Managing code and data in data science projects requires effective version control to ensure consistency and collaboration.

Balancing code and data in your data science projects can be tricky, but mastering version control can make your workflow smoother. Here's how to manage it effectively:

• Use Git for code: Git is a powerful tool that helps track changes, collaborate with others, and revert to previous versions.

• Employ DVC (Data Version Control): DVC manages large datasets and models, integrating seamlessly with Git.

• Automate with CI/CD (Continuous Integration/Continuous Deployment): Set up automated testing and deployment to minimize manual errors and streamline updates.

How do you handle version control in your data science projects? Share your strategies.