1. Introduction to Advanced Data Visualization:
The significance of advanced data visualization in conveying complex patterns and insights. Overview of the topics to be covered in the course. 2. Review of Basic Visualization Techniques:
A brief review of basic data visualization concepts and tools, including Matplotlib and Seaborn. 3. Heatmaps:
What are heatmaps and when to use them. Creating heatmaps with Matplotlib and Seaborn. Customizing color maps and adding annotations. Correlation heatmaps for exploring relationships between variables. 4. Histograms:
Understanding histograms and their use in exploring data distributions. Creating histograms with Matplotlib and Seaborn. Customizing bin sizes, colors, and styles. Overlaying multiple histograms for comparison. 5. Box Plots:
Introduction to box plots and their role in visualizing data distributions and outliers. Creating box plots with Matplotlib and Seaborn. Customizing whiskers, outliers, and orientation. Grouped and stacked box plots. 6. Violin Plots:
Understanding violin plots and how they combine box plots and kernel density estimation. Creating violin plots with Matplotlib and Seaborn. Customizing the width, shape, and colors. 7. Interactive Data Visualization with Plotly:
Introduction to Plotly as an interactive visualization library. Creating interactive scatter plots, line charts, and bar charts with Plotly. Adding interactive elements like tooltips and zooming. Customizing Plotly layouts and themes. 8. Interactive Heatmaps with Plotly:
Building interactive heatmaps with Plotly. Adding interactivity to heatmaps, such as tooltips and zooming. Customizing color scales and annotations. 9. Advanced Plotly Visualizations:
Creating 3D visualizations, polar plots, and choropleth maps with Plotly. Building complex dashboards and interactive applications using Plotly Dash (if time allows). 10. Real-world Projects and Case Studies: - Practical exercises and projects that involve creating advanced visualizations from real-world datasets. - Case studies demonstrating the use of advanced visualizations in data analysis and storytelling.
11. Best Practices in Advanced Data Visualization: - Guidelines for designing effective and user-friendly advanced visualizations. - Addressing challenges and considerations specific to interactive visualizations.
12. Ethical Considerations in Data Visualization: - Addressing ethical concerns related to advanced data visualization, including bias and misrepresentation in interactive visualizations.