Mathematical Methods in Data Science (MMDS) is an interdisciplinary, peer-reviewed academic journal dedicated to advancing the foundational role of mathematics in driving innovation and rigor in data science. Its core aim is to bridge the gap between mathematical theory and real-world data science applications, publishing high-quality research that develops, refines, or applies mathematical frameworks to address complex data-centric challenges. The journal seeks to foster scholarly dialogue across mathematics, statistics, computer science, and domain-specific fields (e.g., engineering, biology, finance), with the goal of enhancing the theoretical depth, methodological robustness, and practical impact of data science. Additionally, it aims to serve as a resource for researchers, practitioners, and educators—supporting the adoption of rigorous mathematical approaches to solve emerging data problems and shaping the future direction of data science as a discipline.