---
esco_uri: http://data.europa.eu/esco/skill/348b74cd-49ce-4844-8bdf-ec188b497213
esco_label: "visual presentation techniques"
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
---

# visual presentation techniques — Data Scientist

For a Data Scientist, 'visual presentation techniques' isn’t about making pretty charts; it's about *effectively communicating insights derived from data*. Daily tasks involve rapidly exploring datasets using visualizations to identify patterns, outliers, and relationships – think histograms for distributions, scatter plots for correlations, boxplots for comparisons. You'll be creating visuals not just for final reports but constantly during exploratory data analysis (EDA) to validate assumptions and guide further investigation. This often means quickly iterating through different chart types in tools like Python’s Matplotlib/Seaborn or R’s ggplot2, or interactive dashboards using Tableau, Power BI, or even Streamlit/Dash.

## Weitere Anreicherung

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