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Python Chart Courses

Python Chart for Beginners

1. Introduction to Data Visualization in Python
2. Installing and Setting Up Matplotlib
3. Line Charts using Matplotlib
4. Bar and Column Charts using Matplotlib
5. Pie and Donut Charts using Matplotlib
6. Histograms and Frequency Distributions
7. Box Plot and Violin Plot Comparisons
8. Scatter Plots and Trend Analysis
9. Area Charts and Stacked Area Charts
10. Heatmaps for Correlation and Matrix Data
11. 3D Charts using Matplotlib
12. Chart Styling, Themes, and Color Palettes
13. Creating Charts with Seaborn
14. Regression and Statistical Plots using Seaborn
15. Interactive Charts using Plotly
16. Animated Charts with Plotly Express
17. Geospatial Maps with Plotly and GeoJSON
18. Dashboards and Interactive Apps using Plotly Dash
19. Exporting Charts to PDF, PNG, and Web
20. Choosing the Right Chart for the Right Data

Advanced Python Charts

1. Advanced Matplotlib Architecture and Custom Rendering
2. Object-Oriented Matplotlib vs Pyplot API
3. Multi-Axis, Twin-Axis, and Broken Axis Charts
4. Advanced Styling with Custom Themes and Fonts
5. Color Theory and Dynamic Gradient Coloring
6. Custom Annotations, Shapes, and Overlay Graphics
7. Creating Reusable Chart Components and Templates
8. High-Performance Large Dataset Plotting Techniques
9. Interactive Visualizations with Plotly Advanced Features
10. Plotly Subplots and Synchronized Interactions
11. Network Graph Visualizations in Python (NetworkX + Plotly)
12. Time-Series Visualizations (Zoom, Pan, Hover, Forecasts)
13. Animated Transition Charts and Storytelling Visuals
14. Real-Time Streaming Data Charts (WebSockets & APIs)
15. Advanced Geospatial Mapping (Choropleth, Tiles, Layers)
16. Machine Learning Visualization (Clusters, Model Evaluation)
17. Financial Charts (Candlesticks, Indicators, Volume Maps)
18. Custom Widgets and Dashboards Using Plotly Dash
19. Publishing Visualizations to Web Apps (Flask, FastAPI, Dash)
20. Building Your Own Visualization Library Concepts (Render Pipelines)