data scientist

Description

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.

Theoretical Knowledge

ESSENTIALTheoretical knowledge required

query languages data ethics quantitative analysis information categorisation online analytical processing visual presentation techniques information extraction mathematical modelling statistics data visualisation software data engineering statistical modeling techniques data models data mining resource description framework query language data science empirical analysis scientific literature

Practical Skills

ESSENTIALApplied skills & competences

integrate gender dimension in research develop data processing applications develop professional network with researchers and scientists synthesise information apply for research funding manage open publications manage personal professional development promote open innovation in research think abstractly draft scientific or academic papers and technical documentation demonstrate disciplinary expertise manage findable accessible interoperable and reusable data build recommender systems promote the transfer of knowledge apply research ethics and scientific integrity principles in research activities manage data collection systems interpret current data write scientific publications handle data samples promote the participation of citizens in scientific and research activities execute analytical mathematical calculations increase the impact of science on policy and society establish data processes operate open source software manage intellectual property rights collect ICT data speak different languages interact professionally in research and professional environments report analysis results evaluate research activities implement data quality processes use databases publish academic research communicate with a non-scientific audience manage research data use data processing techniques conduct research across disciplines perform data cleansing deliver visual presentation of data perform project management mentor individuals normalise data perform scientific research design database scheme disseminate results to the scientific community

OPTIONALOptional competences

integrate ICT data apply blended learning make data-driven decisions perform data mining define data quality criteria teach in academic or vocational contexts design database in the cloud use spreadsheets software manage ICT data classification manage data manage ICT data architecture create data models