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Course Outline
Getting to Know Google Colab for Visualization
- Introduction to Google Colab
- Configuring Google Colab
- Exploring the Google Colab interface
Foundations of Data Visualization
- The significance of data visualization
- Overview of Python visualization libraries
Fundamental Plotting with Matplotlib
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Generating basic plots
- Line graphs
- Bar charts
- Pie charts
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Adjusting plot attributes
- Titles, labels, and legends
- Colors, styles, and themes
Sophisticated Plotting with Matplotlib
- Subplots and multi-figure arrangements
- Using annotations effectively
- Saving and exporting visualizations
Discovering Seaborn
- Seaborn fundamentals
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Building statistical plots
- Distribution plots
- Regression plots
- Categorical plots
Customizing Seaborn Visualizations
- Aesthetic considerations and themes
- Advanced customization options
- Integrating Seaborn with Matplotlib
Managing and Visualizing Real-world Data
- Loading datasets
- Data cleaning and preparation
- Visualizing intricate datasets
Team-Based Visualization Projects
- Sharing and working together on notebooks
- Real-time collaboration capabilities
- Best practices for collaborative efforts
Best Practices and Pro Tips
- Efficient data visualization strategies
- Preventing common visualization errors
- Improving visual aesthetics and clarity
Conclusion and Future Directions
Requirements
- Foundational understanding of Python programming
- Basic grasp of data concepts
Target Audience
- Data scientists
- Data professionals
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