---
esco_uri: http://data.europa.eu/esco/skill/f715be1d-5fc4-49d3-82a5-0b8090d12849
esco_label: "synthesise information"
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
---

# synthesise information — Data Scientist

As a Data Scientist, 'synthesising information' isn’t about writing literature reviews; it's about rapidly building a *coherent understanding* from messy, disparate data sources and research. Daily this means combining insights from exploratory data analysis (EDA – visualisations, summary stats), model outputs (performance metrics, feature importance), domain expert knowledge (often communicated verbally or in reports), and external research papers/blogs. You're not just reporting what each source *says*, but forming a new, actionable narrative - 'Given these customer demographics *and* the recent marketing campaign results *and* industry trends, we should prioritize personalization A over B'.

Typical tasks involve integrating data from multiple databases (SQL, NoSQL), APIs, and unstructured sources (text documents, web scraping). Tools like Pandas for data manipulation, visualization libraries (Matplotlib, Seaborn, Plotly) to identify patterns, and potentially NLP techniques (topic modeling, sentiment analysis) on text data are crucial.  You'll often be 'triangulating' – confirming a hypothesis with multiple lines of evidence. A key skill is identifying conflicting information *between* sources and determining which is more reliable or relevant given the context.

The biggest pitfalls? Getting lost in details (analysis paralysis), confirmation bias (only focusing on data supporting your initial idea), and failing to document your reasoning clearly.  It's easy to build a technically sound model that doesn’t *mean* anything useful because you haven’t connected it back to the business problem or broader context.  Poor synthesis leads to flawed recommendations, wasted resources, and ultimately, distrust in data-driven insights.

Good synthesis looks like concise, impactful communication – not just presenting numbers but telling a story with them. It's being able to explain *why* a particular model is performing well (or poorly), what the key drivers are, and how those findings translate into concrete actions for stakeholders.  It’s demonstrated by proactively identifying data gaps or inconsistencies that need further investigation, rather than blindly accepting everything at face value.

## Weitere Anreicherung

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