Python has become the go-to language for data analysis. Here’s why and how to get started:

Essential Libraries

• Pandas for data manipulation
• NumPy for numerical computing
• Matplotlib/Seaborn for visualization
• Scikit-learn for machine learning

Common Workflow

1. Data Loading and Cleaning
2. Exploratory Data Analysis
3. Feature Engineering
4. Model Building
5. Results Visualization

Best Practices

• Use virtual environments
• Document your code
• Write reusable functions
• Use list comprehensions
• Leverage vectorized operations

Python’s rich ecosystem makes it perfect for data analysis tasks of any scale.