# Eugo for data scientists

Go from a single-machine notebook to distributed analysis over datasets that no longer fit in memory.

Source: https://university.eugo.io/learning-paths/data-scientist

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## Who it's for

You already work in Python and pandas. You have hit the point where a dataset takes hours to process, or no longer fits on one machine, and you would rather not become an infrastructure engineer to fix it. This path takes you from your first session to distributing a real dataframe workload across a cluster.

## Courses, in order

1. [Getting started with Eugo](https://university.eugo.io/courses/getting-started-with-eugo) — What Eugo is, what problem it solves, and how to run your first workload on a cluster.
2. [EugoIDE essentials](https://university.eugo.io/courses/eugoide-essentials) — Work fluently in the browser-based notebook environment where your Python actually runs.
3. [Distributed Python with eugo.hpc](https://university.eugo.io/courses/distributed-python-with-eugo-hpc) — Turn ordinary Python into distributed work with a decorator, then scale it across a cluster.
4. [Scaling a real workload](https://university.eugo.io/courses/scaling-a-real-workload) — Take a single-machine geospatial pipeline to a cluster, and find out where the time actually goes.

## Good to know

### What you need first

Working Python, and familiarity with pandas or NumPy. No prior distributed-computing experience is assumed. The path introduces tasks, futures, and actors from scratch.

### Access

You need a workspace to follow along. If your organization has not set one up, the workspace admin path covers that side.