---
esco_uri: http://data.europa.eu/esco/skill/4a193795-fa63-414e-9f2e-bfdb3924571e
esco_label: "handle data samples"
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
---

# handle data samples — Data Scientist

For a Data Scientist, 'handling data samples' isn’t just about grabbing rows from a database; it's the foundational step for *everything* else. Daily tasks involve defining sampling strategies – are you doing simple random sampling, stratified sampling to ensure representation of key groups, or more complex techniques like cluster sampling? You'll be writing SQL queries (or using tools like Pandas in Python/R) to extract these samples, often dealing with very large datasets where full population access is impractical. It also means understanding *why* you’re sampling – what question are you trying to answer and how does the sample need to reflect the broader population to avoid bias.

## Weitere Anreicherung

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