---
esco_uri: http://data.europa.eu/esco/skill/6360a934-cc87-4954-9656-b32db20592e3
esco_label: "quantitative analysis"
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
---

# quantitative analysis — Data Scientist

For a Data Scientist, 'quantitative analysis' isn’t just *doing* stats; it's framing business problems as questions solvable with data and then rigorously testing hypotheses. Daily work involves exploratory data analysis (EDA) – summarizing key metrics, identifying distributions, spotting outliers – using tools like Pandas & NumPy in Python or dplyr/tidyverse in R. Beyond descriptive statistics, you’ll build predictive models (regression, classification, time series), evaluate their performance (using metrics beyond just accuracy - precision, recall, F1-score are crucial), and interpret the results to provide actionable insights. It's about translating 'what happened?' into 'why did it happen?', and then 'what will likely happen next?'.

A significant portion of a Data Scientist’s time is spent on data cleaning & preprocessing *before* any modeling happens, which requires strong quantitative thinking to handle missing values, skewed distributions, or inconsistent data types.  You'll frequently use SQL for data extraction and transformation, then leverage statistical tests (t-tests, ANOVA, chi-squared) to validate assumptions about your data and model results. A common task is A/B testing – designing experiments, analyzing the results with appropriate statistical rigor, and determining if observed differences are statistically significant.

Pitfalls include overfitting models (creating a model that performs well on training data but poorly on new data), misinterpreting correlation as causation, or using inappropriate statistical tests.  Good quantitative analysis in this role means not just getting *a* result, but understanding the limitations of your methods and clearly communicating uncertainty. It also involves being able to explain complex findings to non-technical stakeholders – translating p-values and confidence intervals into business terms.

What 'good' looks like is a Data Scientist who can independently formulate analytical approaches to ambiguous problems, select appropriate techniques, critically evaluate results (including identifying potential biases), and clearly articulate the implications of their analysis. They don’t just run algorithms; they understand *why* those algorithms work (or don’t) in a given context and are comfortable challenging assumptions.

## Weitere Anreicherung

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