Data Visualization Overview
Content Coming Soon
This section is under development.
Why Visualize Data?
Raw numbers tell part of the story. Visualization reveals patterns, trends, and outliers that would be invisible in spreadsheets.
- Exploration: Discover insights you didn't know to look for
- Communication: Share findings with stakeholders
- Monitoring: Dashboards for real-time system health
- Decision Support: Data-driven choices
Topics to be covered:
- Chart Types: When to use line, bar, pie, scatter, etc.
- Libraries: D3.js, Chart.js, Plotly, ZingChart
- Dashboards: Building interactive analytics displays
- Best Practices: Color, labels, accessibility
- Real-time Updates: Live data visualization
Common Chart Types
- Line Charts: Trends over time (page views per day)
- Bar Charts: Comparing categories (browsers, countries)
- Pie/Donut: Parts of a whole (traffic sources)
- Scatter Plots: Relationships between variables
- Heat Maps: Density patterns (clicks on page)
- Tables/Grids: Detailed data exploration
Available Demos
Explore existing visualization examples:
Hello Data Viz → Database Grid Demo →