If you perform EDA using jupyter notebooks, it’s really easy to share those results with some moderate interaction via a jupyter dashboard. Here are the basic steps:

1. Build the analysis, etc. Assuming this is done locally. Install the dashboard layout extension and lay out some sweet graphs. Optional: decorate some graph function with `@interact`.
2. Commit to github/bitbucket. An example: git commit -am "so much wow"; git push github exploratory.
3. Ssh to server. Pull commit down on the server.
4. Back to local. Scp the data files up to the server, in the same place they were locally. It comes in handy to have put them in a folder called `data`. This looks like `scp -r data myserver:project-dir/`.
5. Ssh back to server. Install virtualenv, all necessary libs (numpy, pandas, etc).
6. Install the dashboard layout (you definitely need it now, if you didn’t do the layout in step 1): https://github.com/jupyter/dashboards.
7. Enable widgets `jupyter nbextension enable –py –sys-prefix widgetsnbextension`.
8. Launch server, point browser, choose dashboard view, run notebook!

I’ve done this all in a screen environment to leave it running. Overhead should be minimal on the server. Before launching the notebook server, I put my necessary credentials into environment variables as well.

Another potentially important step is to password protect the server. This can be done by calling `jupyter notebook password`.

This way, you don’t need the other two dashboard pieces (the bundler or standalone server). Although if someone were to set those up, it could make this process even easier!

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