Doing computational social science: a practical introduction
"Computational approaches offer exciting opportunities for us to do social science differently. This beginner's guide discusses a range of computational methods and how to use them to study the problems and questions you want to research. It assumes no knowledge of programming, offering st...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Los Angeles
SAGE
[2022]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "Computational approaches offer exciting opportunities for us to do social science differently. This beginner's guide discusses a range of computational methods and how to use them to study the problems and questions you want to research. It assumes no knowledge of programming, offering step-by-step guidance for coding in Python and drawing on examples of real data analysis to demonstrate how you can apply each approach, including machine learning and social network analysis, in any discipline. The book also: Considers important principles of social scientific computing, including transparency, accountability and reproducibility. Understands the realities of completing research projects and offers advice for dealing with issues such as messy or incomplete data and systematic biases. Teaches you good habits and working practices that enable you to do programming well. This book is for anyone who wants to use computational methods to conduct a social science research project. Supported by a wealth of online resources, including video tutorials and datasets for practice so you can learn at your own pace, this book equips you with the skills to conduct computational social science research for the first time, with confidence" |
| Beschreibung: | XV, 667 Seiten Illustrationen, Diagramme |
| ISBN: | 9781526468185 1526468182 9781526468192 1526468190 |
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Datensatz im Suchindex
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CONTENTS Discover Your Online Resources! Acknowledgements About the Author Introduction: Learning to Do Computational Social Science 0.1 0.2 0.3 0.4 0.5 Who Is This Book For? Roadmap Datasets Used in This Book Learning Materials Conclusion Part I Foundations xii xiv xvi 1 1 5 6 9 10 11 1 Setting Up Your Open Source Scientific Computing Environment 13 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 Learning Objectives Introduction Command Line Computing Open Source Software Version Control Tools Virtualization Tools Putting the Pieces Together: Python, Jupyter, conda, and git Conclusion 13 13 14 18 22 24 26 27 2 Python Programming:The Basics 28 2.1 2.2 2.3 2.4 2.5 2.6 Learning Objectives Learning Materials Introduction Learning Python Python Foundations Conclusion 28 28 28 29 29 43 3 Python Programming:Data Structures, Functions, and Files 44 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 Learning Objectives Learning Materials Introduction Working With Python's Data Structures Custom Functions Reading and Writing Files Pace Yourself Conclusion 44 44 44 45 58 60 61 62
VI j CONTENTS 4 Collecting Data From Application Programming Interfaces 63 4.1 4.2 4.3 4.4 4.5 4.6 Learning Objectives Learning Materials Introduction What Is an API? Getting Practical: Working With APIs Conclusion 63 63 63 64 67 73 5 Collecting Data From the Web: Scraping 74 5.1 5.2 5.3 5.4 5.5 5.6 5.7 Learning Objectives Learning Materials Introduction An HTML and CSS Primer for Web Scrapers Developing Your First Web Scraper Ethical and Legal Issues in Web Scraping Conclusion 74 74 74 75 77 86 87 б Processing Structured Data 88 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 Learning Objectives Learning Materials Introduction Practical Pandas: First Steps Understanding Pandas Data Structures Aggregation and Grouped Operations Working With Time-Series Data Combining Dataframes Conclusion 88 88 88 89 95 100 103 109 112 7 Visualization and Exploratory Data Analysis 113 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 Learning Objectives Learning Materials Introduction Iterative Research Workflows: EDA and Box's Loop Effective Visualization Univariate EDA: Describing and Visualizing Distributions Multivariate EDA Conclusion 113 113 113 114 115 118 128 144 8 Latent Factors and Components 146 8.1 8.2 8.3 8.4 8.5 8.6 Learning Objectives Learning Materials Introduction Latent Variables and the Curse of Dimensionality Conducting a Principal Component Analysis in Sklearn Conclusion 146 146 146 149 154 162
