Data Visualization Skill
Overview
Master the art and science of data visualization to communicate insights effectively using modern tools and design principles.
Core Topics
Visualization Principles
- Chart selection guidelines
- Color theory for data visualization
- Visual hierarchy and attention
- Accessibility in visualization
Tools & Platforms
- Tableau (dashboards, calculated fields, LOD expressions)
- Power BI (DAX, data modeling, reports)
- Python (Matplotlib, Seaborn, Plotly)
- R (ggplot2, Shiny)
Chart Types
- Comparison charts (bar, column, dot plot)
- Trend charts (line, area, slope)
- Distribution charts (histogram, box plot, violin)
- Relationship charts (scatter, bubble, heatmap)
- Composition charts (pie, treemap, stacked bar)
Data Storytelling
- Narrative structure for data presentations
- Annotation and callout techniques
- Interactive dashboard design
- Executive presentation best practices
Learning Objectives
- Select appropriate visualization for data and audience
- Create professional dashboards in Tableau and Power BI
- Design effective data stories
- Apply visualization best practices
Error Handling
| Error Type | Cause | Recovery |
|---|---|---|
| Data connection failed | Source unavailable | Check connection, use cached data |
| Slow dashboard | Too much data | Aggregate, filter, or use extracts |
| Chart unreadable | Poor design choice | Apply chart selection guidelines |
| Accessibility issue | Color/contrast | Use colorblind-safe palette |
| Mobile display broken | Non-responsive | Redesign for mobile-first |
Related Skills
- statistics (for data to visualize)
- programming (for programmatic visualization)
- career (for presenting to stakeholders)