Active and Experiential Learning at all levels
Astrophysics is not a spectator sport. Central to my teaching style is active engagement of a diverse audience at all levels. This involves group work in class, room for discussion and tangents, and at times a circular style of teaching that revisits topics over and over and over again. These methods have an end goal of both learning the details of the the astrophysics at hand in the course, but also to develop a style of thinking about problems.
In this cross-listed grad/undergrad course, we explore a diverse range of numerical techniques useful for scientific computing. These range from integration/differentiation to Monte Carlo methods to algebra and differential equation solvers. The course is taught in Python, though no prior background is necessary. Course Link