Doing Computational Social Science

Introduction: Learning to do computational social science
Part I: Foundations
Chapter 1: Setting up your open source scientific computing environment
Chapter 2: Python programming: The basics
Chapter 3: Python programming: Data structures, functions and files
Chapter 4: Collecting data from Application Programming Interfaces (APIs)
Chapter 5: Collecting data from the web: Scraping
Chapter 6: Processing structured data
Chapter 7: Visualisation and exploratory data analysis
Chapter 8: Latent factors and components
Part II: Fundamentals of text analysis
Chapter 9: Processing natural language data
Chapter 10: Iterative text analysis
Chapter 11: Exploratory text analysis
Chapter 12: Text similarity and latent semantic space
Part III: Fundamentals of network analysis
Chapter 13: Social networks and relational thinking
Chapter 14: Connection and clustering in social networks
Chapter 15: Influence, inequality and power in social networks
Chapter 16: Going viral: Modelling the epidemic spread of simple contagions
Chapter 17: Not so fast: Modelling the diffusion of complex contagions
Part IV: Research ethics and machine learning
Chapter 18: Research ethics, politics and practices
Chapter 19: Machine learning: Symbolic and connectionist
Chapter 20: Supervised learning with regression and cross-validation
Chapter 21: Supervised learning with tree-based models
Chapter 22: Neural networks and deep learning
Chapter 23: Developing neural network models with Keras and Tensorflow
Part V: Bayesian machine learning and probabilistic programming
Chapter 24: Statistical machine learning and generative models
Chapter 25: Probability: A primer
Chapter 26: Approximate posterior inference with stochastic sampling and MCMC
Part VI: Bayesian data analysis and latent variable modelling with relational and text data
Chapter 27: Bayesian regression models with probabilistic programming
Chapter 28: Bayesian hierarchical regression modelling
Chapter 29: Variational Bayes and the craft of generative topic modelling
Chapter 30: Generative network analysis with Bayesian stochastic blockmodels
Part VII: Embeddings, transformer models and named entity recognition
Chapter 31: Can we model meaning?: Contextual representation and neural word embeddings
Chapter 32: Named entity recognition, transfer learning and transformer models