Abstract: Ever since Mathematica was released over 30 years ago, notebooks evolved into the go-to-tool for data scientists across academia and the industry. However, many challenges remain — collaboration, reproducibility, versioning, missing interactivity, integrations with other systems. With expanding data roles and exploding remote work, the need for data science tooling that effectively fosters collaboration grows. In this talk, we review history of data science notebooks and discuss collaborative notebooks as the tool for the next generation of data scientists.
Overview of data science notebooks, their legacy and evolution.
Key trends and challenges in data science notebooks, including cross-functional and real-time collaboration, sharing, interactivity, versioning.
Perspective of what the world looks like for a modern data scientist that taps into the collaboration-native tooling and opportunities this opens up for their work.
Founder and CEO | DeepNote