IS 417 Data Science in the Humanities

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Human culture provides an ideal testing platform for data science students because the explanatory problems lurking beneath the surface in other areas become apparent here. For example, cultural materials usually appear in analysts' hands in the form of unstructured text, images, or audio files, which forces people to make clear decisions about data modeling and feature extraction. Cultural issues also highlight the importance of explaining statistical patterns related to social context. Last but not least, songs, poems, and stories present us with lively and interesting challenges that are inherently fun to explore.

The IS 417 Data Science in the Humanities course will start with an overview of descriptive and logical statistics and gradually build supervised and unsupervised machine learning applications. We apply these techniques to various cultural materials and use them to simulate the speed of style change in popular music and express gender in novels.

A characteristic feature of our modern era is that a large amount of data captures the behavior of individuals, groups, organizations, cultures and even society as a whole. A large amount of data about modern people is accompanied by large-scale digitization of historical texts and digital historical data collected and stored for hundreds of years in the form of historical newspapers, literary and language corpora, economic data, population census and other government data, as well as libraries, academics and commercial organizations digitization, acquisition and customization.

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