---
esco_uri: http://data.europa.eu/esco/skill/9ff9db9d-d14b-426e-83f3-e7449af6c79f
esco_label: "manage data"
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
---

# manage data — Data Scientist

For a Data Scientist, 'managing data' isn’t just about storage; it's the *majority* of project time. It means taking raw, messy inputs – think website logs, sensor readings, customer databases – and transforming them into analysis-ready datasets. Daily tasks include profiling (understanding distributions & anomalies), cleaning (handling missing values, correcting errors), and feature engineering (creating new variables). You'll be writing code—primarily Python with libraries like Pandas, NumPy, and potentially Spark for large datasets—to parse different formats (CSV, JSON, SQL databases) and standardize data types.  It’s less about ‘administration’ in a sysadmin sense, and more about *data wrangling* to ensure analytical validity.

## Weitere Anreicherung

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