Introducing notebooks:
Loved by 100,000s of data professionals
Describe what you want to do and Deepnote will automagically query, analyze, and interpret data for you. No coding skills necessary.
Deepnote understands your business context and your data stack to deliver high-quality analyses fast.
Refactor code
Generate code
Explain code
Code completion
Debug & fix
Arrange into custom layouts
Hide code blocks
Add input blocks and buttons
Deepnote runs seamlessly in the cloud, making environment management and collaboration with your team a non-issue. And sharing work is as easy as sending a link or email invite.
Product manager
Data scientist
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I knew I’d made the right decision when I started getting feedback from data scientists about how delightful their user experience was.
Director of Engineering
Working in Deepnote is like code review and rapid prototyping at the same time, saving valuable time in the iteration cycles.
CTO
I have been trying out Deepnote for running shared Jupyter notebooks, and I’m very impressed by how smooth and powerful the whole experience is.
Chief Scientist
Collaborative data analysis where team members can freely share their work and get feedback… This made the analysis workflow much faster.
Data Analyst
Delightful user experience reminds me of Superhuman with the command palette and constant reminders of how to use hotkeys to work more efficiently.
Data Scientist
I just love how SQL is now a first-class citizen in Deepnote notebooks! 🔥 It is SO easy to query databases!
Developer
Deepnote enables us to bring people into the phase of data science that’s all about experimentation, helps them understand our processes, and encourages folks to leverage data science in even more ways.
Sr. Manager, Data Science
I enjoy writing code on weekends (mostly hack around with data analysis & machine learning in Python). This year I moved my dev environment fully to Deepnote, and I’m never going back. The future of coding is browser-based.
CEO & co-founder
Used by the next generation of data analysts and data scientists.
Learn more ->Deepnote was incredibly easy to set up and allows us to start new notebooks in seconds. Working together with Deepnote gives us a great window into the ways candidates approach the interview problem.
Head of Data Science
Since metrics require a lot of input from subject matter experts, data consumers, and business stakeholders to define and align on definitions, we needed a collaborative layer where we could get immediate feedback.
Head of Analytics Engineering
I knew I’d made the right decision when I started getting feedback from data scientists about how delightful their user experience was.
Director of Engineering
Working in Deepnote is like code review and rapid prototyping at the same time, saving valuable time in the iteration cycles.
CTO
I have been trying out Deepnote for running shared Jupyter notebooks, and I’m very impressed by how smooth and powerful the whole experience is.
Chief Scientist
Collaborative data analysis where team members can freely share their work and get feedback… This made the analysis workflow much faster.
Data Analyst
Delightful user experience reminds me of Superhuman with the command palette and constant reminders of how to use hotkeys to work more efficiently.
Data Scientist
I just love how SQL is now a first-class citizen in Deepnote notebooks! 🔥 It is SO easy to query databases!
Developer
Deepnote enables us to bring people into the phase of data science that’s all about experimentation, helps them understand our processes, and encourages folks to leverage data science in even more ways.
Sr. Manager, Data Science
I enjoy writing code on weekends (mostly hack around with data analysis & machine learning in Python). This year I moved my dev environment fully to Deepnote, and I’m never going back. The future of coding is browser-based.
CEO & co-founder
Used by the next generation of data analysts and data scientists.
Learn more ->Deepnote was incredibly easy to set up and allows us to start new notebooks in seconds. Working together with Deepnote gives us a great window into the ways candidates approach the interview problem.
Head of Data Science
Since metrics require a lot of input from subject matter experts, data consumers, and business stakeholders to define and align on definitions, we needed a collaborative layer where we could get immediate feedback.
Head of Analytics Engineering