Computational social psychology is a new frontier in the field of social psychology. Computational social psychology has two pillars: (a) computational modeling for theory building and hypothesis generation and (b) computational analysis for pattern discovery and hypothesis testing. This course combines a seminar-style survey of computational approaches to social psychological research with hands-on workshops on methods, tools, and software frameworks. Throughout the course, emphasis is placed on complex systems theory and its application to the study of psychological mechanisms underlying social phenomena. 

Agent-based modeling is a computer modeling technique that simulates the interactions of agents to gain insights into system behaviors. This stochastic model is built from the bottom up and validates generative causality. It has established itself as a powerful research method in the formal, physical, and especially the social sciences. This is a hands-on introduction to some landmark models, the core concepts, and the techniques of agent-based modeling.

Data science is an emerging interdisciplinary field that uses scientific methods, processes, algorithms and systems to dicover knowledge and insights from many structural and unstructured data. To address the current needs for scientific approach to data, this course offers a data science toolbox including computational and statistical thinking, mathematical foundations, model building and software foundation, data curation, and knowledge transference, as well as a hands-on introduction to Python and Google Colab.