---
title: "Jupyter | Skills We Assign For | Azendo"
description: "Jupyter notebooks — exploration versus production, reproducibility, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/jupyter/"
---

[Skills](https://azendo.co/skills/) Analytics and BI 

# Jupyter.

Jupyter provides notebook interfaces combining code, output and narrative in one document. It is the standard environment for exploratory data work, and a poor one for anything that needs to run reliably.

## Where Jupyter fits on a long engagement.

For exploration, notebooks are genuinely the right tool. Iterating on a transformation while seeing the result, keeping the reasoning beside the code, and sharing an analysis someone else can follow are all things a script does worse.

Out-of-order execution is the property that makes them unreliable. A notebook's state depends on the sequence cells were run in, not on their order in the document, so a notebook that produces a result on one machine can produce a different one elsewhere. A notebook that has not been restarted and run top to bottom is not reproducible.

## What an assigned team does with Jupyter.

The recurring organisational problem is notebooks in production. A pipeline that exists as a notebook someone runs each morning is a person with a habit rather than a system, and it fails the day they are unavailable.

Moving that work into tested, scheduled jobs is the transition that makes analytics dependable, assigned under [data engineering outsourcing](https://azendo.co/services/data-engineering/).

## What we use Jupyter for.

* Exploration with the reasoning attached Analysis someone else can follow, rather than a script with no narrative.
* Reproducibility enforced Restart-and-run-all as a rule, because execution order is otherwise invisible.
* Promotion out of the notebook Recurring work converted into scheduled jobs that do not depend on a person.

## How Jupyter capacity is assigned.

Analytical capacity is assigned with a clear line between exploration and production, so recurring work does not remain in a notebook indefinitely.

## Roles we assign Jupyter for

* [Data Scientist Data science](https://azendo.co/services/data-science/data-scientist/)

## Service lines it sits in

* [Data science](https://azendo.co/services/data-science/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in analytics and bi

* [Looker — skill we assign for](https://azendo.co/skills/looker/)
* [Power BI — skill we assign for](https://azendo.co/skills/power-bi/)
* [Tableau — skill we assign for](https://azendo.co/skills/tableau/)
* [semantic layers — skill we assign for](https://azendo.co/skills/semantic-layers/)
* [A/B testing — skill we assign for](https://azendo.co/skills/a-b-testing/)
* [experimentation design — skill we assign for](https://azendo.co/skills/experimentation-design/)
* [causal inference — skill we assign for](https://azendo.co/skills/causal-inference/)
* [cohort analysis — skill we assign for](https://azendo.co/skills/cohort-analysis/)

## Tell us what your roadmap needs Jupyter for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)