04598cam a2200361 i 4500 991107219429406196 20260304125259.0 210414s2022 caua b 001 0 eng 2021937242 GBC1G6125 bnb 020349758 Uk 9781526468185 (paperback) 9781526468192 (hardcover) 9781529737592 (epub) 9781529738490 (epub) 9781529736700 (pdf) (OCoLC)1253472930 (OCoLC)1253473285 (OCoLC)on1253472930 DLC eng rda DLC UKMGB OCLCF CDX BDX QGK YDX OCLCO OCLCL Y@Y English pcc H61.3 .M4185 2022 McLevey, John, author. 26764 Doing computational social science : a practical introduction / John McLevey. Los Angeles : SAGE, [2022] xv, 667 pages : illustrations ; 25 cm Includes bibliographical references (pages 644-655) and index. Introduction: Learning to do computational social science — Part I: Foundations — Setting up your open source scientific computing environment — Python programming: The basics — Python programming: Data structures, functions and files — Collecting data from Application Programming Interfaces (APIs) — Collecting data from the web: Scraping — Processing structured data — Visualisation and exploratory data analysis — Latent factors and components — Part II: Fundamentals of text analysis — Processing natural language data — Iterative text analysis — Exploratory text analysis — Text similarity and latent semantic space — Part III: Fundamentals of network analysis — Social networks and relational thinking — Connection and clustering in social networks — Influence, inequality and power in social networks — Going viral: Modelling the epidemic spread of simple contagions — Not so fast: Modelling the diffusion of complex contagions — Part IV: Research ethics and machine learning — Research ethics, politics and practices — Machine learning: Symbolic and connectionist — Supervised learning with regression and cross-validation — Supervised learning with tree-based models — Neural networks and deep learning — Developing neural network models with Keras and Tensorflow — Part V: Bayesian machine learning and probabilistic programming — Statistical machine learning and generative models — Probability: A primer — Approximate posterior inference with stochastic sampling and MCMC — Part VI: Bayesian data analysis and latent variable modelling with relational and text data — Bayesian regression models with probabilistic programming — Bayesian hierarchical regression modelling — Variational Bayes and the craft of generative topic modelling — Generative network analysis with Bayesian stochastic blockmodels — Part VII: Embeddings, transformer models and named entity recognition — Can we model meaning?: Contextual representation and neural word embeddings — Named entity recognition, transfer learning and transformer models. "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"-- Provided by publisher. Social sciences - Data processing Data processing. Social sciences Statistical methods 5703 lcc 0 0 lcc 0 0 TBS TBS 2026-03-04 0 2026-03-04 1 2026-03-04 1 SOON AVAILABLE 5372 5372