Data Theory by Simon Lindgren

Author:Simon Lindgren [Lindgren, Simon]
Language: eng
Format: epub
Publisher: Wiley
Published: 2020-09-02T00:00:00+00:00

By comparison with more conventionally ‘qualitative’ approaches, distant reading demands that the researcher is prepared to move away from conventional close reading in order to be able to grasp larger sets of data, and also to lose some degree of ‘qualitative’ detail because of this. But, importantly, a distant reading does not make the element of ‘qualitative’ interpretation any less relevant, as the patterns we find still need to be contextualised and understood. However, while it comes from the field of computational literary studies, the idea of distant reading is not alien to what data science wants to achieve. As explained by data scientists John Kelleher and Brendan Tierney:

If a human expert can easily create a pattern in his or her own mind, it is generally not worth the time and effort of using data science to ‘discover’ it. In general, data science becomes useful when we have a large number of data examples and when the patterns are too complex for humans to discover and extract manually.

(Kelleher and Tierney, 2018, p. 4)

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