Working paper Open Access

EDISON Data Science Framework: Part 4. Data Science Professional profiles (DSP profiles) Release 1

Yuri Demchenko

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  <identifier identifierType="DOI">10.5281/zenodo.167597</identifier>
      <creatorName>Yuri Demchenko</creatorName>
      <affiliation>University of Amsterdam</affiliation>
    <title>EDISON Data Science Framework: Part 4. Data Science Professional profiles (DSP profiles) Release 1</title>
    <subject>Data Science</subject>
    <subject>Data Stewardship</subject>
    <subject>EDISON Data Science Framework (EDSF)</subject>
    <subject>Data Science Competences Framework (CF-DS)</subject>
    <subject>Data Science Body of Knowledge (DS-BoK)</subject>
    <subject>Data Science Model Curriculum (MC-DS)</subject>
    <subject>Data Science Professional profiles</subject>
    <subject>Data Science Analytics</subject>
    <subject>Big Data</subject>
    <date dateType="Issued">2016-11-20</date>
  <resourceType resourceTypeGeneral="Text">Working paper</resourceType>
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    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;The presented Data Science Professional profiles definition is a part of the EDISON Data Science Framework (EDSF) and is a product of the EDISON project.&lt;/p&gt;

&lt;p&gt;This document presents the results of the research and development in the EDISON project to define the Data Science Professional (DSP) profiles that is important for defining the Data Scientist roles in the organisation and their alignment with the organizational goals and mission. The Data Science Professional profiles definition is done in the context of the whole EDISON Data Science Framework. The proposed DSP profiles are defined as an extension to current ESCO (European Skills, Competences, Qualifications and Occupations) taxonomy and is intended to be proposed for formal inclusion of the new Data Science professions family into the future ESCO taxonomy edition. The proposed DSP profiles when adopted by the community will have multiple uses. First of all. they will help organisations to plan their staffing for data related functions when migrating to agile data driven organizational model. The Human Resource (HR) departments can effectively use DSP profiles for vacancy description construction and candidates assessment. When used together with CF-DS, the DSP profiles can provide a basis for building interactive/web based tool for individual competences benchmarking against selected (or desirable) professional profiles as well as advising practitioners on the (up/re-) skilling path.&lt;/p&gt;</description>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/675419/">675419</awardNumber>
      <awardTitle>Education for Data Intensive Science to Open New science frontiers</awardTitle>
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