Skip to main content

Data Scientist: The Engineer of the Future

  • Conference paper
  • First Online:
Enterprise Interoperability VI

Part of the book series: Proceedings of the I-ESA Conferences ((IESACONF,volume 7))

Abstract

Although our capabilities to store and process data have been increasing exponentially since the 1960s, suddenly many organizations realize that survival is not possible without exploiting available data intelligently. Out of the blue, “Big Data” has become a topic in board-level discussions. The abundance of data will change many jobs across all industries. Moreover, also scientific research is becoming more data-driven. Therefore, we reflect on the emerging data science discipline. Just like computer science emerged as a new discipline from mathematics when computers became abundantly available, we now see the birth of data science as a new discipline driven by the torrents of data available today. We believe that the data scientist will be the engineer of the future. Therefore, Eindhoven University of Technology (TU/e) established the Data Science Center Eindhoven (DSC/e). This article discusses the data science discipline and motivates its importance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. van der Aalst, W. M. P. (2011). Process mining: Discovery, conformance and enhancement of business processes. Berlin: Springer-Verlag.

    Book  Google Scholar 

  2. Alpaydin, E. (2010). Introduction to machine learning. Cambridge: MIT press.

    MATH  Google Scholar 

  3. Anscombe, F. J. (1973). Graphs in statistical analysis. American Statistician, 27(1), 17–21.

    Google Scholar 

  4. Bergstein, B., & Orcutt, M. (2012). Is Facebook worth it? Estimates of the historical value of a user put the IPO hype in perspective. MIT Technology Review, http://www.technologyreview.com/graphiti/427964/is-facebook-worth-it/

  5. Bramer, M. (2007). Principles of data mining. Berlin: Springer-Verlag.

    MATH  Google Scholar 

  6. Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in information visualization: Using vision to think. San Francisco: Morgan Kaufmann Publishers.

    Google Scholar 

  7. Davenport, T. H., & Patil, D. J. (2012, October). Data scientist: The sexiest Job of the 21st century. Harvard Business Review, 70-76.

    Google Scholar 

  8. Hand, D., Mannila, H., & Smyth, P. (2001). Principles of data mining. Cambridge: MIT press.

    Google Scholar 

  9. Hilbert, M., & Lopez, P. (2011). The world’s technological capacity to store, communicate, and compute information. Science, 332(6025), 60–65.

    Article  Google Scholar 

  10. Howard, C., Plummer, D. C., Genovese, Y., Mann, J., Willis, D. A., & Smith, D. M. (2012). The nexus of forces: Social, mobile, cloud and information. http://www.gartner.com

  11. Keim, D., Kohlhammer, J., Ellis, G., & Mansmann, F. (Ed.). (2010). Mastering the information age: Solving problems with visual analytics. VisMaster. http://www.vismaster.eu/book/

  12. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute. http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation.

  13. McCallum, J. C. (2013). Historical costs of memory and storage. http://hblok.net/blog/storage/

  14. Mitchell, T. M. (1997). Machine learning. New York: McGraw-Hill.

    MATH  Google Scholar 

  15. Pearson, T., & Wegener, R. (2013). Big data: The organizational challenge. bain and company. San Francisco: Bain & Company. http://www.bain.com/publications/articles/big_data_the_organizational_challenge.aspx/

  16. Plattner, H., & Zeier, A. (2012). In-Memory data management: Technology and applications. Berlin: Springer-Verlag.

    Book  Google Scholar 

  17. Press, G. (2013). A very short history of data science. Forbes Technology. http://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/

  18. Smolan, R., & Erwitt, J. (2012). The human face of big data. Against All Odds Productions. New York.

    Google Scholar 

  19. Thomas, J. J., & Cook, K. A. (Ed.). (2005). Illuminating the path: The research and development agenda for visual analytics. IEEE CS Press. Los Alamitos, CA.

    Google Scholar 

  20. van Wijk, J. J. (2005). The value of visualization. In C. Silva, H. Rushmeier & E. Groller (Eds.) Visualization 2005 (pp. 79-86). IEEE CS Press. Los Alamitos, CA.

    Google Scholar 

  21. Wikipedia. (2013). Data science. http://en.wikipedia.org/wiki/data_science

  22. Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (second edition). San Francisco: Morgan Kaufmann.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wil M. P. van der Aalst .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

van der Aalst, W.M.P. (2014). Data Scientist: The Engineer of the Future. In: Mertins, K., Bénaben, F., Poler, R., Bourrières, JP. (eds) Enterprise Interoperability VI. Proceedings of the I-ESA Conferences, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-04948-9_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-04948-9_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04947-2

  • Online ISBN: 978-3-319-04948-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics