Focus and Scope

Focus

Journal of Mathematical and Analytical Data Science (JMADS) focuses on the development and application of mathematical theories, analytical methods, and data science techniques to address complex problems across scientific, engineering, and interdisciplinary domains. The journal emphasizes methodological rigor, analytical depth, and data-driven innovation, bridging theoretical mathematics with practical and computational data analysis.

JMADS prioritizes research that advances mathematical modeling, statistical analysis, computational analytics, and data-driven methodologies, either through theoretical contributions, methodological innovations, or high-quality applied studies supported by robust analytical frameworks.

Scope

The journal welcomes original research articles, review papers, and methodological studies covering, but not limited to, the following areas:

Mathematical Foundations and Modeling

  • Pure and applied mathematics relevant to data analysis

  • Mathematical modeling of complex systems

  • Differential equations and dynamical systems

  • Optimization theory and operations research

  • Numerical methods and scientific computing

Statistical and Analytical Methods

  • Statistical inference and hypothesis testing

  • Multivariate analysis and dimensionality reduction

  • Bayesian methods and probabilistic modeling

  • Time series analysis and forecasting

  • Experimental design and analytical validation

Data Science and Analytics

  • Data mining and knowledge discovery

  • Machine learning and statistical learning theory

  • Big data analytics and scalable data processing

  • Predictive analytics and decision-support systems

  • Data visualization and analytical reporting

Computational and Algorithmic Approaches

  • Computational mathematics and simulation-based analytics

  • Algorithm development for data analysis

  • High-performance and parallel computing for data science

  • Numerical optimization and algorithm efficiency

Interdisciplinary and Applied Data Science

  • Mathematical and analytical data science in engineering and technology

  • Applications in natural sciences, health, economics, and social sciences

  • Educational data science and learning analytics

  • Mathematical approaches to artificial intelligence and intelligent systems