Data science education: a preliminary analysis of the U.S landscape
DOI:
https://doi.org/10.5433/1981-8920.2016v21n2p307Keywords:
Data Science, Data Scientist, Professional Skills, Professional QualificationAbstract
Introduction: Data scientists has received great attention in recent years following the demands of the labor market stimulated by the open science and big data era. Originally widespread in 2008 and, since then, present in many different industries and applications; data science was announced in 2012 as the most attractive and one of the best paid jobs of the century, culminating with an increasing supply of training courses. Objective: Characterize and understand the formative aspects of data scientists. Methodology: This article describes part of a survey research based on analysis of 93 degrees in data science offered by US institutions. Results: The content analysis of the information publicized on the websites of the identified programs provides evidence that this professional is trained to deal with issues related to the collection, treatment, processing, analysis, visualization and curation of large and heterogeneous data collections in order to solving real-life and practical problems. Conclusion: Findings also revealed that, in general, training in science data places great emphasis on statistical skills, mathematics and computing, including programming and advanced modeling, many of which are placed as prerequisites for admission in these programs.Downloads
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CLEVELAND, W. S. Data Science: anactionplan for expandingthetechnicalareasofthefieldofstatistics. InternationalStatisticalReview, Malden, MA, v. 69, p. 21-26, 2001. doi:10.1111/j.1751-5823.2001.tb00477.x
CONAWAY, D. The data sciencevenndiagram. 2010. Disponível em: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram>. Acesso em: 10 ago. 2016.
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FINZER, W. The data scienceeducationdilemma. Technology Innovations in StatisticsEducation, Caifórnia, v. 7, n. 2, 2013. Disponível em: http://escholarship.org/uc/item/7gv0q9dc>. Acesso em: 22 ago. 2016.
GRANVILLE, V. Developinganalytictalent: becoming a data scientist. Indianapolis: John Wiley, 2014.
KIM, J. Y.; LEE, C. K. Anempiricalanalysisofrequirements for data scientistsusing online jobpostings. InternationalJournalof Software Engineeringand its Applications, Seoul, v. 10, n. 4, p.161-172, 2016.
LANEY, D. 3D Data management: controlling data volume, velocityandvariety. Application Delivery Strategies, Stanford. 2001. Disponível em: http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf>. Acesso em: 20 ago. 2016.
LOUKIDES, Mike. Whatis data science?Sebastopol, CA: O'Reilly Media, 2011.
PATIL, T. H.; DAVENPORT, D. J. Data Scientist: thesexiestjobofthe 21st century. Harvard Business Review, Brighton, MA, 2012. Disponível em: https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century>. Acesso em: 5 ago. 2016.
STANTON, J. et al. Interdisciplinary data scienceeducation. In: XIAO, N..; MCEWEN,L. R. SpecialIssues in Data Management. Washington, DC: American ChemicalSociety, 2012. p. 97-113. (ACS Symposium Series, v. 1110).doi: 10.1021/bk-2012-1110.ch006
SWAN, A.; BROWN, S. The skills, role andcareerstructureof data scientistsandcurators: anassessmentofcurrentpracticeand future needs. Reporttothe Joint Information Systems Committee (JISC). Truro: Key Perspectives for JISC, 2008. 34 p.
WARD, S.; BARKER, A. Undefinedby data: a surveyof big data definitions. 2013. Disponível em: arXivpreprint arXiv:1309.5821>. Acesso em: 10 out. 2016.
VAN DER AALST, W. M. P. Data scientist: theengineerofthe future. In: Enterprise Interoperability VI: interoperability for agility, resilienceandplasticityofcollaborations. Springer: New York,2014. doi: 10.1007/978-3-319-04948-9_2
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Published
2016-12-20
How to Cite
Curty, R. G., & Serafim, J. da S. (2016). Data science education: a preliminary analysis of the U.S landscape. Informação & Informação, 21(2), 307–331. https://doi.org/10.5433/1981-8920.2016v21n2p307
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