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/0823ccef-813f-4f22-afef-ac0d68615e8f | computer simulation | optional | 15-2051.00 | model-knowledge | high | 0 | gemma3:27b (prompt-designed and spot-checked by Claude) | 2026-07-10 |
computer simulation — Data Scientist
For a Data Scientist, 'computer simulation' isn’t about building realistic video game physics; it's about model validation and what-if analysis. You'll frequently encounter situations where you build predictive models (e.g., customer churn, fraud detection) but lack real-world data to fully test edge cases or future scenarios. Simulation lets you generate synthetic datasets based on your model’s assumptions – essentially 'running the model forward' under controlled conditions. This helps identify weaknesses before deployment and quantify uncertainty. Think simulating thousands of potential customer behaviors to stress-test a marketing campaign prediction, or creating artificial transaction data to evaluate a new fraud rule.
Weitere Anreicherung
Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).