The International Journal of Data Science and Statistics reports methodological innovation and substantive application at the intersection of statistics, machine learning, and computation. We publish work that develops new inferential or predictive methods and work that applies principled analysis to real problems in science, industry, and society.

Our scope includes statistical theory and inference, Bayesian and probabilistic modelling, high-dimensional and big-data methods, machine learning and data mining, experimental design, and the statistical computing that makes these techniques usable at scale. We especially welcome contributions that pair sound theory with reproducible analysis on substantively important data.

Submissions are evaluated for technical correctness, novelty relative to existing methods, and the clarity with which assumptions, limitations, and uncertainty are communicated. We encourage authors to share code and data so results can be verified and extended. As an open-access journal, we aim to serve statisticians, data scientists, and quantitative researchers who build and apply the tools of modern data analysis.

We publish methodological research, novel applications, simulation and benchmark studies, and review articles surveying the state of the art. Authors are strongly encouraged to share data and reproducible code so findings can be verified and extended. Made freely available on publication, the journal serves statisticians, data scientists, and quantitative researchers across academia and industry who develop new analytical methods or apply them to consequential problems.

Topics Covered

• Statistical Theory & Inference
• Machine Learning Methods
• Big Data & Computational Statistics
• Bayesian & Probabilistic Modelling
• Data Mining & Analytics
• Experimental Design
• Statistical Computing
• Applications Across Domains