Megan Lieu - Collaborate with your team using data science notebooks | PyData Global 2023

Notebooks bridge the gap between usability and power - offering accessibility like spreadsheets but with code scalability

Key collaboration principles for notebooks:

• Never use local files
• Notebooks should not be read-only
• Implement robust version control
• Include explicit documentation and requirements
• Enable feedback mechanisms between collaborators

Modern notebooks should support:

• Multiple languages (SQL, Python, R) in the same notebook
• Cloud-based environments for scalability
• Integrations with data sources and ML tools
• Interactive visualizations
• Easy sharing and permissions management

Best practices for notebook organization:

• Place all inputs at the top
• Document experiments as you go
• Split development/production environments
• Include package requirements
• Parameterize notebooks for reproducibility
• Implement continuous integration

Data democratization benefits:

• Enables collaboration between technical and non-technical teams
• Supports citizen data scientists
• Makes insights discoverable throughout organizations
• Allows domain knowledge integration
• Facilitates feedback loops between teams

Modern features needed for effective collaboration:

• Real-time multi-user editing
• Version control and change tracking
• Native data source integrations
• One-click deployment to apps/dashboards
• Asynchronous feedback capabilities
• Access controls and permissions