---
esco_uri: http://data.europa.eu/esco/skill/07889c08-7220-47c8-96f7-6068fbea00dc
esco_label: "normalise data"
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
---

# normalise data — Data Scientist

As a Data Scientist, 'normalising data' isn’t about achieving strict database normal forms (1NF, 2NF etc.) – though understanding those principles *helps*. It's primarily about scaling and transforming features to ensure they don't disproportionately influence your models. Daily this means tasks like Min-Max scaling (bringing values between 0 & 1), Z-score standardisation (centering around zero with unit variance), or robust scaling for datasets with outliers. You’ll be using libraries like scikit-learn (`sklearn.preprocessing`) in Python, Pandas for initial data manipulation, and potentially Spark for large-scale transformations.  It's about preparing features so algorithms can learn effectively *from* the relationships between them, not just be dominated by magnitude.

## Weitere Anreicherung

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