Zusammenfassung
In unserem Beitrag beleuchten wir die Rolle von Statistik und Data Science im Umfeld von Big Data. Data Science liegt dabei zwischen Statistik und Informatik und vereint die unterschiedlichen Konzepte der Datenanalyse. Wir zeigen anhand von zwei Beispielen auf, warum Statistik und statistisches Denken auch im Zeitalter von Big Data wichtig und hilfreich ist.
Abstract
In our work we illuminate the role of statistics and data science in the field of Big Data. Data Science thereby lies between statistics and computer science and unites the different concepts of data analytics. We show with examples, why statistics and statistical literacy is important and helpful in the times of Big Data.
This is a preview of subscription content, log in via an institution to check access.
Literatur
Angrist JD, Imbens GW, Rubin DB (1996) Identification of causal effects using instrumental variables. JASA 91(434):444–455
Binder H, Blettner M (2015) Big Data in der wissenschaftlichen Medizin – eine biostatistische Perspektive. Dtsch Arztebl Int 112(9):137–142. doi:10.3238/arztebl.2015.0137
Breiman L (2001) Statistical modeling: The two cultures (with comments and a rejoinder by the author). Stat Sci 16(3):199–231
Bühlman P, van den Geer S (2011) Statistics for High Dimensional Data. Springer, Berlin
Cleveland WS (2001) Data science: An action plan for expanding the technical areas of the field of statistics. Int Stat Rev 69:21–26
Elliot JH, Girmshaw J, Altman R, Bero L, Goodman SN, Henry D, Macleod D, Tovey D, Tugwell P, White H, Sim I (2015) Informatics: Make sense of health data. Nature 527:31–32
Geppert LN et al (2015) Random projections for Bayesian regression. Stat Comput:1–23. doi:10.1007/s11222-015-9608-z
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, 2. Aufl. Springer, Berlin
Heinecke A, Breuer A et al (2014) Petascale high order dynamic rupture earthquake simulations on heterogeneous supercomputers. In: IEEE Press Piscataway, NJ, USA (Hrsg.) Proceedings of the international conference for high performance computing networking, storage and analysis SC14, S 3–14
Meinshausen N, Bühlmann P (2015) Maximin effects in inhomogeneous large-scale data. Ann Stat 43:1801–1830
Rudin C, A Working Group of the ASA, - (2014) Discovery with data: Leveraging statistics with computer science to transform science and society
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kauermann, G., Küchenhoff, H. Statistik, Data Science und Big Data. AStA Wirtsch Sozialstat Arch 10, 141–150 (2016). https://doi.org/10.1007/s11943-016-0188-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11943-016-0188-y