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Humanities Data Analysis

Folgert Karsdorp | Mike Kestemont | Allen Riddell
€62.99
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A01=Folgert Karsdorp
A01=Mike Kestemont
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automatic-update
Bayes' theorem
Bayesian
Bayesian inference
Bigram
Calculation
Case study
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Chain letter
Cluster analysis
COP=United States
Cosine similarity
Data analysis
Data model
Data set
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Document-term matrix
eq_bestseller
eq_biography-true-stories
eq_computing
eq_history
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Exploratory data analysis
Family resemblance
For loop
Function word
Genre
Handbook
Hierarchical clustering
Histogram
HTML
Inference
Ingredient
Instance (computer science)
Interquartile range
JSON
Language_English
Latent Dirichlet allocation
Least squares
LibreOffice Calc
Machine learning
Mixture model
Namespace
Naming convention (programming)
Normal distribution
NumPy
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Pandas (software)
Parameter (computer programming)
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Principal component analysis
Probability
Probability distribution
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Python (programming language)
Quantitative research
Random variable
Ranking (information retrieval)
Recipe
Respondent
Result
Scikit-learn
softlaunch
Source lines of code
Statistic
Statistical classification
Statistics
Stylometry
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Taxicab geometry
Text corpus
Topic model
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Vector space model
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XML

Product details

  • ISBN 9780691172361
  • Dimensions: 178 x 254mm
  • Publication Date: 12 Jan 2021
  • Publisher: Princeton University Press
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Description

A practical guide to data-intensive humanities research using the Python programming language

The use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials. Despite this growth, there are few resources addressed to students and scholars who wish to take advantage of these powerful tools. Humanities Data Analysis offers the first intermediate-level guide to quantitative data analysis for humanities students and scholars using the Python programming language. This practical textbook, which assumes a basic knowledge of Python, teaches readers the necessary skills for conducting humanities research in the rapidly developing digital environment.

The book begins with an overview of the place of data science in the humanities, and proceeds to cover data carpentry: the essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. Then, drawing from real-world, publicly available data sets that cover a variety of scholarly domains, the book delves into detailed case studies. Focusing on textual data analysis, the authors explore such diverse topics as network analysis, genre theory, onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. Exercises and resources for further reading are provided at the end of each chapter.

An ideal resource for humanities students and scholars aiming to take their Python skills to the next level, Humanities Data Analysis illustrates the benefits that quantitative methods can bring to complex research questions.

  • Appropriate for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python
  • Applicable to many humanities disciplines, including history, literature, and sociology
  • Offers real-world case studies using publicly available data sets
  • Provides exercises at the end of each chapter for students to test acquired skills
  • Emphasizes visual storytelling via data visualizations
About Allen Riddell | Folgert Karsdorp | Mike Kestemont
Folgert Karsdorp is a postdoctoral researcher at the Meertens Institute of the Royal Netherlands Academy of Arts and Sciences. Mike Kestemont is assistant professor of literature at the University of Antwerp. Twitter @Mike_Kestemont Allen Riddell is assistant professor of information science at Indiana University.

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