Humanities Data Analysis: Case Studies with Python

Humanities Data Analysis is a practical guide to data-intensive humanities research using Python. It covers data carpentry, quantitative methods, and case studies in textual data analysis, network analysis, genre theory, onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. The book is suitable for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python and is applicable to many humanities disciplines. It emphasizes visual storytelling via data visualizations.

Format: Hardback
Length: 360 pages
Publication date: 12 January 2021
Publisher: Princeton University Press

The use of quantitative methods in the humanities and related social sciences has experienced a significant surge in recent years, granting researchers the ability to uncover patterns in a vast array of source materials. Despite this remarkable growth, there are limited resources available to students and scholars seeking to leverage these powerful tools. Humanities Data Analysis presents the first intermediate-level guide to quantitative data analysis for humanities students and scholars utilizing the Python programming language. This practical textbook, designed for individuals with a foundational understanding of Python, equips readers with the essential skills required for conducting humanities research in the rapidly evolving digital landscape.

The book begins by providing an overview of the role of data science in the humanities, followed by a comprehensive exploration of data carpentry, which encompasses the fundamental techniques for collecting, cleaning, representing, and transforming textual and tabular data. Subsequently, the book delves into detailed case studies, drawing from real-world, publicly available data sets spanning various scholarly domains. With a particular focus on textual data analysis, the authors examine a wide range of topics, including network analysis, genre theory,onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. Each chapter concludes with exercises and additional resources for further reading.

Ideal for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python, Humanities Data Analysis serves as a valuable resource for those seeking to enhance their Python skills and apply them to complex research inquiries.

Weight: 1178g
Dimension: 185 x 262 x 27 (mm)
ISBN-13: 9780691172361