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
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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/fbafa41f-cd05-4109-a649-8b44d306d779 | create data models | optional | 15-2051.00 | model-knowledge | high | 0 | gemma3:27b (prompt-designed and spot-checked by Claude) | 2026-07-10 |
create data models — Data Scientist
As a Data Scientist, 'creating data models' isn’t about drawing ER diagrams for database admins – it's about translating business problems into structures that machine learning algorithms can understand. Daily tasks involve taking messy, real-world data (think customer interactions, sensor readings, web logs) and defining how those pieces relate to each other for predictive purposes. This means going beyond just identifying entities; you’re thinking about features, transformations needed for model input, and potential biases embedded in the data structure. You'll often be building 'feature stores' – organized collections of these modeled features ready for training.
Weitere Anreicherung
Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).