CONTENTS į VII Part II Fundamentals of Text Analysis 163 Processing Natural Language Data 165 9 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 Learning Objectives Learning Materials Introduction Text Processing Normalizing Text via Lemmatization Part-of-Speech Tagging Syntactic Dependency Parsing Conclusion 165 165 165 166 172 174 177 181 10 Iterative Text Analysis 182 10.1 10.2 10.3 10.4 10.5 10.6 10.7 182 182 182 183 187 190 193 11 11.1 11.2 11.3 11.4 11.5 11.6 Learning Objectives Learning Materials Introduction Exploration in Context: Text Analysis Pipelines Count-Based Feature Extraction: From Strings to a Bag of Words Close Reading Conclusion Exploratory Text Analysis - Working With Word Frequencies and Proportions Learning Objectives Learning Materials Introduction Scaling Up: Processing Political Speeches Creating DTMs With Sklearn Conclusion 12 Exploratory Text Analysis - Word Weights, Text Similarity, and Latent Semantic Analysis 12.1 12.2 12.3 12.4 12.5 194 Learning Objectives Learning Materials Introduction Exploring Latent Semantic SpaceWith Matrix Decomposition Conclusion 194 194 194 195 202 211 212 212 212 212 220 226 Part III Fundamentals of Network Analysis 227 13 Social Networks and Relational Thinking 229 13.1 13.2 13.3 13.4 13.5 13.6 13.7 229 229 229 230 236 240 243 Learning Objectives Learning Materials Introduction What Are Social Networks? Working With Relational Data Walk Structure and Network Flow Conclusion
VIII { CONTENTS 14 Connection and Clustering in Social Networks 244 14.1 14.2 14.3 14.4 14.5 14.6 244 244 244 246 255 269 Learning Objectives Learning Materials Introduction Micro-Level Network Structure and Processes Detecting Cohesive Subgroupsand Assortative Structure Conclusion 15 Influence, Inequality, and Power in Social Networks 270 15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8 15.9 270 270 270 272 274 278 282 295 295 Learning Objectives Learning Materials Introduction Centrality Measures: The Big Picture Shortest Paths and Network Flow Betweenness Centrality, Two Ways Popularity, Power, and Influence Conclusion Chapter Appendix 16 Going Viral: Modelling the Epidemic Spread of Simple Contagions 298 16.1 16.2 16.3 16.4 16.5 16.6 16.7 298 298 298 299 301 305 319 Learning Objectives Learning Materials Introduction Epidemic Spread and Diffusion Modelling Spreading Processes With NDlib Simple Contagions and Epidemic Spread Conclusion 17 Not So Fast: Modelling the Diffusion of Complex Contagions 320 17.1 17.2 17.3 17.4 17.5 17.6 17.7 320 320 320 321 323 325 335 Learning Objectives Learning Materials Introduction From Simple to Complex Contagions Beyond Local Neighbourhoods: Network Effects and Thresholds Threshold Models for Complex Contagions Conclusion Part IV Research Ethics and Machine Learning 337 18 Research Ethics, Politics, and Practices 339 18.1 18.2 18.3 18.4 18.5 18.6 339 339 339 340 342 346 Learning Objectives Learning Materials Introduction Research Ethics and Social Network Analysis Informed Consent, Privacy, and Transparency Bias and Algorithmic Decision-Making
CONTENTS ļ IX 18.7 Ditching the Value-Free Ideal for Ethics, Politics, and Science 18.8 Conclusion 348 352 19 Machine Learning: Symbolic and Connectionist 353 Learning Objectives Learning Materials Introduction Types of Machine Learning Symbolic and Connectionist Machine Learning Conclusion 353 353 353 354 359 363 19.1 19.2 19.3 19.4 19.5 19.6 20 ISupervised Learning With Regression and Cross-validation 364 Learning Objectives Learning Materials Introduction Supervised Learning With Linear Regression Classification With Logistic Regression Conclusion 364 364 364 367 375 377 21 !Supervised Learning With Tree-Based Models 378 20.1 20.2 20.3 20.4 20.5 20.6 21.1 21.2 21.3 21.4 21.5 21.6 21.7 Learning Objectives Learning Materials Introduction Rules-Based Learning With Trees Ensemble Learning Evaluation Beyond Accuracy Conclusion 22 Neural Networks and Deep Learning 22.1 22.2 22.3 22.4 22.5 22.6 22.7 22.8 Learning Objectives Learning Materials Introduction The Perceptron Multilayer Perceptrons Training ANNs With Backpropagation and Gradient Descent More Complex ANN Architectures Conclusion 378 378 378 379 384 390 393 394 394 394 394 395 397 400 405 410 23 1Developing Neural Network Models With Keras and TensorFlow 411 23.1 23.2 23.3 23.4 23.5 23.6 411 411 411 415 419 429 Learning Objectives Learning Materials Introduction Getting Started With Keras End-to-End Neural Network Modelling Conclusion
