The Future of Data Science Notebooks is Collaborative
The Future of Data Science Notebooks is Collaborative


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.

Learning outcomes:
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.


Jakub Jurovych is a founder and CEO of Deepnote. As an engineer, Jakub built tools for JavaScript development and worked on Firefox DevTools. Jakub studied Computer Science at Cambridge University, focusing on Human-Computer Interaction, Machine Learning and Computer Vision. Jakub is a YC alumnus (2019) and ranked in Forbes 30 Under 30. In 2019, Jakub founded Deepnote, a new kind of data science notebook.

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