Program Structure

Module 1: Visualization Basics + Setup

• What makes a good chart: clarity, honesty, comparison.
• Notebook workflow: Jupyter/Colab setup and file handling.
• Quick EDA: head/info/describe and missing values.
• Chart selection guide: when to use which plot.

Module 2: Pandas for Exploratory Analysis

• Import/export: CSV/Excel basics.
• Cleaning: types, nulls, duplicates, outliers (basic handling).
• Groupby summaries: counts, means, percent share.
• Time-series basics: parsing dates and trend summaries.

Module 3: Matplotlib Fundamentals

• Line and bar charts for trends and comparisons.
• Scatter plots for relationships; annotations (intro).
• Histograms for distributions; bin choice concepts.
• Subplots, figure size, titles, labels, legends.

Module 4: Seaborn for Statistical Visualization

• Distribution plots: hist/kde, box, violin.
• Categorical plots: countplot, barplot, boxplot by group.
• Relationship plots: regplot, lmplot, pairplot (intro).
• Heatmaps and correlation matrices (with interpretation cautions).

Module 5: Customization & Export

• Styling: fonts, ticks, grids, color palettes (basics).
• Axes control: limits, scales (log), formatting numbers.
• Saving plots: DPI, file formats (PNG/SVG/PDF).
• Reusable plotting functions for consistent visuals.

Module 6: Visual Storytelling & Dashboards (Simple)

• Building a narrative: question → chart → insight.
• Multi-chart layouts: small dashboards in notebooks.
• Highlighting key points: callouts and reference lines (intro).
• Common mistakes: clutter, misleading scales, too many colors.

Final Project

• Pick a dataset (sales, finance, health, research, or public data).
• Deliverables: EDA notebook + 8–12 charts + insights summary.
• Optional: export a short PDF report with plots.