---
esco_uri: http://data.europa.eu/esco/skill/9cf681c7-89ec-470c-b651-7fe03786f586
esco_label: "query languages"
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
---

# query languages — Data Scientist

For Data Scientists, 'query languages' isn’t just about *knowing* SQL or SPARQL; it's about rapidly extracting, transforming, and validating data for analysis. Daily tasks involve writing complex queries to pull specific datasets from relational databases (PostgreSQL, MySQL, Snowflake), NoSQL stores (MongoDB, Cassandra), and cloud data warehouses (BigQuery, Redshift). You’ll frequently be joining tables, applying filters, aggregating data, and handling missing values *within* the query itself – essentially pre-processing data before it even hits your Python/R environment.  Good looks like writing efficient queries that minimize resource usage and return clean, reliable results; understanding indexing for performance is crucial.

## Weitere Anreicherung

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