Computational approaches offer exciting opportunities for us to do social science differently. This beginner’s guide discusses a range of computational methods and how toÌýuseÌýthem to study the problems and questions you want to research.
ItÌýassumes 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 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; and empowers you to learn at your own pace, with online resources including screencast tutorials and datasets that enable you to practice your skills and get up to speed.
For anyone who wants to use computational methods to conduct a social science research project, this book equips you with the skills, good habits and best working practices to do rigorous, high quality work.
Contents
Part I: Foundations
ÌýÌý
Chapter 1: Setting up your open source scientific computing environment ÌýÌý
Chapter 2: Python programming: The basics ÌýÌý
Chapter 3: Python programming: Data structures, functions and files ÌýÌý
Chapter 4: Collecting data from Application Programming Interfaces (APIs) ÌýÌý
Chapter 5: Collecting data from the web: Scraping ÌýÌý
Chapter 6: Processing structured data ÌýÌý
Chapter 7: Visualisation and exploratory data analysis ÌýÌý
Chapter 8: Latent factors and components Ìý
Part II: Fundamentals of text analysis
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Chapter 9: Processing natural language data ÌýÌý
Chapter 10: Iterative text analysis ÌýÌý
Chapter 11: Exploratory text analysis ÌýÌý
Chapter 12: Text similarity and latent semantic space Ìý
Part III: Fundamentals of network analysis
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Chapter 13: Social networks and relational thinking ÌýÌý
Chapter 14: Connection and clustering in social networks ÌýÌý
Chapter 15: Influence, inequality and power in social networks ÌýÌý
Chapter 16: Going viral: Modelling the epidemic spread of simple contagions ÌýÌý
Chapter 17: Not so fast: Modelling the diffusion of complex contagions Ìý
Part IV: Research ethics and machine learning
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Chapter 18: Research ethics, politics and practices ÌýÌý
Chapter 19: Machine learning: Symbolic and connectionist ÌýÌý
Chapter 20: Supervised learning with regression and cross-validation ÌýÌý
Chapter 21: Supervised learning with tree-based models ÌýÌý
Chapter 22: Neural networks and deep learning ÌýÌý
Chapter 23: Developing neural network models with Keras and Tensorflow Ìý
Part V: Bayesian machine learning and probabilistic programming
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Chapter 24: Statistical machine learning and generative models ÌýÌý
Chapter 25: Probability: A primer ÌýÌý
Chapter 26: Approximate posterior inference with stochastic sampling and MCMC Ìý
Part VI: Bayesian data analysis and latent variable modelling with relational and text data
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Chapter 27: Bayesian regression models with probabilistic programming ÌýÌý
Chapter 28: Bayesian hierarchical regression modelling ÌýÌý
Chapter 29: Variational Bayes and the craft of generative topic modelling ÌýÌý
Chapter 30: Generative network analysis with Bayesian stochastic blockmodels Ìý
Part VII: Embeddings, transformer models and named entity recognition
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Chapter 31: Can we model meaning?: Contextual representation and neural word embeddings ÌýÌý
Chapter 32: Named entity recognition, transfer learning and transformer models
Supplementary Resources
You can find the supplementary materials for the book on the Ìýand on .