---
esco_uri: http://data.europa.eu/esco/skill/fecf8a0d-62c4-4e71-9b03-0f4fc2ad7bf5
esco_label: "data models"
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
---

# data models — Data Scientist

As a Data Scientist, 'data models' isn’t about theoretical database design; it's about *quickly* understanding and manipulating data structures to answer business questions. Daily tasks involve reverse-engineering existing data (often messy!) – figuring out how tables relate in SQL databases, deciphering nested JSON from APIs, or untangling complex CSV files. You'll spend a lot of time translating these 'raw' models into formats suitable for analysis: Pandas DataFrames, Spark DataFrames, or even graph structures if relationships are key.  It’s less about *creating* pristine schemas and more about *adapting* to what exists.

Tools heavily used include SQL (essential!), Python with Pandas/Polars, potentially Spark for large datasets, and data profiling tools like Great Expectations or pandas-profiling to quickly assess data quality & relationships.  A key skill is recognizing common data modeling patterns – star schemas, snowflake schemas – even if they aren't perfectly implemented. You’ll also use visualization tools (Tableau, Power BI) to *explore* the model and communicate findings.

A major pitfall is assuming a clean, well-structured dataset. Real-world data is often inconsistent, incomplete, or poorly documented.  Spending too much time trying to force data into an ideal model instead of working with what you have is a common mistake. Another is failing to document the *actual* structure you’re using – leading to reproducibility issues. Good looks like being able to rapidly ingest and transform diverse data sources, clearly documenting assumptions about the underlying data model, and proactively identifying potential data quality problems.

Finally, 'interpreting data structures' means understanding how biases or limitations in the original data collection process are *encoded* within the model itself. For example, a missing value might not be random; it could indicate a systematic issue with data capture.  This requires critical thinking and domain knowledge to avoid drawing incorrect conclusions from your analysis.

## Weitere Anreicherung

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