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The Data Scientist Job in Italy: What Companies Require

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Advances on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 96))

Abstract

In recent years, experts have considered the job of data scientists as the sexiest of 21st century. However, people skilled with data scientist’s expertise seem to be rare. This probably happens for the complex set of competences that this profession requires. In this paper, we deal with companies that are searching for data scientists to expand their workforce. Scraping data from the business-networking website LinkedIn, as for companies, we collected dimensions, sectors, kinds of employment, contract forms, working functions, and required skills. Our findings suggest that data scientist profession extends to several sectors but it is not yet consolidated. This condition intensifies the misconception about the skills required. Based on all this, we think that the role of higher institutions becomes fundamental, on the one hand to define data science as a discipline, and on the other to train young people for acquiring the set of skills needed.

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References

  1. Schewe, K.D., Thalheim, B.: Semantics in data and knowledge bases. In: International Workshop on Semantics in Data and Knowledge Bases, pp. 1–25. Springer, Berlin (2008)

    Google Scholar 

  2. Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., Zhou, X.: Big data challenge: a data management perspective. Front. Comput. Sci. 7(2), 157–164 (2013)

    Article  MathSciNet  Google Scholar 

  3. Davenport, T.H., Patil, D.J.: Data scientist. Harvard Bus. Rev. 90(5), 70–76, 72 (2012)

    Google Scholar 

  4. Yin, S., Kaynak, O.: Big data for modern industry: challenges and trends (point of view). Proc. IEEE 103(2), 143–146 (2015)

    Article  Google Scholar 

  5. Davenport, T.H., Dyché, J.: Big data in big companies. International Institute for Analytics, p. 3 (2013). https://docs.media.bitpipe.com/io_10x/io_102267/item_725049/Big-Data-in-Big-Companies.pdf. Accessed 20 Mar 2019

  6. Gartner: Data and Analytics Leadership Vision for 2017. https://www.gartner.com/binaries/content/assets/events/keywords/business-intelligence/bie18i/gartner_data-analytics_research-note_da-leadership-vision_2016.pdf. Accessed 20 Oct 2018

  7. Oussous, A., Benjelloun, F.Z., Lahcen, A.A., Belfkih, S.: Big data technologies: a survey. J. King Saud Univ.-Comput. Inf. Sci. 30(4), 431–448 (2018)

    Google Scholar 

  8. Gantz, J., Reinsel, D.: The digital universe in 2020: big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Anal. Future 2012, 1–16 (2007)

    Google Scholar 

  9. Storey, V.C., Song, I.Y.: Big data technologies and management: what conceptual modeling can do. Data Knowl. Eng. 108, 50–67, 52 (2017)

    Google Scholar 

  10. Kim, M., Zimmermann, T., DeLine, R., Begel, A.: The emerging role of data scientists on software development teams. In: Proceedings of the 38th International Conference on Software Engineering, pp. 96–107. ACM (2016)

    Google Scholar 

  11. Besse, P., Laurent, B.: De statisticien à Data Scientist-Développements pédagogiques à l’INSA de Toulouse. Statistique et Enseignement 7(1), 75–93 (2016)

    Google Scholar 

  12. Dhar, V.: Data science and prediction. Commun. ACM 56(12), 64–73 (2013)

    Article  Google Scholar 

  13. IBM: Data Scientists. https://www.ibm.com/analytics/us/en/technology/clouddataservices/data-Scientist. Accessed 16 Oct 2018

  14. Thompson, N.: When Tech Knows You Better Than You Know Yourself. https://www.wired.com/story/artificial-intelligence-yuval-noah-harari-tristan-harris/. Accessed 16 Feb 2019

  15. Harris, H.D., Murphy, S.P., Vaisman, M.: Analyzing the Analyzers: An Introspective Survey of Data Scientists and Their Work. O’Reilly, Sebastopol (2013)

    Google Scholar 

  16. Fisher, D., DeLine, R., Czerwinski, M., Drucker, S.: Interactions with big data analytics. Interactions 19(3), 50–59 (2012)

    Article  Google Scholar 

  17. Kim, M., Zimmermann, T., DeLine, R., Begel, A.: The emerging role of data scientists on software development teams. In: Proceedings of the 38th International Conference on Software Engineering, ICSE 2016, pp. 96–107 (2016)

    Google Scholar 

  18. Kim, M., Zimmermann, T., DeLine, R., Begel, A.: Data scientists in software teams: state of the art and challenges. IEEE Trans. Softw. Eng. 44(11), 1024–1038 (2018)

    Article  Google Scholar 

  19. Kandel, S., Paepcke, A., Hellerstein, J.M., Heer, J.: Enterprise data analysis and visualization: an interview study. IEEE Trans. Visual Comput. Graphics 12, 2917–2926 (2012)

    Article  Google Scholar 

  20. Asamoah, D.A., Sharda, R., Hassan Zadeh, A., Kalgotra, P.: Preparing a data scientist: a pedagogic experience in designing a big data analytics course. Decis. Sci. J. Innov. Educ. 15(2), 161–190 (2017)

    Article  Google Scholar 

  21. Anderson, P., Bowring, J., McCauley, R., Pothering, G., Starr, C.: An undergraduate degree in data science: curriculum and a decade of implementation experience. In: Proceedings of the 45th ACM Technical Symposium on Computer Science Education, pp. 145–150 (2014)

    Google Scholar 

  22. Aasheim, C.L., Williams, S., Rutner, P., Gardiner, A.: Data analytics vs. data science: a study of similarities and differences in undergraduate programs based on course descriptions. J. Inf. Syst. Educ. 26(2), 103–115 (2015)

    Google Scholar 

  23. Anderson, C.: The Long Tail: How Endless Choice is Creating Unlimited Demand. Random House, New York City (2007)

    Google Scholar 

  24. Galloway, S.: The four: the hidden DNA of Amazon, Apple, Facebook and Google. Random House, New York City (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Business, Management and Innovation System, University of Salerno, Fisciano, Italy

    Maddalena della Volpe

  2. Department of Political and Communication Sciences, University of Salerno, Fisciano, Italy

    Francesca Esposito

Authors
  1. Maddalena della Volpe
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  2. Francesca Esposito
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Corresponding author

Correspondence to Maddalena della Volpe .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. University of Antwerp, Antwerpen, Belgium

    Peter Hellinckx

  3. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

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della Volpe, M., Esposito, F. (2020). The Data Scientist Job in Italy: What Companies Require. In: Barolli, L., Hellinckx, P., Natwichai, J. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2019. Lecture Notes in Networks and Systems, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-030-33509-0_84

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  • DOI: https://doi.org/10.1007/978-3-030-33509-0_84

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33508-3

  • Online ISBN: 978-3-030-33509-0

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