data scientist
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also called: electromobility data scientist, research data scientist, data expert, data research scientist
- 63
- essential skills
- 34
- optional skills
- 8
- related roles
- 2511
- ISCO-08 group
Where you can go from here
8 rolesOccupations sharing the most essential skills with this one. The number is a count, not an opinion.
- statisticianWhat changes?37 skills in common
- demographerWhat changes?34 skills in common
- behavioural scientist33 skills in common
- bioinformatics scientist33 skills in common
- biometrician33 skills in common
- economist33 skills in common
- physicistWhat changes?33 skills in common
- analytical chemist32 skills in common
Essential skills
63- 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
Optional skills
34- apply blended learning
- business analytics
- business intelligence
- computational biology
- computer simulation
- create data models
- data quality assessment
- define data quality criteria
- design database in the cloud
- digital curation
- Hadoop
- healthcare analytics
- image recognition
- integrate ICT data
- LDAP
- LINQ
- make data-driven decisions
- manage data
- manage ICT data architecture
- manage ICT data classification
- marketing analytics
- MDX
- multidisciplinary research
- N1QL
- perform data mining
- research design
- scientific computing
- social network analysis
- SPARQL
- state estimation
- teach in academic or vocational contexts
- unstructured data
- use spreadsheets software
- XQuery
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