Humanities data analysis: case studies with Python
"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 t...
Gespeichert in:
| Hauptverfasser: | , , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Princeton ; Oxford
Princeton University Press
[2021]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "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"-- |
| Beschreibung: | xi, 337 Seiten Diagramme, Karten |
| ISBN: | 9780691172361 |
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| 520 | 3 | |a "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. | |
| 520 | 3 | |a 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. | |
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| adam_text | Contents Preface I ix Data Analysis Essentials Chapter 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 Quantitative Data Analysis and the Humanities Overview of the Book Related Books How to Use This Book 1.4.1 What you should know 1.4.2 Packages and data 1.4.3 Exercises An Exploratory Data Analysis of the United States’ Culinary History Cooking with Tabular Data Taste Trends in Culinary USHistory America’s Culinary Melting Pot Further Reading Chapter 2 2.1 2.2 2.3 2.4 2.5 2.6 Introduction Parsing and Manipulating Structured Data Introduction Plain Text CSV PDF JSON XML 2.6.1 Parsing XML 2.6.2 Creating XML 2.6.3 TEI 2.7 HTML 2.7.1 Retrieving HTML from the web 1 3 3 5 6 7 8 12 13 13 14 18 26 30 32 32 33 36 40 43 46 48 51 56 57 64
vi · Contents 2.8 Extracting Character Interaction Networks 2.9 Conclusion and Further Reading Chapter 3 Exploring Texts Using the Vector Space Model 3.1 Introduction 3.2 From Texts to Vectors 3.2.1 Text preprocessing 3.3 Mapping Genres 3.3.1 Computing distances between documents 3.3.2 Nearest neighbors 3.4 Further Reading 3.5 Appendix: Vectorizing Texts with NumPy 3.5.1 Constructing arrays 3.5.2 Indexing and slicing arrays 3.5.3 Aggregating functions 3.5.4 Array broadcasting Chapter 4 Processing Tabular Data 4.1 Loading, Inspecting, and Summarizing Tabular Data 4.1.1 Reading tabular data with Pandas 4.2 Mapping Cultural Change 4.2.1 Turnover in naming practices 4.2.2 Visualizing turnovers 4.3 Changing Naming Practices 4.3.1 Increasing name diversity 4.3.2 A bias for names ending in w? 4.3.3 Unisex names in the United States 4.4 Conclusions and Further Reading II Advanced Data Analysis Chapter 5 Statistics Essentials: Who Reads Novels? 5.1 Introduction 5.2 Statistics 5.3 Summarizing Location and Dispersion 5.3.1 Data: Novel reading in the UnitedStates 5.4 Location 5.5 Dispersion 5.5.1 Variation in categorical values 5.6 Measuring Association 5.6.1 Measuring association between numbers 5.6.2 Measuring association between categories 5.6.3 Mutual information 5.7 Conclusion 5.8 Further Reading 65 74 78 78 79 81 90 97 107 111 113 113 117 120 122 126 127 130 136 136 146 149 150 153 158 162 165 169 169 170 171 171 175 179 184 188 188 192 195 197 198
Contents · vii Chapter 6 Introduction to Probability 6.1 Uncertainty and Thomas Pynchon 6.2 Probability 6.2.1 Probability and degree of belief 6.3 Example: Bayes’s Rule and AuthorshipAttribution 6.3.1 Random variables and probabilitydistributions 6.4 Further Reading 6.5 Appendix 6.5.1 Bayes’s rule 6.5.2 Fitting a negative binomial distribution Chapter 7 Narrating with Maps 7.1 7.2 7.3 7.4 7.5 7.6 Introduction Data Preparations Projections and Basemaps Plotting Battles Mapping the Development of the War Further Reading Chapter 8 Stylometry and the Voice of Hildegard 8.1 Introduction 8.2 Authorship Attribution 8.2.1 Burrows’s Delta 8.2.2 Function words 8.2.3 Computing document distances with Delta 8.2.4 Authorship attribution evaluation 8.3 Hierarchical Agglomerative Clustering 8.4 Principal Component Analysis 8.4.1 Applying PCA 8.4.2 The intuition behind PCA 8.4.3 Loadings 8.5 Conclusions 8.6 Further Reading Chapter 9 A Topic Model of United States Supreme Court Opinions, 1900-2000 9.1 Introduction 9.2 Mixture Models: Artwork Dimensions in the Tate Galleries 9.3 Mixed-Membership Model of Texts 9.3.1 Parameter estimation 9.3.2 Checking an unsupervised model 9.3.3 Modeling different word senses 9.3.4 Exploring trends over time in theSupreme Court 9.4 Conclusion 201 202 203 205 208 213 225 227 227 228 229 229 230 233 236 238 244 248 248 250 252 254 257 260 262 266 268 271 274 280 280 285 28 5 287 294 300 304 309 313 317
viii · Contents 9.5 Further Reading 9.6 Appendix: Mapping Between Our Topic Model and Lauderdale andClark (2014) 318 320 Epilogue: Good Enough Practices 323 Bibliography Index 325 333
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| adam_txt |
Contents Preface I ix Data Analysis Essentials Chapter 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 Quantitative Data Analysis and the Humanities Overview of the Book Related Books How to Use This Book 1.4.1 What you should know 1.4.2 Packages and data 1.4.3 Exercises An Exploratory Data Analysis of the United States’ Culinary History Cooking with Tabular Data Taste Trends in Culinary USHistory America’s Culinary Melting Pot Further Reading Chapter 2 2.1 2.2 2.3 2.4 2.5 2.6 Introduction Parsing and Manipulating Structured Data Introduction Plain Text CSV PDF JSON XML 2.6.1 Parsing XML 2.6.2 Creating XML 2.6.3 TEI 2.7 HTML 2.7.1 Retrieving HTML from the web 1 3 3 5 6 7 8 12 13 13 14 18 26 30 32 32 33 36 40 43 46 48 51 56 57 64
