Abstract
We define Data Science as the combination of statistical and computational data analytic approaches. We argue that only this combination allows to tackle the many problems occurring in today’s Big Data era. We outline a possible curriculum, which focuses on both statistics and computer science aspects of data analytics. The proposed curriculum is implemented in the Data Science master program run at the University Munich (www.datascience-munich.de). We also argue that data ethical aspects as well as practical and communication skills are essential in a modern Data Science study program.
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https://en.wikipedia.org/wiki/Data_science assessed 24.2.2017.
References
Cleveland, W.S.: Data science: an action plan for expanding the technical areas of the field of statistics. Int. Stat. Rev. 69, 21–26 (2001)
Breimann, L.: Statistical modelling: the two cultures. Stat. Sci. 16, 199–231 (2001)
Donoho, D.: 50 years of Data Science. J. Comput. Graph. Stat. 26(4), 745–766 (2017)
Molenberghs, G., Fitzmaurice, G., Kenward, M.G., Tsiatis, A., Verbeke, G.: Handbook of Missing Data Methology. CRC Press, Boca Raton (2015)
Wooldridge, J.M.: Introductory Econometrics: A Modern Approach, 5th edn. South-Western, Mason (2013)
Carroll, R., Ruppert, D., Stefanski, L.A., Crainiceanu, C.M.: Measurement Error in Nonlinear Models: A Modern Perspective, 2nd edn. CRC Press, Boca Raton (2006)
Funding
Funding was provided by Elitenetzwerk Bayern (Grant No. S-NW-2015-313, Elitestudiengang Data Science).
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Kauermann, G., Seidl, T. Data Science: a proposal for a curriculum. Int J Data Sci Anal 6, 195–199 (2018). https://doi.org/10.1007/s41060-018-0113-2
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DOI: https://doi.org/10.1007/s41060-018-0113-2