Data Scientist: The Engineer of the Future

Conference paper
Part of the Proceedings of the I-ESA Conferences book series (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.

Keywords

Data science Big data Process mining Data mining Visual analytics Internet of things 

References

  1. 1.
    van der Aalst, W. M. P. (2011). Process mining: Discovery, conformance and enhancement of business processes. Berlin: Springer-Verlag.CrossRefGoogle Scholar
  2. 2.
    Alpaydin, E. (2010). Introduction to machine learning. Cambridge: MIT press.MATHGoogle Scholar
  3. 3.
    Anscombe, F. J. (1973). Graphs in statistical analysis. American Statistician, 27(1), 17–21.Google Scholar
  4. 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. 5.
    Bramer, M. (2007). Principles of data mining. Berlin: Springer-Verlag.MATHGoogle Scholar
  6. 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. 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. 8.
    Hand, D., Mannila, H., & Smyth, P. (2001). Principles of data mining. Cambridge: MIT press.Google Scholar
  9. 9.
    Hilbert, M., & Lopez, P. (2011). The world’s technological capacity to store, communicate, and compute information. Science, 332(6025), 60–65.CrossRefGoogle Scholar
  10. 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. 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. 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. 13.
    McCallum, J. C. (2013). Historical costs of memory and storage. http://hblok.net/blog/storage/
  14. 14.
    Mitchell, T. M. (1997). Machine learning. New York: McGraw-Hill.MATHGoogle Scholar
  15. 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. 16.
    Plattner, H., & Zeier, A. (2012). In-Memory data management: Technology and applications. Berlin: Springer-Verlag.CrossRefGoogle Scholar
  17. 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. 18.
    Smolan, R., & Erwitt, J. (2012). The human face of big data. Against All Odds Productions. New York.Google Scholar
  19. 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. 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. 21.
    Wikipedia. (2013). Data science. http://en.wikipedia.org/wiki/data_science
  22. 22.
    Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (second edition). San Francisco: Morgan Kaufmann.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Mathematics and Computer Science (MF 7.103)Eindhoven University of TechnologyEindhovenThe Netherlands

Personalised recommendations