vi · Contents 2.8 Extracting Character Interaction Networks 2.9 Conclusion and Further Reading Chapter 3 Exploring Texts Using the Vector Space Model 3.1 Introduction 3.2 From Texts to Vectors 3.2.1 Text preprocessing 3.3 Mapping Genres 3.3.1 Computing distances between documents 3.3.2 Nearest neighbors 3.4 Further Reading 3.5 Appendix: Vectorizing Texts with NumPy 3.5.1 Constructing arrays 3.5.2 Indexing and slicing arrays 3.5.3 Aggregating functions 3.5.4 Array broadcasting Chapter 4 Processing Tabular Data 4.1 Loading, Inspecting, and Summarizing Tabular Data 4.1.1 Reading tabular data with Pandas 4.2 Mapping Cultural Change 4.2.1 Turnover in naming practices 4.2.2 Visualizing turnovers 4.3 Changing Naming Practices 4.3.1 Increasing name diversity 4.3.2 A bias for names ending in w? 4.3.3 Unisex names in the United States 4.4 Conclusions and Further Reading II Advanced Data Analysis Chapter 5 Statistics Essentials: Who Reads Novels? 5.1 Introduction 5.2 Statistics 5.3 Summarizing Location and Dispersion 5.3.1 Data: Novel reading in the UnitedStates 5.4 Location 5.5 Dispersion 5.5.1 Variation in categorical values 5.6 Measuring Association 5.6.1 Measuring association between numbers 5.6.2 Measuring association between categories 5.6.3 Mutual information 5.7 Conclusion 5.8 Further Reading 65 74 78 78 79 81 90 97 107 111 113 113 117 120 122 126 127 130 136 136 146 149 150 153 158 162 165 169 169 170 171 171 175 179 184 188 188 192 195 197 198
Contents · vii Chapter 6 Introduction to Probability 6.1 Uncertainty and Thomas Pynchon 6.2 Probability 6.2.1 Probability and degree of belief 6.3 Example: Bayes’s Rule and AuthorshipAttribution 6.3.1 Random variables and probabilitydistributions 6.4 Further Reading 6.5 Appendix 6.5.1 Bayes’s rule 6.5.2 Fitting a negative binomial distribution Chapter 7 Narrating with Maps 7.1 7.2 7.3 7.4 7.5 7.6 Introduction Data Preparations Projections and Basemaps Plotting Battles Mapping the Development of the War Further Reading Chapter 8 Stylometry and the Voice of Hildegard 8.1 Introduction 8.2 Authorship Attribution 8.2.1 Burrows’s Delta 8.2.2 Function words 8.2.3 Computing document distances with Delta 8.2.4 Authorship attribution evaluation 8.3 Hierarchical Agglomerative Clustering 8.4 Principal Component Analysis 8.4.1 Applying PCA 8.4.2 The intuition behind PCA 8.4.3 Loadings 8.5 Conclusions 8.6 Further Reading Chapter 9 A Topic Model of United States Supreme Court Opinions, 1900-2000 9.1 Introduction 9.2 Mixture Models: Artwork Dimensions in the Tate Galleries 9.3 Mixed-Membership Model of Texts 9.3.1 Parameter estimation 9.3.2 Checking an unsupervised model 9.3.3 Modeling different word senses 9.3.4 Exploring trends over time in theSupreme Court 9.4 Conclusion 201 202 203 205 208 213 225 227 227 228 229 229 230 233 236 238 244 248 248 250 252 254 257 260 262 266 268 271 274 280 280 285 28 5 287 294 300 304 309 313 317
viii · Contents 9.5 Further Reading 9.6 Appendix: Mapping Between Our Topic Model and Lauderdale andClark (2014) 318 320 Epilogue: Good Enough Practices 323 Bibliography Index 325 333 |
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| spelling | Karsdorp, Folgert 1983- Verfasser (DE-588)1229662871 aut Humanities data analysis case studies with Python Folgert Karsdorp, Mike Kestemont & Allen Riddell Princeton ; Oxford Princeton University Press [2021] © 2021 xi, 337 Seiten Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier "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"-- Humanities Research Methodology Social sciences Research Methodology Quantitative research Data processing Python (Computer program language) Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Digital Humanities (DE-588)1038714850 gnd rswk-swf Digital Humanities (DE-588)1038714850 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Kestemont, Mike 1985- Verfasser (DE-588)1060659425 aut Riddell, Allen 19XX- Verfasser (DE-588)1234027968 aut Erscheint auch als Online-Ausgabe 978-0-691-20033-0 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032532673&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title_full_unstemmed | Humanities data analysis case studies with Python Folgert Karsdorp, Mike Kestemont & Allen Riddell |
| title_short | Humanities data analysis |
| title_sort | humanities data analysis case studies with python |
| title_sub | case studies with Python |
| topic | Humanities Research Methodology Social sciences Research Methodology Quantitative research Data processing Python (Computer program language) Python Programmiersprache (DE-588)4434275-5 gnd Digital Humanities (DE-588)1038714850 gnd |
| topic_facet | Humanities Research Methodology Social sciences Research Methodology Quantitative research Data processing Python (Computer program language) Python Programmiersprache Digital Humanities |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032532673&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT karsdorpfolgert humanitiesdataanalysiscasestudieswithpython AT kestemontmike humanitiesdataanalysiscasestudieswithpython AT riddellallen humanitiesdataanalysiscasestudieswithpython |