---
esco_uri: http://data.europa.eu/esco/skill/5da8018b-ae85-4cde-ad93-0394369018f3
esco_label: "SPARQL"
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
---

# SPARQL — Data Scientist

As a Data Scientist, SPARQL isn't about replacing SQL; it’s about *extending* your data access capabilities. Increasingly, valuable datasets are structured as RDF triples – think knowledge graphs representing relationships between entities (e.g., drug-target interactions, product features, scientific publications).  SPARQL is the standard query language for these graph databases. Daily tasks might involve extracting specific relationships from a knowledge graph to build features for machine learning models, validating data integrity by querying for inconsistencies, or identifying patterns and connections that would be difficult (or impossible) to find with relational queries. You'll likely use it alongside other tools like Python’s `rdflib` library or dedicated graph database interfaces.

## Weitere Anreicherung

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