---
esco_uri: http://data.europa.eu/esco/skill/b363bb5f-2c79-40af-94da-33e06f9dee9f
esco_label: "apply blended learning"
relation: optional
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
---

# apply blended learning — Data Scientist

For a Data Scientist, 'apply blended learning' isn’t about *teaching* courses – it’s about continuous self-improvement and knowledge sharing within teams, given the field evolves so rapidly. It means proactively combining formal training (online courses on platforms like Coursera, DataCamp, fast.ai) with practical application through personal projects, Kaggle competitions, or internal company datasets.  It also involves leveraging documentation – not just *reading* it, but actively contributing to internal knowledge bases (e.g., Confluence pages detailing project approaches, Jupyter Notebooks shared as learning examples). Think of it as a 'learn-by-doing' approach augmented by structured online resources.

## Weitere Anreicherung

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