Arrangementer

Statistics courses typically come in two different flavors: 1) A first type of course seeks deep understanding based on mathematical reasoning and proofs. 2) A second type of course focuses on practical applications, primarily based on fixed recipes without delving into underlying details. 3) I will here propose a third way, which seeks understanding of fundamental concepts, but through programming and simulation instead of mathematical proofs. This third way is thus geared towards students that have a stronger background in programming than mathematics, and that seek to understand the fundamentals as a basis for developing statistical analyses and data science methodology. This third way would also exploit its basis in programming to promote self-driven exploration, drawing inspiration from how computing has been integrated into science education through the long-running CSE initiative. I will provide examples ranging from how to explore the central limit theorem to performing Bayesian marginalization.