The discussion centers around comparing the difficulty of applied linear algebra and probability modeling courses. Participants note that linear algebra often serves as an introduction to proof-based and abstract mathematics, which can be challenging for those unprepared for such concepts. Conversely, probability modeling is generally perceived as moderately difficult, with a focus on real-world applications and computations, though it can become complex when programming elements are introduced. There is no clear consensus on which course is harder, as difficulty varies based on individual preferences, prior experience, and teaching styles. Some find linear algebra easier once foundational concepts are grasped, while others struggle with its abstract nature. The prerequisites for these courses also differ, with linear algebra requiring only Calculus 2, while probability modeling requires Calculus 3, indicating a potentially higher level of mathematical complexity. Participants highlight that both subjects are valuable for fields like physics, with linear algebra techniques being particularly essential.