---
esco_uri: http://data.europa.eu/esco/skill/93aaf65c-5aad-4e6f-a7ba-ce9bf7c0e6e6
esco_label: "information categorisation"
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
---

# information categorisation — Data Scientist

For a Data Scientist, 'information categorisation' isn’t just about filing things neatly; it's the bedrock of feature engineering and data understanding. Daily tasks involve classifying raw data – text from customer reviews (sentiment analysis categories), images (object detection labels), sensor readings (anomaly/normal behaviour) – into meaningful groups *before* any modelling happens. This means defining those 'clearly defined purposes' from the ESCO definition upfront: are we building a recommendation engine? Detecting fraud? Predicting equipment failure? The chosen categories directly impact model performance and interpretability. Think of it as translating messy reality into something algorithms can work with.

## Weitere Anreicherung

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