Plainbook: Data Science, in Plain Language

📰 ArXiv cs.AI

Learn how Plainbook makes data science accessible to non-coders using natural language notebooks

intermediate Published 8 Jul 2026

Action Steps

Read the Plainbook abstract to understand its purpose and functionality
Explore the Plainbook interface to learn how natural language is used to create and share notebooks
Create a Plainbook notebook using plain language to analyze a sample dataset and share results with non-technical colleagues
Compare the accessibility of Plainbook notebooks to traditional Jupyter Notebooks for non-coders
Apply Plainbook to a real-world data analysis project to evaluate its effectiveness in communicating insights to a broader audience

Who Needs to Know This

Data scientists and researchers can use Plainbook to share insights with non-technical team members, such as policymakers or business stakeholders, who need to understand data analysis results without requiring coding knowledge

Key Insight

💡 Plainbook uses natural language to make data science more accessible to non-coders, promoting broader understanding and collaboration

Key Takeaways

Learn how Plainbook makes data science accessible to non-coders using natural language notebooks

Full Article

Title: Plainbook: Data Science, in Plain Language

Abstract:
arXiv:2607.05717v1 Announce Type: cross Abstract: Jupyter Notebooks have become widely adopted in data science, as they allow the sharing of reproducible computational analysis. They are, however, accessible only to people who understand computer code. To reach the broader audience of scientists interested in data analysis and computation, but unfamiliar with code, we introduce Plainbook, notebooks centered on natural language rather than code. Plainbook is based on two principles: promote the n

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