30 karma · joined August 2, 2014
In general the main selling points are live collaboration (you can work on a notebook with you team as you'd do on a google doc), and integrations (you can plug-in your snowflake db, or s3 bucket or whatever, and have it connected for any further analysis, or a long-term training, etc.
For many non-software-developer data scientists, it's also easier to work in a cloud environment compared to installing stuff locally, and to version their notebooks in Deepnote instead of git. But this really depends on the particular workflow that one has.
Here's a public notebook in Deepnote if anyone wants to play around with the code or duplicate it: https://deepnote.com/project/Converting-Markdown-to-Epub-or-...
2 fun facts about Deepnote:
1. You can create a Custom environment by writing a Dockerfile with all the libraries you need to install and everytime you're in a need to re-use a similar functionality (e.g. convert yet another book to mobi), you can just fire it up and all will be preinstalled. https://docs.deepnote.com/environment/custom-environments
2. You can turn any notebook to a blogpost right away and publish within Deepnote directly.
Disclaimer: I'm a software engineer at Deepnote.
What would be even cooler is some kind of analysis of what jupyter notebooks usually contain, how big they are, what python libraries they use, what plotting library they use, etc.