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HIGH-IMPACT VISUAL STORYTELLING: MATPLOTLIB & SEABORN
Build clean, executive-ready charts: line graphs, subplots, statistical distributions, and correlation heatmaps.
Module Content
Matplotlib Foundations: Creating & Customizing Plots
Introduction to Matplotlib: line plots, styles, formatting, titles, axis labels, legends, and exporting figures.
Matplotlib Subplots & The Object-Oriented Interface
Master the Object-Oriented Matplotlib interface: fig, ax = plt.subplots(), multi-panel dashboards, and shared axes.
Statistical Visualizations with Seaborn
Create automated statistical charts directly from DataFrames: barplot, countplot, boxplot, and histplot.
Visualizing Multidimensional Data: Scatter Plots, Color Encodings & Log Scales
Guide to multi-variable scatter plots, color mapping (cmap), size encodings, and logarithmic scale adjustments.
Advanced Seaborn: The Complete Statistical Charting Cheatsheet
Comprehensive cheatsheet for histplot (with KDE), boxplots, violinplots, pairplots, and FacetGrid matrices.
Designing Executive Correlation Heatmaps
Guide to computing df.corr(), masking diagonal triangles, and styling diverging correlation heatmaps with Seaborn.
QUIZ: Data Visualization with Matplotlib & Seaborn
15-question MCQ assessment testing plot customization, subplots, boxplots, pairplots, and heatmaps.