The course 'Advanced Quantitative Methods: Agent-based Computational Modeling' aims to familiarize participants with the methods of computational agent-based modeling (ABM). Computational modeling techniques are commonly not part of the standard repertoire of quantitative analyses in the social sciences. They have increasingly gained prominence though as powerful techniques that can effectively compliment more standard approaches such as regression analyses, in particular, in settings characterized by complex systemic interactions. The course will first introduce the theoretical foundations of the technique in the field of complexity theory. Students will then learn the methodology of agent-based modeling. The course first introduces classical examples such as Schelling's (1971) model of segregation and then familiarizes students with more complex recent models. In the last segment of the course, students will learn about the state-of-the-art approach of evidence-driven modeling, i.e., embedding and validating agent-based models using empirical data. The course places a particular emphasis on highlighting strengths and weaknesses of computational modeling approaches for quantitative analyses and will carefully place the methods in the context of other quantitative techniques more commonly used in the social sciences. Students will also gain first-hand experience in implementing and applying the methods learned through a set of practical exercises in Python or R. The course therefore assumes that students have some programming background. Students obtain credits through active participation in the course and submission of a computational modeling project (coding + project report) that is due 4-6 weeks after the course.