About the Journal

The SPC Journal of Data Science & Analytics (SPCJDSA) is an international, peer-reviewed, open-access journal covering the methods and applications of data science and analytics. It publishes original research, reviews, and applied case studies on extracting knowledge and value from data, spanning statistical learning, big-data engineering, and domain applications. The journal bridges computer science, statistics, and applied domains, offering rapid and rigorous open dissemination under a Creative Commons Attribution licence to a broad community of researchers, analysts, and practitioners.

Focus and Scope

SPCJDSA aims to advance the science and practice of working with data. Topics within scope include, but are not limited to: statistical and machine learning for data analysis; big data architectures, pipelines, and engineering; data mining and knowledge discovery; predictive, prescriptive, and visual analytics; time-series, spatial, and streaming data; natural language and text analytics; business, health, financial, and scientific analytics; data quality, governance, privacy, and ethics; reproducible and open data-science workflows; and benchmarking of analytical methods on real datasets. The journal prioritises methodologically sound studies with clear, validated results and encourages the sharing of code and data.