About the Journal

Overview

The SPC Journal of Data Science & Analytics (SPCJDSA) is a peer-reviewed, open-access journal covering methods and applications in data science and analytics. It connects computer science, statistics, data engineering, and applied research for researchers, analysts, and practitioners.

Aims and Scope

The journal welcomes work on statistical and machine learning; data mining and knowledge discovery; big-data architectures and pipelines; predictive, prescriptive, and visual analytics; time-series, spatial, and streaming data; natural language and text analytics; data quality, governance, privacy, and ethics; reproducible data-science workflows; benchmarking; and applications in business, health, finance, engineering, and the natural and social sciences.

Article Types

SPCJDSA considers original research articles, review articles, short communications, technical notes, and relevant applied case studies. Studies should describe their methods clearly and support their conclusions with appropriate validation.

Peer Review

Submissions receive editorial screening for relevance, originality, clarity, and basic ethical requirements. Suitable manuscripts are sent to independent subject experts. The editor makes the final decision after considering the manuscript, reviewer reports, and any author revisions.

Open Access and Licensing

Published articles are freely available online. Articles published under the current SPC policy use the Creative Commons Attribution 4.0 International License (CC BY 4.0). Previously published articles retain the license stated at the time of publication unless properly relicensed.