X ļ CONTENTS Part V Bayesian Data Analysis and Generative Modelling with Probabilistic Programming 431 24 Statistical Machine Learning and Generative Models 433 Learning Objectives Learning Materials Introduction Statistics, Machine Learning, and Statistical Machine Learning: Where Are the Boundaries and What Do They Bind? 24.5 Generative Versus Discriminative Models 24.6 Conclusion 433 433 433 25 Probability: A Primer 444 25.1 Learning Objectives 25.2 Learning Materials 25.3 Introduction 25.4 Foundational Concepts in Probability Theory 25.5 Probability Distributions and Likelihood Functions 25.6 Continuous Distributions, Probability DensityFunctions 25.7 Joint and Conditional Probabilities 25.8 Bayesian Inference 25.9 Posterior Probability 25.10 Conclusion 444 444 444 445 447 453 456 458 463 463 26 Approximate Posterior Inference With Stochastic Sampling and MCMC 464 24.1 24.2 24.3 24.4 26.1 26.2 26.3 26.4 26.5 26.6 27 Bayesian Regression Models With Probabilistic Programming Learning Objectives Learning Materials Introduction Developing Our Bayesian Model Conclusion 28 Bayesian Hierarchical Regression Modelling 28.1 28.2 28.3 28.4 28.5 464 464 464 465 471 477 Learning Objectives Learning Materials Introduction Bayesian Regression Stochastic Sampling Methods Conclusion Part VI Probabilistic Programming and Bayesian Latent Variable Models for Structured, Relational, and Text Data 27.1 27.2 27.3 27.4 27.5 434 441 443 Learning Objectives Learning Materials Introduction So, What's a Hierarchical Model? Goldilocks and the Three Pools 479 481 481 481 481 485 502 503 503 503 503
504 505
CONTENTS I XI 28.6 The Best Model Our Data Can Buy 28.7 The Fault in Our (Lack of) Stars 28.8 Conclusion 523 531 532 29 Variational Bayes and the Craft of Generative Topic Modelling 533 29.1 29.2 29.3 29.4 29.5 29.6 533 533 533 534 545 557 Learning Objectives Learning Materials Introduction Generative Topic Models Topic Modelling With Gensim Conclusion 30 Generative Network Analysis With Bayesian Stochastic Block Models 30.1 30.2 30.3 30.4 30.5 560 560 560 565 587 Learning Objectives Learning Materials Introduction Block Modelling With Graph-Tool Conclusion Part VII Embeddings, Transformer Models, and Named Entity Recognition 31 Can We Model Meaning? Contextual Representation and Neural Word Embeddings 31.1 31.2 31.3 31.4 31.5 31.6 31.7 31.8 31.9 560 Learning Objectives Learning Materials Introduction What Words Mean What Are Neural Word Embeddings? Cultural Cartography: Getting a Feel for Vector Space Learning Embeddings With Gensim Comparing Embeddings Conclusion 589 591 591 591 591 592 595 599 603 605 613 32 Named Entity Recognition, Transfer Learning, and Transformer Models 614 32.1 32.2 32.3 32.4 32.5 32.6 Learning Objectives Learning Materials Introduction Named Entity Recognition Transformer Models Conclusion References Index 614 614 614 615 625 643 644 656 |
| adam_txt |
CONTENTS Discover Your Online Resources! Acknowledgements About the Author Introduction: Learning to Do Computational Social Science 0.1 0.2 0.3 0.4 0.5 Who Is This Book For? Roadmap Datasets Used in This Book Learning Materials Conclusion Part I Foundations xii xiv xvi 1 1 5 6 9 10 11 1 Setting Up Your Open Source Scientific Computing Environment 13 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 Learning Objectives Introduction Command Line Computing Open Source Software Version Control Tools Virtualization Tools Putting the Pieces Together: Python, Jupyter, conda, and git Conclusion 13 13 14 18 22 24 26 27 2 Python Programming:The Basics 28 2.1 2.2 2.3 2.4 2.5 2.6 Learning Objectives Learning Materials Introduction Learning Python Python Foundations Conclusion 28 28 28 29 29 43 3 Python Programming:Data Structures, Functions, and Files 44 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 Learning Objectives Learning Materials Introduction Working With Python's Data Structures Custom Functions Reading and Writing Files Pace Yourself Conclusion 44 44 44 45 58 60 61 62
