📊 Understanding Data Visualization

Repository type: SQL Portfolio Project
Training: Data Analyst Career Track, DataCamp
Project focus: Data Visualization Theory, Best Practices & Visual Analytics

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🎯 Purpose

This project demonstrates how thoughtful visual design improves insight and interpretation in real-world datasets. Each chapter translates theoretical best practices into hands-on exercises, grounded in business use cases and analytical thinking.

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📦 Repository Structure

├── data projects/             # Real-world inspired data investigations
├── docs/                      # Markdown-based learning guides & documentation
│   ├── Visualizing-distributions.md
│   ├── Visualizing-two-variables.md
│   ├── The-color-and-the-shape.md
│   ├── 99-problems-but-a-plot.md
│   ├── chart-type-guide.md
│   ├── data-communication-concepts.md
│   ├── reporting-structures.md
│   ├── audience-mapping-guide.md
│   ├── presentation-tips.md
├── visuals/                   # Plots and graphics used in exercises & modules
│   ├── Chart Type Guide/
│   ├── Visualizing Distributions/
│   ├── Visualizing Two Variables/
│   ├── 99 Problems But a Plot Ain’t One/
│   ├── Chart Type Guide/
├── README.md                  # Repository overview

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✅ Learning Outcomes

• Select appropriate chart types for different data and analytical goals
• Identify and avoid misleading plots through design best practices
• Apply color, shape, scale, and layout to enhance clarity and insight
• Translate raw data into stakeholder-ready visual stories
• Communicate insights clearly through dashboards, reports, and presentations

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📂 Topics Covered

• 📈 Distribution analysis of income, education, and consumption
• 🧭 Survey data, health metrics, and public sentiment visualizations
• 🐾 Animal tracking, stock market trends, COVID-19 cases
• 📊 Dashboard design, OKRs, KPI reporting, and stakeholder targeting

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🛠️ Tools & Format

• SQL (via DataCamp SQL workspace)
• Markdown for theory and reflection
• GitHub used as a professional communication medium and portfolio

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📁 Explore the docs/ folder for all chapter-based walkthroughs and concept files
🖼️ Explore visuals/ for output graphics from each learning module
📂 View data projects/ to follow applied analytics scenarios

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“Data graphics should draw the viewer's attention to the sense and substance, not something else.”
— Edward Tufte

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“Clarity trumps cleverness. A chart is only as good as the insight it enables.”
— Inspired by Tufte & Few