119 lines
5.2 KiB
Markdown
119 lines
5.2 KiB
Markdown
---
|
|
name: data-scientist
|
|
description: "Occupational skill for the role 'data scientist' (also: data expert, research data scientist, data research scientist, electromobility data scientist). Use when the user asks for typical data scientist work such as: Report results of statistical analyses in peer-reviewed papers and technical manuals.; Develop software applications or programming for statistical modeling and graphic analysis.; Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students."
|
|
---
|
|
|
|
# Data Scientist
|
|
|
|
Data scientists locate and interpret rich data sources, manage large volumes of data, merge these sources, and ensure data-set consistency; they also create visualizations to facilitate understanding. Utilizing data, they build mathematical models, present and communicate insights and findings to both specialists and non-expert audiences as needed, and recommend practical applications for the data.
|
|
|
|
## Core workflow
|
|
|
|
1. Report results of statistical analyses in peer-reviewed papers and technical manuals.
|
|
2. Develop software applications or programming for statistical modeling and graphic analysis.
|
|
3. Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students.
|
|
4. Determine whether statistical methods are appropriate, based on user needs or research questions of interest.
|
|
5. Report results of statistical analyses, including information in the form of graphs, charts, and tables.
|
|
6. Process large amounts of data for statistical modeling and graphic analysis, using computers.
|
|
7. Identify relationships and trends in data, as well as any factors that could affect the results of research.
|
|
8. Analyze and interpret statistical data to identify significant differences in relationships among sources of information.
|
|
|
|
## How to use this skill
|
|
|
|
- Read [references/profile.md](references/profile.md) for the occupation profile and scope.
|
|
- Consult [references/tasks.md](references/tasks.md) for the full task and activity inventory.
|
|
- Check [references/skills.md](references/skills.md) for essential vs. optional competences.
|
|
- Check [references/tools.md](references/tools.md) for the software commonly used in this role.
|
|
- See [references/ai-skills.md](references/ai-skills.md) — matched external AI agent skills (tiered, per-source attribution).
|
|
- See [references/practitioner-qa.md](references/practitioner-qa.md) — curated Stack Exchange practitioner Q&A (per-entry attribution, CC-BY-SA 4.0).
|
|
|
|
## Key competences (essential)
|
|
|
|
- apply for research funding
|
|
- apply research ethics and scientific integrity principles in research activities
|
|
- build recommender systems
|
|
- collect ICT data
|
|
- communicate with a non-scientific audience
|
|
- conduct research across disciplines
|
|
- data engineering
|
|
- data ethics
|
|
- data mining
|
|
- data models
|
|
- data science
|
|
- data visualisation software
|
|
- deliver visual presentation of data
|
|
- demonstrate disciplinary expertise
|
|
- design database scheme
|
|
- develop data processing applications
|
|
- develop professional network with researchers and scientists
|
|
- disseminate results to the scientific community
|
|
- draft scientific or academic papers and technical documentation
|
|
- empirical analysis
|
|
- establish data processes
|
|
- evaluate research activities
|
|
- execute analytical mathematical calculations
|
|
- handle data samples
|
|
- implement data quality processes
|
|
- increase the impact of science on policy and society
|
|
- information categorisation
|
|
- information extraction
|
|
- integrate gender dimension in research
|
|
- interact professionally in research and professional environments
|
|
- interpret current data
|
|
- manage data collection systems
|
|
- manage findable accessible interoperable and reusable data
|
|
- manage intellectual property rights
|
|
- manage open publications
|
|
- manage personal professional development
|
|
- manage research data
|
|
- mathematical modelling
|
|
- mentor individuals
|
|
- normalise data
|
|
- online analytical processing
|
|
- operate open source software
|
|
- perform data cleansing
|
|
- perform project management
|
|
- perform scientific research
|
|
- promote open innovation in research
|
|
- promote the participation of citizens in scientific and research activities
|
|
- promote the transfer of knowledge
|
|
- publish academic research
|
|
- quantitative analysis
|
|
- query languages
|
|
- report analysis results
|
|
- resource description framework query language
|
|
- scientific literature
|
|
- speak different languages
|
|
- statistical modeling techniques
|
|
- statistics
|
|
- synthesise information
|
|
- think abstractly
|
|
- use data processing techniques
|
|
- use databases
|
|
- visual presentation techniques
|
|
- write scientific publications
|
|
|
|
|
|
<!-- hot-tech -->
|
|
|
|
## Hot technologies
|
|
|
|
Top tools from 46 gated job ads (see references/market.md, as of 2026-07-11):
|
|
|
|
- Python — 78 %
|
|
- SQL — 46 %
|
|
- Spark — 24 %
|
|
- AWS — 22 %
|
|
- EMR — 11 %
|
|
- Hadoop — 11 %
|
|
- scikit-learn — 11 %
|
|
- Azure — 9 %
|
|
- Conda — 9 %
|
|
- H2O — 9 %
|
|
|
|
<!-- hot-tech -->
|
|
|
|
---
|
|
*Sources: ESCO v1.2.1 (http://data.europa.eu/esco/occupation/258e46f9-0075-4a2e-adae-1ff0477e0f30), O*NET 30.3 (15-2041.00). See manifest.json for licensing/attribution.*
|
|
*O*NET nearest match: 15-2041.00 Statisticians (proxy — no exact O*NET occupation exists).*
|