Doing Computational Social Science:A Practical Introduction

Doing Computational Social Science:A Practical Introduction

计算社会科学：实用简介
社会学史

原价：

￥ 1715

售价：

优惠

平台大促低至8折优惠

发货周期：预计5-7周发货

作者

John McLevey

出版社

Sage

出版时间

2021年12月13日

装帧

精装

ＩＳＢＮ

9781526468192

复制

页码

688

开本

242*170mm

语种

英文

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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.

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

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