Humanities Data Analysis (eBook)

Case Studies with Python
eBook Download: PDF
2021
360 Seiten
Princeton University Press (Verlag)
978-0-691-20033-0 (ISBN)

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Humanities Data Analysis -  Folgert Karsdorp,  Mike Kestemont,  Allen Riddell
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A practical guide to data-intensive humanities research using the Python programming languageThe 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 PythonApplicable to many humanities disciplines, including history, literature, and sociologyOffers real-world case studies using publicly available data setsProvides exercises at the end of each chapter for students to test acquired skillsEmphasizes visual storytelling via data visualizations
Erscheint lt. Verlag 12.1.2021
Zusatzinfo 69 color + 12 b/w illus. 5 tables.
Sprache englisch
Themenwelt Geisteswissenschaften Geschichte
Geisteswissenschaften Sprach- / Literaturwissenschaft Anglistik / Amerikanistik
Geisteswissenschaften Sprach- / Literaturwissenschaft Literaturwissenschaft
Mathematik / Informatik Informatik Datenbanken
Mathematik / Informatik Informatik Programmiersprachen / -werkzeuge
Mathematik / Informatik Informatik Software Entwicklung
Mathematik / Informatik Informatik Theorie / Studium
Mathematik / Informatik Informatik Web / Internet
Mathematik / Informatik Mathematik
Sozialwissenschaften Kommunikation / Medien Medienwissenschaft
Schlagworte Accuracy and precision • Addition • American Civil War • Analyzing Linguistic Data • Annotation • Array data structure • authorial styles • authorship controversy • Bayesian • Bayesian inference • Bayes' Theorem • Bigram • Binomial Distribution • Box Plot • Calculation • Case study • Categorical Distribution • Categorical variable • Chain letter • child naming practices • Civil War • civil war battles • classical French drama • cluster analysis • code blocks • Cohen's kappa • Computation • Computational resource • Conditional (computer programming) • content analysis • Corpus Linguistics • Cosine similarity • Data Analysis • data carpentry • Data Exchange • data gathering • data humanities • data model • Data Science • data set • diachronic developments • disputed authorship • Distance matrix • Document-term matrix • expectation–maximization algorithm • Exploratory data analysis • Family resemblance • For loop • Function Word • Garrett Grolemund • Genre • Geographic Maps • Hadley Wickham • Handbook • hierarchical clustering • High- and low-level • histogram • historical cookbooks • HTML • Humanities • Humanities Data in R • inference • Information Theory • ingredient • Instance (computer science) • Interquartile range • Jake VanderPlas • JSON • Latent Dirichlet Allocation • Lauren Tilton • Least Squares • Lexical Analysis • LibreOffice Calc • Literary Theory • Literature • machine learning • Matthew Jockers • mixed-membership models • Mixture model • Namespace • Naming convention (programming) • negative binomial distribution • Normal distribution • NumPy • pairwise • Pairwise comparison • Pandas • Pandas (software) • Parameter (computer programming) • parsing • Pattern Matching • plain text • Principal Component Analysis • Probability • Probability Distribution • Probability Theory • Processing (programming language) • programming • Publication • Punctuation • Python • Python Data Science Handbook • Python for Data Analysis • Python (programming language) • Quantitative Data Analysis • quantitative research • Random Variable • Ranking (information retrieval) • Recipe • Respondent • result • R for Data Science • R. H. Baayen • scientific ecosystem • scikit-learn • sorting algorithm • Source lines of code • Standard Library • Statistic • Statistical classification • Statistics • Statistics for Linguistics with R • Stefan Gries • stemming • Stylometric Analysis • stylometry • Subset • summary statistics • Syntax error • Tabular Data • Taxicab geometry • Taylor Arnold • Text Analysis with R for Students of Literature • Text corpus • The Federalist Papers • Time Series Analysis • topical shifts • topic model • topic modeling • topic models • US Supreme Court • US Supreme Court decisions • Variable (computer science) • Variable (mathematics) • Vector Space • vector space model • Visualization Techniques • visual storytelling • vocabulary • Wes McKinney • Writing • Writing Style • Writing Styles • XML
ISBN-10 0-691-20033-5 / 0691200335
ISBN-13 978-0-691-20033-0 / 9780691200330
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