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
1.1 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/98a1dec3-8138-4f46-a596-5e2a83b884b9 | business analytics | optional | 15-2051.00 | model-knowledge | high | 0 | gemma3:27b (prompt-designed and spot-checked by Claude) | 2026-07-10 |
business analytics — Data Scientist
For a Data Scientist, 'Business Analytics' isn’t just doing data analysis; it's framing that analysis around concrete business objectives. Daily work involves translating vague requests like “improve customer retention” into measurable KPIs (e.g., reduce churn rate by X%), identifying relevant data sources to address those KPIs, and then applying statistical modeling/machine learning – but always with the 'so what?' firmly in mind. It's about building models that drive action, not just demonstrate technical skill. Typical tasks include A/B test analysis, cohort analysis for customer behavior, forecasting sales or demand, and creating dashboards to monitor key business metrics.
Weitere Anreicherung
Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).