---
esco_uri: http://data.europa.eu/esco/skill/1e77e42a-181f-4b48-8e74-201ce50ffc29
esco_label: "interpret current data"
relation: essential
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# interpret current data — Data Scientist

For a Data Scientist, 'interpreting current data' isn’t just about *reading* numbers; it's about rapidly understanding what those numbers mean *right now*, and how that impacts business decisions or model performance. Daily tasks involve monitoring key metrics (conversion rates, user engagement, fraud detection scores) from live dashboards – think tools like Tableau, Power BI, or custom-built Streamlit apps. You’ll be constantly asking 'why' a metric shifted: is it seasonal? A bug in the data pipeline? A competitor action? This often means slicing and dicing data *ad hoc* using SQL, Python (Pandas), or Spark to investigate anomalies.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
