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001 978-3-030-51023-7
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005 20240423125323.0
007 cr nn 008mamaa
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020 _a9783030510237
_9978-3-030-51023-7
024 7 _a10.1007/978-3-030-51023-7
_2doi
050 4 _aQA76.9.D35
050 4 _aQ350-390
072 7 _aUMB
_2bicssc
072 7 _aGPF
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072 7 _aUMB
_2thema
072 7 _aGPF
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082 0 4 _a005.73
_223
082 0 4 _a003.54
_223
245 1 4 _aThe Data Science Framework
_h[electronic resource] :
_bA View from the EDISON Project /
_cedited by Juan J. Cuadrado-Gallego, Yuri Demchenko.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXIV, 194 p. 35 illus., 31 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction to the Data Science Framework -- Data Science Competences -- Data Science Body of Knowledge -- Data Science Curriculum -- Data Science Professional Profiles -- Use Cases and Applications -- App. A, Data Science Related Process Models.
520 _aThis edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader. The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models. The book can be used todevelop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines.
650 0 _aData structures (Computer science).
650 0 _aInformation theory.
650 0 _aComputers.
650 0 _aProfessions.
650 0 _aEngineering
_xData processing.
650 0 _aStatistics .
650 1 4 _aData Structures and Information Theory.
650 2 4 _aThe Computing Profession.
650 2 4 _aData Engineering.
650 2 4 _aStatistics.
700 1 _aCuadrado-Gallego, Juan J.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aDemchenko, Yuri.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030510220
776 0 8 _iPrinted edition:
_z9783030510244
776 0 8 _iPrinted edition:
_z9783030510251
856 4 0 _uhttps://doi.org/10.1007/978-3-030-51023-7
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c176675
_d176675