Curricular design in higher education significantly impacts student success and institutional performance. However, academic programs' complexity--shaped by pass rates, prerequisite dependencies, and course repeat policies--creates challenges for administrators. This paper presents a method for modeling curricular pathways including development of a "Curricular Analytics App," a scalable platform that models curricula as directed acyclic graphs (DAGs) to detect structural inefficiencies and bottlenecks. This method integrates Critical Path Analysis to highlight bottleneck courses delaying student progression, enhanced Monte Carlo simulations to capture real-world variability in course pass rates and retakes, and introduces "Passability Complexity," a novel metric incorporating probabilistic pass rates into structural complexity. These features provide deeper insights into curriculum difficulty and graduation timelines. As a proof of concept that allows for applied analysis, the "Curricular Analytics App" has an interactive interface which users can modify courses and prerequisites in real time, enabling data-driven curriculum optimization. The app's efficient graph-based algorithms ensure scalability for large academic programs. By linking curriculum structure to student outcomes, it supports institutions in improving graduation rates and streamlining degree pathways through evidence-based decision-making. [For the complete proceedings, see ED675583.]