Focus: Statistical Modeling and Data Science (SMDS) is a peer-reviewed academic journal that publishes high-quality research on the development, implementation, and application of statistical methods and data science techniques. The journal serves as an interdisciplinary forum bridging statistical theory, computational methods, and real-world applications to address challenges in modern data analysis. SMDS welcomes both methodological innovations and applied studies that contribute to advancing knowledge, practice, and technology in statistics and data science. Scope: SMDS invites original contributions covering, but not limited to, the following topics: Statistical Modeling and Methodology: Classical and modern regression models, generalized linear and mixed models, Bayesian methods, time series and forecasting, spatial and spatiotemporal analysis, survival and reliability studies, nonparametric and semiparametric methods, causal inference, graphical models, and statistical learning theory. Computational and Data Science Approaches: Machine learning (supervised, unsupervised, and reinforcement learning), deep learning, high-dimensional data analysis, big data and scalable computing, regularization and optimization methods, data mining and knowledge discovery, text and natural language processing, and network science. Applications Across Domains: Health and medicine, biology and bioinformatics, economics and finance, social and behavioral sciences, engineering and quality control, environmental and ecological studies, information technology, and other fields where statistical modeling and data science play a central role. Audience: Statistical Modeling and Data Science (SMDS) is intended for a wide readership, including statisticians, data scientists, computer scientists, quantitative researchers, and professionals from academia, industry, and government. The journal particularly welcomes contributions from those interested in advancing the theory, methodology, and practice of statistical modeling and data science in diverse application areas. Submission Types: SMDS accepts a variety of manuscript types to encourage broad scholarly exchange: Original Research Articles: Full-length papers presenting novel theoretical developments, innovative methodologies, or significant applications in statistics and data science. Review Articles: Comprehensive surveys that synthesize and critically evaluate existing research, highlight current challenges, and suggest future research directions. Case Studies and Applied Papers: Detailed analyses demonstrating the application of statistical or data science methods to real-world problems with substantial practical relevance. Technical Notes and Short Communications: Concise articles introducing new algorithms, computational techniques, or software tools, as well as preliminary findings of high potential impact. Frequency: (Biannual)