Data Science II: Complexity

4.0 credits

As the number of interacting degrees of freedom (or agents) in a given system increases, its behavior often changes qualitatively, and not only quantitatively. Complexity is the emerging field of research, which investigates the shared underlying concepts and principles of such systems. It finds its applications in Physics, Computer Science, Mathematics, Biology, Social Sciences, Economy, and more. In this introductory course we will focus on these common features and their utilization in understanding complex systems. They will include for example: Fractals, non-linearity and chaos, adaptation and evolution, critical and tipping points, patterns formation, networks modeling, feedback loops, emergence and unpredictability, etc. Students in the course will be given ample opportunities to study farther these systems and/or techniques that are of particular interest to them.