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/1973c966-f236-40c9-b2d4-5d71a89019be | use spreadsheets software | optional | 15-2051.00 | model-knowledge | high | 0 | gemma3:27b (prompt-designed and spot-checked by Claude) | 2026-07-10 |
use spreadsheets software — Data Scientist
For a Data Scientist, 'using spreadsheet software' isn’t about basic data entry; it's a surprisingly frequent initial step in exploratory data analysis (EDA) and quick validation. Think of tools like Excel or Google Sheets as your first glance at a new dataset – quickly checking for missing values, understanding data types, calculating simple statistics (mean, median, standard deviation), and spotting obvious outliers before moving to more powerful scripting languages like Python or R. It's also crucial for communicating findings to non-technical stakeholders; creating easily digestible charts and tables is often faster in a spreadsheet than coding them from scratch.
Typical tasks include data cleaning (removing duplicates, standardizing formats), simple transformations (e.g., converting units), and building pivot tables for initial aggregation. You'll also use spreadsheets to sanity-check the output of your code – verifying that aggregations or calculations performed in Python match what you’d expect based on a smaller sample viewed in Excel. Common pitfalls are relying too heavily on spreadsheets for large datasets (performance issues, version control nightmares) and making assumptions about data types or formulas without careful review.
A 'good' Data Scientist using spreadsheets isn't necessarily an expert formula-builder, but they are efficient and precise. They understand the limitations of the tool – it’s not a database! – and know when to transition to more robust solutions. Version control (even simple file naming conventions) is vital. They also leverage features like data validation rules to prevent errors during manual input or small-scale edits.
Finally, while Python/R are core, knowing spreadsheet shortcuts and functions can dramatically speed up ad-hoc analysis and reporting. Being able to quickly prototype a visualization in Excel before implementing it with matplotlib or ggplot2 is a valuable skill. It's about pragmatic efficiency – using the right tool for the job, even if that job seems 'simple'.
Weitere Anreicherung
Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).