Data Visualization Basics

Data visualization transforms raw data into visual formats like charts and graphs, making insights accessible. It’s a key skill in data science. This article covers chart types, design principles, an example, and uses.

Common Chart Types

• Bar Chart: Compare categories (e.g., sales by region).
• Line Chart: Show trends (e.g., stock prices).
• Scatter Plot: Display relationships (e.g., height vs. weight).
• Pie Chart: Show proportions (e.g., market share).

Design Principles

• Clarity: Avoid clutter, use labels.
• Accuracy: Scale axes properly.
• Color: Use distinct, meaningful colors.

Ensures effective communication.

Example Visualization

Data: Monthly sales {Jan: 100, Feb: 120, Mar: 150}:

• Type: Line chart.
• X-axis: Months, Y-axis: Sales.
• Trend: Upward, clear growth.

Visualizes performance easily.

Applications

Used in:

• Business: Dashboards for KPIs.
• Science: Plotting research data.
• Media: Infographics for storytelling.

Enhances understanding.