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/4216e465-7baa-4884-a241-54b197bb9278 | perform data mining | optional | 15-2051.00 | model-knowledge | high | 0 | gemma3:27b (prompt-designed and spot-checked by Claude) | 2026-07-10 |
perform data mining — Data Scientist
For a Data Scientist, 'performing data mining' isn’t just running algorithms; it's about investigative problem solving. Daily work involves taking messy, real-world datasets – often from multiple sources (databases, APIs, logs) – and formulating questions that can be answered through pattern discovery. This means going beyond pre-defined reports. You might be tasked with identifying customer segments for targeted marketing, detecting fraudulent transactions, predicting equipment failure, or understanding feature importance in a model. It's heavily iterative: exploring data visually (using tools like Tableau/PowerBI initially), then using scripting languages (Python/R) and libraries (Pandas, Scikit-learn, dplyr) to clean, transform, and apply statistical techniques or machine learning algorithms.
Weitere Anreicherung
Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).