VI j CONTENTS 4 Collecting Data From Application Programming Interfaces 63 4.1 4.2 4.3 4.4 4.5 4.6 Learning Objectives Learning Materials Introduction What Is an API? Getting Practical: Working With APIs Conclusion 63 63 63 64 67 73 5 Collecting Data From the Web: Scraping 74 5.1 5.2 5.3 5.4 5.5 5.6 5.7 Learning Objectives Learning Materials Introduction An HTML and CSS Primer for Web Scrapers Developing Your First Web Scraper Ethical and Legal Issues in Web Scraping Conclusion 74 74 74 75 77 86 87 б Processing Structured Data 88 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 Learning Objectives Learning Materials Introduction Practical Pandas: First Steps Understanding Pandas Data Structures Aggregation and Grouped Operations Working With Time-Series Data Combining Dataframes Conclusion 88 88 88 89 95 100 103 109 112 7 Visualization and Exploratory Data Analysis 113 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 Learning Objectives Learning Materials Introduction Iterative Research Workflows: EDA and Box's Loop Effective Visualization Univariate EDA: Describing and Visualizing Distributions Multivariate EDA Conclusion 113 113 113 114 115 118 128 144 8 Latent Factors and Components 146 8.1 8.2 8.3 8.4 8.5 8.6 Learning Objectives Learning Materials Introduction Latent Variables and the Curse of Dimensionality Conducting a Principal Component Analysis in Sklearn Conclusion 146 146 146 149 154 162
CONTENTS į VII Part II Fundamentals of Text Analysis 163 Processing Natural Language Data 165 9 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 Learning Objectives Learning Materials Introduction Text Processing Normalizing Text via Lemmatization Part-of-Speech Tagging Syntactic Dependency Parsing Conclusion 165 165 165 166 172 174 177 181 10 Iterative Text Analysis 182 10.1 10.2 10.3 10.4 10.5 10.6 10.7 182 182 182 183 187 190 193 11 11.1 11.2 11.3 11.4 11.5 11.6 Learning Objectives Learning Materials Introduction Exploration in Context: Text Analysis Pipelines Count-Based Feature Extraction: From Strings to a Bag of Words Close Reading Conclusion Exploratory Text Analysis - Working With Word Frequencies and Proportions Learning Objectives Learning Materials Introduction Scaling Up: Processing Political Speeches Creating DTMs With Sklearn Conclusion 12 Exploratory Text Analysis - Word Weights, Text Similarity, and Latent Semantic Analysis 12.1 12.2 12.3 12.4 12.5 194 Learning Objectives Learning Materials Introduction Exploring Latent Semantic SpaceWith Matrix Decomposition Conclusion 194 194 194 195 202 211 212 212 212 212 220 226 Part III Fundamentals of Network Analysis 227 13 Social Networks and Relational Thinking 229 13.1 13.2 13.3 13.4 13.5 13.6 13.7 229 229 229 230 236 240 243 Learning Objectives Learning Materials Introduction What Are Social Networks? Working With Relational Data Walk Structure and Network Flow Conclusion
VIII { CONTENTS 14 Connection and Clustering in Social Networks 244 14.1 14.2 14.3 14.4 14.5 14.6 244 244 244 246 255 269 Learning Objectives Learning Materials Introduction Micro-Level Network Structure and Processes Detecting Cohesive Subgroupsand Assortative Structure Conclusion 15 Influence, Inequality, and Power in Social Networks 270 15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8 15.9 270 270 270 272 274 278 282 295 295 Learning Objectives Learning Materials Introduction Centrality Measures: The Big Picture Shortest Paths and Network Flow Betweenness Centrality, Two Ways Popularity, Power, and Influence Conclusion Chapter Appendix 16 Going Viral: Modelling the Epidemic Spread of Simple Contagions 298 16.1 16.2 16.3 16.4 16.5 16.6 16.7 298 298 298 299 301 305 319 Learning Objectives Learning Materials Introduction Epidemic Spread and Diffusion Modelling Spreading Processes With NDlib Simple Contagions and Epidemic Spread Conclusion 17 Not So Fast: Modelling the Diffusion of Complex Contagions 320 17.1 17.2 17.3 17.4 17.5 17.6 17.7 320 320 320 321 323 325 335 Learning Objectives Learning Materials Introduction From Simple to Complex Contagions Beyond Local Neighbourhoods: Network Effects and Thresholds Threshold Models for Complex Contagions Conclusion Part IV Research Ethics and Machine Learning 337 18 Research Ethics, Politics, and Practices 339 18.1 18.2 18.3 18.4 18.5 18.6 339 339 339 340 342 346 Learning Objectives Learning Materials Introduction Research Ethics and Social Network Analysis Informed Consent, Privacy, and Transparency Bias and Algorithmic Decision-Making
