College: Institute of Graduate Programs

This interdisciplinary major combines social sciences and computational methods to analyze and understand complex social phenomena. Students explore key areas such as social network analysis, data science, agent-based modeling, computational economics, and digital humanities. The program focuses on quantitative skills, programming, data analysis, and the application of computational tools to social science research. Graduates are prepared for careers in data science, social research, policy analysis, and related fields.

Learning Objectives

• Understand the fundamentals of computational social sciences.
• Develop skills in social network analysis, data science, and agent-based modeling.
• Learn techniques for applying computational methods to social science research.
• Explore principles of computational economics and digital humanities.
• Understand ethical considerations in data analysis and computational research.
• Analyze challenges and opportunities in computational social science research.
• Develop teamwork and problem-solving skills for research projects and initiatives.

Main Curriculum

1. Introduction to Computational Social Sciences
   • Overview of computational social sciences, basic concepts, and historical context.
   • Fundamentals of applying computational methods to social science research.
2. Social Network Analysis
   • Principles of social network analysis, including network structures, metrics, and visualization.
   • Techniques for analyzing social networks and understanding social dynamics.
3. Data Science for Social Sciences
   • Basics of data science, including data collection, cleaning, and analysis.
   • Techniques for applying data science methods to social science research.
4. Agent-Based Modeling
   • Principles of agent-based modeling, including simulating social systems and emergent behaviors.
   • Techniques for developing and analyzing agent-based models.
5. Computational Economics
   • Fundamentals of computational economics, including economic modeling, simulation, and analysis.
   • Techniques for applying computational methods to economic research.
6. Digital Humanities
   • Exploration of digital humanities, including digital archives, text analysis, and cultural analytics.
   • Techniques for applying computational methods to humanities research.
7. Ethical Considerations in Computational Research
   • Principles of ethical considerations in data analysis and computational research.
   • Techniques for conducting ethical and responsible research in computational social sciences.
8. Research Methods in Computational Social Sciences
   • Principles of research methods, including experimental design, data collection, and analysis.
   • Techniques for conducting and evaluating research in computational social sciences.
9. Practical Training in Computational Social Sciences
   • Real-world research experiences, including observations, internships, and practical projects.
   • Techniques for applying acquired skills in practical computational social science environments.
10. Capstone Project in Computational Social Sciences
    • An extensive project to apply skills in social network analysis, data science, or agent-based modeling.
    • Techniques for delivering a sophisticated and in-depth research project in computational social sciences.

Evaluation Methods

Reports on social network analysis, data science projects, agent-based modeling simulations, computational economics analyses, digital humanities studies, ethical considerations papers, research methodology papers, practical training reports, capstone projects, group projects, and internships.

Recommended Textbooks

• "Social Network Analysis" by Stanley Wasserman and Katherine Faust.
• "Data Science for Social Good" by Rayid Ghani and Rachel Schutt.
• "Agent-Based Modeling" by Nigel Gilbert.
• "Computational Economics" by Kenneth L. Judd.
• "Digital Humanities" by various authors.

Prerequisites

Basic knowledge of social sciences, mathematics, and computer science. Suitable for students interested in data science, social research, and computational methods.

Duration of the Major

Typically 4 years for a bachelor's degree, including coursework, projects, practical training, and internships.

Certification

Graduates can earn a degree in computational social sciences and pursue professional certifications in data science, social research, or related fields.

Target Audience

Aspiring data scientists, social researchers, policy analysts, and professionals seeking to specialize in computational social sciences. This major equips students with the quantitative, programming, and practical skills needed to excel in computational social sciences, supporting careers in data science, social research, policy analysis, and related fields.