5c03e07ded
Simpler story (slide decks unread -> three-bullet status email), proper Claude-chat look with avatars and bubbles on both sides. Canonical trigger prompt switched to English everywhere (homepage, architecture, both adapter generators rebuilt) - V7 consistency green, all checks pass. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01PDKeXvpT6tENSvyQGLV1Uq
1.2 KiB
esco_uri, esco_label, relation, onet_soc, source, confidence, qa_count, generator, generated
| esco_uri | esco_label | relation | onet_soc | source | confidence | qa_count | generator | generated |
|---|---|---|---|---|---|---|---|---|
| http://data.europa.eu/esco/skill/46ec0033-3c71-415e-bcff-b065675ba2dc | integrate ICT data | optional | 15-2051.00 | model-knowledge | high | 0 | gemma3:27b (prompt-designed and spot-checked by Claude) | 2026-07-10 |
integrate ICT data — Data Scientist
For a Data Scientist, 'integrating ICT data' isn’t just about collecting information; it's the core of building usable datasets for analysis. Daily tasks involve pulling data from diverse sources – relational databases (SQL Server, PostgreSQL), NoSQL stores (MongoDB, Cassandra), cloud storage (AWS S3, Azure Blob Storage), APIs (REST, GraphQL), streaming platforms (Kafka, Spark Streaming) and even flat files like CSVs or JSON. It means writing scripts (Python with Pandas is dominant, but also R, Scala) to extract, transform, and load (ETL) this data into a consistent format suitable for modeling. Think joining customer transaction data with website clickstream data and social media sentiment – all needing cleaning, standardization, and deduplication.
Weitere Anreicherung
Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).