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.
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References
van der Aalst, W. M. P. (2011). Process mining: Discovery, conformance and enhancement of business processes. Berlin: Springer-Verlag.
Alpaydin, E. (2010). Introduction to machine learning. Cambridge: MIT press.
Anscombe, F. J. (1973). Graphs in statistical analysis. American Statistician, 27(1), 17–21.
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/
Bramer, M. (2007). Principles of data mining. Berlin: Springer-Verlag.
Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in information visualization: Using vision to think. San Francisco: Morgan Kaufmann Publishers.
Davenport, T. H., & Patil, D. J. (2012, October). Data scientist: The sexiest Job of the 21st century. Harvard Business Review, 70-76.
Hand, D., Mannila, H., & Smyth, P. (2001). Principles of data mining. Cambridge: MIT press.
Hilbert, M., & Lopez, P. (2011). The world’s technological capacity to store, communicate, and compute information. Science, 332(6025), 60–65.
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
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/
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.
McCallum, J. C. (2013). Historical costs of memory and storage. http://hblok.net/blog/storage/
Mitchell, T. M. (1997). Machine learning. New York: McGraw-Hill.
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/
Plattner, H., & Zeier, A. (2012). In-Memory data management: Technology and applications. Berlin: Springer-Verlag.
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/
Smolan, R., & Erwitt, J. (2012). The human face of big data. Against All Odds Productions. New York.
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.
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.
Wikipedia. (2013). Data science. http://en.wikipedia.org/wiki/data_science
Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (second edition). San Francisco: Morgan Kaufmann.
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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
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DOI: https://doi.org/10.1007/978-3-319-04948-9_2
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