# Data Visualization

> Data visualization techniques

- Skill: `ffsshhttiikk/data-visualization` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ffsshhttiikk/data-visualization`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ffsshhttiikk/data-visualization/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: ffsshhttiikk (https://skillmd.com/u/ffsshhttiikk)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/ffsshhttiikk/data-visualization

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## 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