Data scientist: The engineer of the future

Proceedings of the I-ESA Conferences

Volumn 7, Issue , 2014, Pages 13-26

  van der Aalst, Wil M P ^a  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

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

Indexed keywords

EID: 84937400122     PISSN: 21992533     EISSN: 21992541     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-319-04948-9_2     Document Type: Chapter

References (22)

1. van der Aalst, W. M. P. (2011). Process mining: Discovery, conformance and enhancement of business processes. Berlin: Springer-Verlag.
2. Alpaydin, E. (2010). Introduction to machine learning. Cambridge: MIT press.
3. Anscombe, F. J. (1973). Graphs in statistical analysis. American Statistician, 27(1), 17–21.
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.
6. Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in information visualization: Using vision to think. San Francisco: Morgan Kaufmann Publishers.
7. Davenport, T. H., & Patil, D. J. (2012, October). Data scientist: The sexiest Job of the 21st century. Harvard Business Review, 70-76.
8. Hand, D., Mannila, H., & Smyth, P. (2001). Principles of data mining. Cambridge: MIT press.
9. Hilbert, M., & Lopez, P. (2011). The world’s technological capacity to store, communicate, and compute information. Science, 332(6025), 60–65.
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.
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.
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.
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.
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.
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.