Computational Social Science Specialization Course Syllabus

Overview: This five-course specialization provides a comprehensive introduction to computational social science, blending social theory with practical computational tools. You'll progress from foundational methods to advanced applications across social network analysis, machine learning, and simulations. With approximately 55 total hours of content, the program features hands-on labs using Python, IBM Watson, and network visualization tools, culminating in a capstone project that integrates scraping, NLP, network analysis, and simulation techniques. Designed for learners with basic programming experience, this project-based curriculum prepares you to generate data-driven insights into human behavior and social systems.

Module 1: Computational Social Science Methods

Estimated time: 11 hours

• Examine the history and evolution of social science in the digital age
• Configure databases for social data analysis
• Train simple artificial intelligence models
• Detect patterns of social emergence

Module 2: Big Data, Artificial Intelligence, and Ethics

Estimated time: 9 hours

• Define big data and its opportunities and limitations
• Use IBM Watson for personality analysis via natural language processing
• Study real-world applications of AI in social contexts
• Evaluate ethical considerations in big data and AI deployment

Module 3: Social Network Analysis

Estimated time: 10 hours

• Learn core definitions and languages of network science
• Wrangle and clean social network data
• Visualize social networks using computational tools
• Explore generative mechanisms behind network structures
• Apply SNA in case studies of real-world phenomena

Module 4: Computer Simulations

Estimated time: 12 hours

• Explore agent-based models including Schelling’s segregation model
• Study the Sugarscape simulation framework
• Build artificial societies to test social theories
• Integrate hypothetical models with empirical data

Module 5: Computational Social Science Capstone Project

Estimated time: 13 hours

• Scrape social media data using Python tools
• Visualize social networks from collected data
• Apply machine learning-powered NLP to analyze content
• Simulate generative mechanisms in an integrative lab environment

Prerequisites

• Basic comfort with programming concepts
• Familiarity with Python recommended
• No prior experience in social science required

What You'll Be Able to Do After

• Web scrape online data and analyze social patterns
• Create and visualize social network structures
• Apply machine learning and NLP techniques to textual data
• Design and run agent-based simulations of social phenomena
• Integrate computational methods into ethical, real-world research projects