---
esco_uri: http://data.europa.eu/esco/skill/2b34764d-637c-48f5-aa70-31e15c965db6
esco_label: "operate open source software"
relation: essential
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# operate open source software — Data Scientist

For a Data Scientist, 'operating open source software' isn’t just *using* it – it’s deeply interwoven into almost every task. Daily work revolves around tools like Python (and its ecosystem: NumPy, Pandas, Scikit-learn), R, and increasingly, frameworks like TensorFlow, PyTorch, or Spark. This means being comfortable installing packages via `pip` or `conda`, managing virtual environments to avoid dependency conflicts, understanding how to contribute bug reports or feature requests on GitHub, and knowing when/how to fork a repository for experimentation. It's about leveraging the collective power of open source rather than building everything from scratch.

Beyond usage, awareness of licensing (MIT, Apache 2.0, GPL) is crucial. You need to understand what you can legally *do* with the software – especially when incorporating it into commercial products or sharing your own code.  Good practice includes documenting dependencies clearly in `requirements.txt` or similar files for reproducibility and collaborating effectively on projects using version control (Git). Knowing how to read basic open-source project documentation, understand issue trackers, and navigate community forums is also key.

A common pitfall is blindly trusting packages without understanding their provenance or maintenance status – security vulnerabilities are a real concern. Another is failing to manage environments properly, leading to 'it works on my machine' problems.  Good Data Scientists proactively check package dependencies for known issues, contribute back to the community when possible (even small documentation fixes!), and prioritize reproducible research through well-defined environments.

Ultimately, competence here looks like being a *responsible* consumer and potential contributor to the open source ecosystem. It’s not about becoming an expert in every tool, but having the foundational knowledge to quickly learn new ones, adapt existing code, and understand the implications of using (and sharing) open-source components within data science workflows.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
