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Megan Lieu - Collaborate with your team using data science notebooks | PyData Global 2023
Collaborate with your team using data science notebooks, a crucial tool for facilitating teamwork and improving communication between data scientists, engineers, and business stakeholders.
- Collaboration in data science teams is crucial, and data science notebooks can facilitate it.
- Jupyter notebooks are used by 80% of data scientists, but others may use alternative tools.
- Data science notebooks can be used by a variety of personas, including data scientists, engineers, and business stakeholders.
- To collaborate effectively, teams should version their notebooks, write robust tests, and use external link sharing.
- Data science notebooks can include a variety of blocks, such as input, Python, and SQL blocks.
- DeepNote is a data science notebook platform that offers integrations with hosted experimentation tracking tools and allows users to combine multiple languages in a single notebook.
- To improve collaboration, teams should log all their experiments, use a robust testing framework, and make their notebooks reproducible.
- Data science notebooks can facilitate the development of apps or dashboards with a click of a button.
- The rise of citizen data scientists requires data science teams to be able to collaborate with non-technical teams and facilitate conversations around data and insights.
- Best practices for using data science notebooks include listing packages in a requirements text file, using external link sharing, and making notebooks reproducible.
- Collaborative data science notebooks can help unlock the full potential of data science teams and facilitate collaboration between data scientists, engineers, and business stakeholders.