1. Introduction to Data Visualization:
The importance of data visualization in data analysis and communication. Overview of Matplotlib and Seaborn as Python visualization libraries. 2. Setting Up the Environment:
Installing Matplotlib and Seaborn. Configuring plotting settings and styles. 3. Basic Plots:
3.1. Line Plots:
Creating line plots to visualize trends over time or continuous data. Customizing line styles, colors, and markers. 3.2. Bar Plots:
Creating bar plots for categorical data. Customizing bar colors, widths, and labels. Grouped and stacked bar plots. 3.3. Scatter Plots:
Creating scatter plots to explore relationships between two continuous variables. Adding labels, colors, and markers to data points. 4. Customizing Plots:
4.1. Titles, Labels, and Legends:
Adding titles and axis labels to plots. Creating and customizing legends. 4.2. Colors and Colormaps:
Choosing and customizing colors for plot elements. Working with colormaps to represent data. 4.3. Axes and Ticks:
Customizing axis scales, limits, and ticks. Creating secondary axes. 4.4. Annotations and Text:
Adding text, arrows, and annotations to highlight specific data points or regions. 5. Subplots and Multi-plot Layouts:
Creating multiple plots within a single figure. Customizing subplot arrangement and spacing. 6. Advanced Plot Types:
6.1. Heatmaps:
Creating heatmaps to visualize data density. Customizing color mapping and annotations. 6.2. Box Plots and Violin Plots:
Creating box plots and violin plots to show data distributions. Customizing whiskers, outliers, and orientation. 6.3. Pair Plots:
Generating pair plots for visualizing pairwise relationships in datasets. Customizing pair plot aesthetics. 7. Seaborn Themes and Styles:
Utilizing built-in themes and styles to improve the overall appearance of plots. 8. Saving and Exporting Plots:
Saving plots in various formats (PNG, JPEG, PDF). Exporting plots for use in reports and presentations. 9. Interactive Plots (Optional):
Introduction to interactive plotting libraries like Plotly for web-based visualizations. 10. Real-world Data Visualization Projects: - Practical exercises and projects that involve creating meaningful visualizations from real-world datasets.
11. Best Practices in Data Visualization: - Guidelines for creating effective, honest, and informative visualizations. - Avoiding common pitfalls and misrepresentations.
12. Ethical Considerations in Data Visualization: - Addressing ethical concerns related to data visualization, including bias and misleading representations.