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Humanities Data Analysis: Case Studies with Python [Kõva köide]

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  • Formaat: Hardback, 360 pages, kõrgus x laius: 254x178 mm, 69 color + 12 b/w illus. 5 tables.
  • Ilmumisaeg: 12-Jan-2021
  • Kirjastus: Princeton University Press
  • ISBN-10: 0691172366
  • ISBN-13: 9780691172361
Teised raamatud teemal:
Humanities Data Analysis: Case Studies with PythonSuurem pilt
  • Formaat: Hardback, 360 pages, kõrgus x laius: 254x178 mm, 69 color + 12 b/w illus. 5 tables.
  • Ilmumisaeg: 12-Jan-2021
  • Kirjastus: Princeton University Press
  • ISBN-10: 0691172366
  • ISBN-13: 9780691172361
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780691172361.html
Märksõnad:
"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"--

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
Preface ix
I Data Analysis Essentials
1(164)
Chapter 1 Introduction
3(29)
1.1 Quantitative Data Analysis and the Humanities
3(2)
1.2 Overview of the Book
5(1)
1.3 Related Books
6(1)
1.4 How to Use This Book
7(6)
1.4.1 What you should know
8(4)
1.4.2 Packages and data
12(1)
1.4.3 Exercises
13(1)
1.5 An Exploratory Data Analysis of the United States' Culinary History
13(1)
1.6 Cooking with Tabular Data
14(4)
1.7 Taste Trends in Culinary US History
18(8)
1.8 America's Culinary Melting Pot
26(4)
1.9 Further Reading
30(2)
Chapter 2 Parsing and Manipulating Structured Data
32(46)
2.1 Introduction
32(1)
2.2 Plain Text
33(3)
2.3 CSV
36(4)
2.4 PDF
40(3)
2.5 JSON
43(3)
2.6 XML
46(11)
2.6.1 Parsing XML
48(3)
2.6.2 Creating XML
51(5)
2.6.3 TEI
56(1)
2.7 HTML
57(8)
2.7.1 Retrieving HTML from the web
64(1)
2.8 Extracting Character Interaction Networks
65(9)
2.9 Conclusion and Further Reading
74(4)
Chapter 3 Exploring Texts Using the Vector Space Model
78(48)
3.1 Introduction
78(1)
3.2 From Texts to Vectors
79(11)
3.2.1 Text preprocessing
81(9)
3.3 Mapping Genres
90(21)
3.3.1 Computing distances between documents
97(10)
3.3.2 Nearest neighbors
107(4)
3.4 Further Reading
111(2)
3.5 Appendix: Vectorizing Texts with NumPy
113(13)
3.5.1 Constructing arrays
113(4)
3.5.2 Indexing and slicing arrays
117(3)
3.5.3 Aggregating functions
120(2)
3.5.4 Array broadcasting
122(4)
Chapter 4 Processing Tabular Data
126(39)
4.1 Loading, Inspecting, and Summarizing Tabular Data
127(9)
4.1.1 Reading tabular data with Pandas
130(6)
4.2 Mapping Cultural Change
136(13)
4.2.1 Turnover in naming practices
136(10)
4.2.2 Visualizing turnovers
146(3)
4.3 Changing Naming Practices
149(13)
4.3.1 Increasing name diversity
150(3)
4.3.2 A bias for names ending in w?
153(5)
4.3.3 Unisex names in the United States
158(4)
4.4 Conclusions and Further Reading
162(3)
II Advanced Data Analysis
165(158)
Chapter 5 Statistics Essentials: Who Reads Novels?
169(32)
5.1 Introduction
169(1)
5.2 Statistics
170(1)
5.3 Summarizing Location and Dispersion
171(4)
5.3.1 Data: Novel reading in the United States
171(4)
5.4 Location
175(4)
5.5 Dispersion
179(9)
5.5.1 Variation in categorical values
184(4)
5.6 Measuring Association
188(9)
5.6.1 Measuring association between numbers
188(4)
5.6.2 Measuring association between categories
192(3)
5.6.3 Mutual information
195(2)
5.7 Conclusion
197(1)
5.8 Further Reading
198(3)
Chapter 6 Introduction to Probability
201(28)
6.1 Uncertainty and Thomas Pynchon
202(1)
6.2 Probability
203(5)
6.2.1 Probability and degree of belief
205(3)
6.3 Example: Bayes's Rule and Authorship Attribution
208(17)
6.3.1 Random variables and probability distributions
213(12)
6.4 Further Reading
225(2)
6.5 Appendix
227(2)
6.5.1 Bayes's rule
227(1)
6.5.2 Fitting a negative binomial distribution
228(1)
Chapter 7 Narrating with Maps
229(19)
7.1 Introduction
229(1)
7.2 Data Preparations
230(3)
7.3 Projections and Basemaps
233(3)
7.4 Plotting Battles
236(2)
7.5 Mapping the Development of the War
238(6)
7.6 Further Reading
244(4)
Chapter 8 Stylometry and the Voice of Hildegard
248(37)
8.1 Introduction
248(2)
8.2 Authorship Attribution
250(12)
8.2.1 Burrows's Delta
252(2)
8.2.2 Function words
254(3)
8.2.3 Computing document distances with Delta
257(3)
8.2.4 Authorship attribution evaluation
260(2)
8.3 Hierarchical Agglomerative Clustering
262(4)
8.4 Principal Component Analysis
266(14)
8.4.1 Applying PCA
268(3)
8.4.2 The intuition behind PCA
271(3)
8.4.3 Loadings
274(6)
8.5 Conclusions
280(1)
8.6 Further Reading
280(5)
Chapter 9 A Topic Model of United States Supreme Court Opinions, 1900-2000
285(38)
9.1 Introduction
285(2)
9.2 Mixture Models: Artwork Dimensions in the Tate Galleries
287(7)
9.3 Mixed-Membership Model of Texts
294(23)
9.3.1 Parameter estimation
300(4)
9.3.2 Checking an unsupervised model
304(5)
9.3.3 Modeling different word senses
309(4)
9.3.4 Exploring trends over time in the Supreme Court
313(4)
9.4 Conclusion
317(1)
9.5 Further Reading
318(2)
9.6 Appendix: Mapping Between Our Topic Model and Lauderdale and Clark (2014)
320(3)
Epilogue: Good Enough Practices 323(2)
Bibliography 325(8)
Index 333
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.