CONTENTS ļ IX 18.7 Ditching the Value-Free Ideal for Ethics, Politics, and Science 18.8 Conclusion 348 352 19 Machine Learning: Symbolic and Connectionist 353 Learning Objectives Learning Materials Introduction Types of Machine Learning Symbolic and Connectionist Machine Learning Conclusion 353 353 353 354 359 363 19.1 19.2 19.3 19.4 19.5 19.6 20 ISupervised Learning With Regression and Cross-validation 364 Learning Objectives Learning Materials Introduction Supervised Learning With Linear Regression Classification With Logistic Regression Conclusion 364 364 364 367 375 377 21 !Supervised Learning With Tree-Based Models 378 20.1 20.2 20.3 20.4 20.5 20.6 21.1 21.2 21.3 21.4 21.5 21.6 21.7 Learning Objectives Learning Materials Introduction Rules-Based Learning With Trees Ensemble Learning Evaluation Beyond Accuracy Conclusion 22 Neural Networks and Deep Learning 22.1 22.2 22.3 22.4 22.5 22.6 22.7 22.8 Learning Objectives Learning Materials Introduction The Perceptron Multilayer Perceptrons Training ANNs With Backpropagation and Gradient Descent More Complex ANN Architectures Conclusion 378 378 378 379 384 390 393 394 394 394 394 395 397 400 405 410 23 1Developing Neural Network Models With Keras and TensorFlow 411 23.1 23.2 23.3 23.4 23.5 23.6 411 411 411 415 419 429 Learning Objectives Learning Materials Introduction Getting Started With Keras End-to-End Neural Network Modelling Conclusion
X ļ CONTENTS Part V Bayesian Data Analysis and Generative Modelling with Probabilistic Programming 431 24 Statistical Machine Learning and Generative Models 433 Learning Objectives Learning Materials Introduction Statistics, Machine Learning, and Statistical Machine Learning: Where Are the Boundaries and What Do They Bind? 24.5 Generative Versus Discriminative Models 24.6 Conclusion 433 433 433 25 Probability: A Primer 444 25.1 Learning Objectives 25.2 Learning Materials 25.3 Introduction 25.4 Foundational Concepts in Probability Theory 25.5 Probability Distributions and Likelihood Functions 25.6 Continuous Distributions, Probability DensityFunctions 25.7 Joint and Conditional Probabilities 25.8 Bayesian Inference 25.9 Posterior Probability 25.10 Conclusion 444 444 444 445 447 453 456 458 463 463 26 Approximate Posterior Inference With Stochastic Sampling and MCMC 464 24.1 24.2 24.3 24.4 26.1 26.2 26.3 26.4 26.5 26.6 27 Bayesian Regression Models With Probabilistic Programming Learning Objectives Learning Materials Introduction Developing Our Bayesian Model Conclusion 28 Bayesian Hierarchical Regression Modelling 28.1 28.2 28.3 28.4 28.5 464 464 464 465 471 477 Learning Objectives Learning Materials Introduction Bayesian Regression Stochastic Sampling Methods Conclusion Part VI Probabilistic Programming and Bayesian Latent Variable Models for Structured, Relational, and Text Data 27.1 27.2 27.3 27.4 27.5 434 441 443 Learning Objectives Learning Materials Introduction So, What's a Hierarchical Model? Goldilocks and the Three Pools 479 481 481 481 481 485 502 503 503 503 503
504 505
CONTENTS I XI 28.6 The Best Model Our Data Can Buy 28.7 The Fault in Our (Lack of) Stars 28.8 Conclusion 523 531 532 29 Variational Bayes and the Craft of Generative Topic Modelling 533 29.1 29.2 29.3 29.4 29.5 29.6 533 533 533 534 545 557 Learning Objectives Learning Materials Introduction Generative Topic Models Topic Modelling With Gensim Conclusion 30 Generative Network Analysis With Bayesian Stochastic Block Models 30.1 30.2 30.3 30.4 30.5 560 560 560 565 587 Learning Objectives Learning Materials Introduction Block Modelling With Graph-Tool Conclusion Part VII Embeddings, Transformer Models, and Named Entity Recognition 31 Can We Model Meaning? Contextual Representation and Neural Word Embeddings 31.1 31.2 31.3 31.4 31.5 31.6 31.7 31.8 31.9 560 Learning Objectives Learning Materials Introduction What Words Mean What Are Neural Word Embeddings? Cultural Cartography: Getting a Feel for Vector Space Learning Embeddings With Gensim Comparing Embeddings Conclusion 589 591 591 591 591 592 595 599 603 605 613 32 Named Entity Recognition, Transfer Learning, and Transformer Models 614 32.1 32.2 32.3 32.4 32.5 32.6 Learning Objectives Learning Materials Introduction Named Entity Recognition Transformer Models Conclusion References Index 614 614 614 615 625 643 644 656 |
