Explore the intersection of topological data analysis and data science in this 58-minute lecture from the Applied Algebraic Topology Network. Delve into the Steinhaus filtration, a novel approach based on generalized Steinhaus distance, and its applications in Mapper construction. Learn about stability results, equivalence to Čech filtration, and the relationship with Vietoris-Rips filtration. Discover how stable paths within this filtration can be applied to recommendation systems and explainable machine learning, with practical examples using movie datasets and image classification models. Gain insights into the potential of topological methods for analyzing complex data structures and improving data-driven decision-making processes.