jm + notebooks   1

Reproducible research: Stripe’s approach to data science
This is intriguing -- using Jupyter notebooks to embody data analysis work, and ensure it's reproducible, which brings better rigour similarly to how unit tests improve coding. I must try this.
Reproducibility makes data science at Stripe feel like working on GitHub, where anyone can obtain and extend others’ work. Instead of islands of analysis, we share our research in a central repository of knowledge. This makes it dramatically easier for anyone on our team to work with our data science research, encouraging independent exploration.

We approach our analyses with the same rigor we apply to production code: our reports feel more like finished products, research is fleshed out and easy to understand, and there are clear programmatic steps from start to finish for every analysis.
stripe  coding  data-science  reproducability  science  jupyter  notebooks  analysis  data  experiments 
november 2016 by jm

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