---
esco_uri: http://data.europa.eu/esco/skill/1b70a55d-b8a4-49fc-96c6-15ab4cff2522
esco_label: "use data processing techniques"
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
---

# use data processing techniques — Data Scientist

For a Data Scientist, 'use data processing techniques' isn’t just about running pre-built functions; it's the *core* of turning raw information into actionable insights. Daily tasks involve cleaning messy real-world datasets (handling missing values, outliers, inconsistent formats), transforming data for modeling (feature engineering – creating new variables from existing ones, scaling/normalizing), and building data pipelines to automate these processes. Think SQL for database querying, Python with libraries like Pandas & NumPy for manipulation, and potentially Spark or Dask for handling truly large datasets. It's about efficiently preparing data *for* the analysis, not just doing it.

## Weitere Anreicherung

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