graphs
- Bar charts
- Pie charts
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Plot customization
- Adding titles, axis labels, and legends
- Modifying colors, styles, and themes
Advanced Matplotlib Techniques
- Managing subplots and multi-plot layouts
- Incorporating annotations for emphasis
- Methods for saving and exporting visualizations
Introduction to Seaborn
- Overview of the Seaborn library
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Creating statistical visualizations
- Distribution plots
- Regression plots
- Categorical plots
Customizing Seaborn Visualizations
- Applying aesthetics and theme settings
- Implementing advanced customization options
- Integrating Seaborn with Matplotlib for combined results
Visualizing Real-world Datasets
- Importing external data sources
- Data cleaning and preprocessing techniques
- Visualizing complex and large-scale data structures
Collaborative Visualization Workflows
- Sharing notebooks and collaborating on projects
- Leveraging real-time collaboration tools
- Best practices for team-based visualization projects
Best Practices and Tips
- Techniques for effective data presentation
- Avoiding common errors in data visualization
- Enhancing visual clarity and appeal
Summary and Future Directions
Requirements
- Foundational understanding of Python programming
- Basic grasp of fundamental 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