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Course Outline
Foundations of Google Colab for Visualisation
- Summary of Google Colab capabilities
- Configuring the Google Colab environment
- Exploring the Google Colab user interface
Initial Steps in Data Visualisation
- The significance of data visualisation
- Overview of Python visualisation libraries
Fundamental Plotting with Matplotlib
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Generating basic charts
- Line graphs
- Bar diagrams
- Pie charts
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Modifying plot attributes
- Titles, axis labels, and legends
- Colour schemes, styles, and themes
Sophisticated Plotting with Matplotlib
- Managing subplots and multiple chart arrays
- Incorporating annotations
- Saving and exporting chart files
Introduction to Seaborn
- Overview of the Seaborn library
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Constructing statistical visuals
- Distribution plots
- Regression plots
- Categorical plots
Customising Seaborn Visuals
- Aesthetic adjustments and theme selection
- Advanced configuration options
- Integrating Seaborn with Matplotlib
Processing and Visualising Real-World Datasets
- Importing data sources
- Data cleansing and preparation
- Visualising intricate data structures
Collaborative Visualisation Projects
- Sharing and co-editing notebooks
- Features for real-time team collaboration
- Best practices for group projects
Recommendations and Best Practices
- Efficient data visualisation strategies
- Identifying and avoiding common visualisation errors
- Improving visual impact and clarity
Recap and Future Directions
Requirements
- Foundational understanding of Python programming
- General grasp of fundamental data concepts
Target Audience
- Data scientists
- Data specialists
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
