Description
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 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.
- 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.
Table of Contents
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