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| id | DE-604.BV048205595 |
| illustrated | Illustrated |
| index_date | 2024-07-03T19:47:43Z |
| indexdate | 2024-08-29T00:02:51Z |
| institution | BVB |
| isbn | 9781526468185 1526468182 9781526468192 1526468190 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033586501 |
| oclc_num | 1292634393 |
| open_access_boolean | |
| owner | DE-739 DE-1102 DE-91 DE-BY-TUM |
| owner_facet | DE-739 DE-1102 DE-91 DE-BY-TUM |
| physical | XV, 667 Seiten Illustrationen, Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | SAGE |
| record_format | marc |
| spelling | McLevey, John Verfasser (DE-588)1221963872 aut Doing computational social science a practical introduction John McLevey Los Angeles SAGE [2022] XV, 667 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier "Computational approaches offer exciting opportunities for us to do social science differently. This beginner's guide discusses a range of computational methods and how to use them to study the problems and questions you want to research. It assumes no knowledge of programming, offering step-by-step guidance for coding in Python and drawing on examples of real data analysis to demonstrate how you can apply each approach, including machine learning and social network analysis, in any discipline. The book also: Considers important principles of social scientific computing, including transparency, accountability and reproducibility. Understands the realities of completing research projects and offers advice for dealing with issues such as messy or incomplete data and systematic biases. Teaches you good habits and working practices that enable you to do programming well. This book is for anyone who wants to use computational methods to conduct a social science research project. Supported by a wealth of online resources, including video tutorials and datasets for practice so you can learn at your own pace, this book equips you with the skills to conduct computational social science research for the first time, with confidence" Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Computational social science (DE-588)1249405939 gnd rswk-swf Computational social science (DE-588)1249405939 s Datenanalyse (DE-588)4123037-1 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033586501&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | McLevey, John Doing computational social science a practical introduction Python Programmiersprache (DE-588)4434275-5 gnd Datenanalyse (DE-588)4123037-1 gnd Computational social science (DE-588)1249405939 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4123037-1 (DE-588)1249405939 |
| title | Doing computational social science a practical introduction |
| title_auth | Doing computational social science a practical introduction |
| title_exact_search | Doing computational social science a practical introduction |
| title_exact_search_txtP | Doing computational social science a practical introduction |
| title_full | Doing computational social science a practical introduction John McLevey |
| title_fullStr | Doing computational social science a practical introduction John McLevey |
| title_full_unstemmed | Doing computational social science a practical introduction John McLevey |
| title_short | Doing computational social science |
| title_sort | doing computational social science a practical introduction |
| title_sub | a practical introduction |
| topic | Python Programmiersprache (DE-588)4434275-5 gnd Datenanalyse (DE-588)4123037-1 gnd Computational social science (DE-588)1249405939 gnd |
| topic_facet | Python Programmiersprache Datenanalyse Computational social science |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033586501&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT mcleveyjohn doingcomputationalsocialscienceapracticalintroduction |