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/9ff9db9d-d14b-426e-83f3-e7449af6c79f | manage data | optional | 15-2051.00 | model-knowledge | high | 0 | gemma3:27b (prompt-designed and spot-checked by Claude) | 2026-07-10 |
manage data — Data Scientist
For a Data Scientist, 'managing data' isn’t just about storage; it's the majority of project time. It means taking raw, messy inputs – think website logs, sensor readings, customer databases – and transforming them into analysis-ready datasets. Daily tasks include profiling (understanding distributions & anomalies), cleaning (handling missing values, correcting errors), and feature engineering (creating new variables). You'll be writing code—primarily Python with libraries like Pandas, NumPy, and potentially Spark for large datasets—to parse different formats (CSV, JSON, SQL databases) and standardize data types. It’s less about ‘administration’ in a sysadmin sense, and more about data wrangling to ensure analytical validity.
Weitere Anreicherung
Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).