data-visualization

## What I do - Create effective data visualizations - Design dashboards and reports - Choose appropriate chart types - Implement interactive visualizations - Apply color theory and design principles - Tell stories with data - Build self-service analytics ## When to use me Use me when: - Presenting data to stakeholders - Building analytics dashboards - Exploring data patterns - Creating reports and infographics - Communicating insights visually ## Key Concepts ### Chart Selection Guide | Relationship | Chart Type | |--------------|------------| | Comparison | Bar, Column, Grouped | | Distribution | Histogram, Box Plot | | Composition | Pie, Stacked Bar, Treemap | | Trend | Line, Area | | Correlation | Scatter Plot | | Geographic | Choropleth, Map | ### Python Visualization Stack ```python import matplotlib.pyplot as plt import seaborn as sns import plotly.express as px # Seaborn for statistical visualization sns.set_theme(style="whitegrid") tips = sns.load_dataset("tips") fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # Distribution plot sns.histplot(data=tips, x="total_bill", hue="day", ax=axes[0], kde=True) # Relationship plot sns.scatterplot(data=tips, x="total_bill", y="tip", hue="smoker", size="size", ax=axes[1]) plt.tight_layout() plt.show() # Interactive with Plotly fig = px.scatter(tips, x="total_bill", y="tip", color="smoker", size="size", title="Tips Analysis") fig.show() ``` ### Dashboard Design Principles - **Clarity**: Clear purpose, minimal clutter - **Hierarchy**: Most important metrics prominent - **Consistency**: Unified color, fonts, layout - **Interactivity**: Allow exploration - **Responsiveness**: Work on all devices ### BI Tools - **Tableau**: Enterprise, powerful - **Power BI**: Microsoft ecosystem - **Looker**: Data modeling (LookML) - **Metabase**: Open-source, simple - **Grafana**: Metrics and monitoring