Abstract
Several distinct intellectual communities are involved in modern developments in data analysis. Of these, perhaps the most important are statistics and computing science. These two disciplines have different emphases, different strengths, and different weaknesses. Statistical data analysis has the merit of a clear and principled theoretical base, but this has been earned at the cost of restricting the class of problems to which the methods may be applied. In contrast, data analysis derived from a computing science background often lacks a sound theoretical base, but this frees it to tackle a wider range of problems. This talk looks at the historical contexts from which the two approaches arose, and examines some of the similarities and differences between them.
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Hand, D.J. (2002). Modern Data Analysis: A Clash of Paradigms. In: Gaul, W., Ritter, G. (eds) Classification, Automation, and New Media. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55991-4_8
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DOI: https://doi.org/10.1007/978-3-642-55991-4_8
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