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