Focus and Scope

 

Mathematical, Statistical and Data Sciences (MSDS) is an international, peer-reviewed journal dedicated to original research in mathematics, statistics, and data science. The journal publishes rigorous theoretical results, methodological advances, mathematical and statistical models, and substantive applications. It welcomes contributions within each discipline and research that develops connections among them.

MSDS seeks to advance mathematical knowledge, strengthen statistical methodology, and develop sound approaches to understanding complex systems and data. Its scope encompasses pure and applied mathematics, probability, statistical inference, mathematical modelling, numerical methods, and the foundations and applications of data science.

Reflecting its commitment to Bridging Theory and Practice, the journal values both foundational research and work motivated by practical problems. Contributions are not required to combine all three disciplines or include an empirical application; theoretical, methodological, and applied papers are considered on their respective scientific merits.

Areas of Interest

  1. Pure and Applied Mathematics — Mathematical analysis; algebra and algebraic structures; geometry and topology; number theory; discrete mathematics and graph theory; differential, integral, and functional equations; operator theory; approximation theory; and related fields. Contributions may develop abstract mathematical results or rigorous approaches to problems arising in other disciplines.
  2. Mathematical Modelling and Numerical Methods — Deterministic and stochastic models; dynamical systems; mathematical biology and mathematical physics; numerical analysis; inverse problems; optimisation and optimal control; operations research; and numerical simulation. Topics include model formulation and analysis, numerical accuracy, stability, convergence, and validation.
  3. Probability and Statistical Sciences — Probability theory and stochastic processes; distribution theory; classical and Bayesian inference; statistical modelling; experimental design; regression; multivariate analysis; time series; spatial statistics; nonparametric and semiparametric methods; and uncertainty quantification. Relevant application areas include reliability, survival analysis, risk analysis, insurance, and other fields requiring principled statistical methods.
  4. Data Science and Statistical Learning — Mathematical and statistical foundations of machine learning; statistical learning theory; predictive modelling; classification and clustering; high-dimensional data analysis; dimensionality reduction; network data; data integration; and interpretable, robust, and uncertainty-aware learning. Research on artificial intelligence is welcome when it provides a substantive mathematical, statistical, or methodological contribution.
  5. Interdisciplinary Applications — Applications of mathematics, statistics, and data science across engineering, the natural and life sciences, health, economics, finance, business, environmental science, and the social sciences. Applied contributions should clearly formulate the research problem, justify the methods used, and demonstrate the significance and validity of the findings.

Article Types

  • Original research articles — Full-length papers presenting significant theoretical, methodological, or applied contributions.
  • Review articles — Authoritative surveys and critical syntheses that assess existing research, identify unresolved questions, and outline useful directions for further investigation.
  • Short communications — Concise, self-contained papers presenting complete and significant results that warrant focused publication.
  • Methodological notes — Focused contributions introducing or substantially improving techniques, algorithms, numerical procedures, or analytical tools.

All submissions should demonstrate originality, scientific rigour, and a clear contribution to the relevant field. Theoretical claims should be supported by appropriate proofs, while methodological and applied claims should be supported by suitable analytical, numerical, or empirical evidence.