Computational Sociology with Python: Data-Driven Insights into Social Structures, Networks, and Behaviors - Tapa blanda

Sinopsis

Reactive Publishing

Computational Sociology with Python offers a rigorous, hands-on introduction to applying computational methods to the study of society. Bridging social theory and modern data science, this book equips researchers, students, and analysts with the Python tools needed to move beyond traditional surveys and small-scale studies toward large-scale, data-driven analysis of social structures, networks, and human behavior.

• Core computational approaches to modeling social phenomena, including agent-based models, network analysis, and statistical simulations.
• Practical Python implementations using libraries such as NetworkX, Pandas, NumPy, SciPy, Matplotlib, and scikit-learn to analyze real-world datasets.
• Key topics in computational sociology: social network dynamics, community detection, opinion formation and diffusion, inequality and segregation, collective behavior, and emergent social patterns.
• End-to-end workflows — from data collection and cleaning through exploratory analysis, visualization, and modeling to interpretation of results.
• Theoretical foundations paired with code examples that illustrate how classic sociological concepts (e.g., social capital, structural holes, homophily) can be operationalized and tested at scale.

Written in a clear, accessible style suitable for both social scientists new to programming and data scientists interested in social applications, the book emphasizes reproducible research practices and ethical considerations in computational social science.

Whether you are an academic researcher, graduate student, data analyst, or policy professional, Computational Sociology with Python provides the conceptual framework and technical skills to extract meaningful insights from the complex web of human interactions in the digital age.

Perfect for:

• Sociology and social science students seeking computational methods training
• Data scientists and Python developers exploring social applications
• Researchers working with social media, survey, or administrative data
• Anyone interested in understanding society through code and data

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