---
esco_uri: http://data.europa.eu/esco/skill/50b100ea-74fd-4706-99db-3e4ca55e51b8
esco_label: "perform data cleansing"
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
---

# perform data cleansing — Data Scientist

As a Data Scientist, 'performing data cleansing' isn’t just about fixing typos; it's *the* foundational step before any meaningful analysis or modeling. Daily, this means wrestling with real-world messiness: missing values (deciding imputation strategies – mean/median/mode vs. more sophisticated methods), inconsistent formatting (dates as ‘MM/DD/YY’ in one place and ‘YYYY-MM-DD’ elsewhere), outliers that skew results, and duplicate records. You'll spend a significant chunk of time writing code to identify these issues programmatically, not manually fixing them in spreadsheets. Think regular expressions for text cleaning, statistical methods for outlier detection (IQR, Z-score), and fuzzy matching algorithms for near-duplicate identification.

## Weitere Anreicherung

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