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
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
1---2name: data-visualization3description: Data visualization techniques4license: MIT5---67## What I do89- Create effective data visualizations10- Design dashboards and reports11- Choose appropriate chart types12- Implement interactive visualizations13- Apply color theory and design principles14- Tell stories with data15- Build self-service analytics1617## When to use me1819Use me when:20- Presenting data to stakeholders21- Building analytics dashboards22- Exploring data patterns23- Creating reports and infographics24- Communicating insights visually2526## Key Concepts2728### Chart Selection Guide29| Relationship | Chart Type |30|--------------|------------|31| Comparison | Bar, Column, Grouped |32| Distribution | Histogram, Box Plot |33| Composition | Pie, Stacked Bar, Treemap |34| Trend | Line, Area |35| Correlation | Scatter Plot |36| Geographic | Choropleth, Map |3738### Python Visualization Stack39```python40import matplotlib.pyplot as plt41import seaborn as sns42import plotly.express as px4344# Seaborn for statistical visualization45sns.set_theme(style="whitegrid")46tips = sns.load_dataset("tips")4748fig, axes = plt.subplots(1, 2, figsize=(12, 5))4950# Distribution plot51sns.histplot(data=tips, x="total_bill", hue="day", 52 ax=axes[0], kde=True)5354# Relationship plot55sns.scatterplot(data=tips, x="total_bill", y="tip", 56 hue="smoker", size="size", ax=axes[1])5758plt.tight_layout()59plt.show()6061# Interactive with Plotly62fig = px.scatter(tips, x="total_bill", y="tip", 63 color="smoker", size="size",64 title="Tips Analysis")65fig.show()66```6768### Dashboard Design Principles69- **Clarity**: Clear purpose, minimal clutter70- **Hierarchy**: Most important metrics prominent71- **Consistency**: Unified color, fonts, layout72- **Interactivity**: Allow exploration73- **Responsiveness**: Work on all devices7475### BI Tools76- **Tableau**: Enterprise, powerful77- **Power BI**: Microsoft ecosystem78- **Looker**: Data modeling (LookML)79- **Metabase**: Open-source, simple80- **Grafana**: Metrics and monitoring
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