Foundations of Data Science focuses on advancing the theoretical and practical aspects of data science. It encompasses interdisciplinary approaches, bridging mathematics, statistics, and computer science to tackle complex data-driven problems.
Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.
Publication & Citation Trend
Articles published
Times cited
2019
2020
2021
2022
2023
2024
2025
2026
Source: OpenAlex · Note: citations accumulate over time so older years appear higher
SJR Quartile by Discipline
Scimago ranks this journal separately in each subject category — its quartile can differ by discipline.
Applied MathematicsQ2
Computational Theory and MathematicsQ2
AnalysisQ3
Statistics and ProbabilityQ3
Subject Classification
Web of Science Categories
Mathematics, AppliedStatistics & Probability
Scopus Categories
Applied MathematicsAnalysisComputational Theory and MathematicsStatistics and Probability
Research Topics (OpenAlex)
Topological and Geometric Data AnalysisGaussian Processes and Bayesian InferenceModel Reduction and Neural NetworksAdvanced Neuroimaging Techniques and ApplicationsMarkov Chains and Monte Carlo MethodsSparse and Compressive Sensing TechniquesTarget Tracking and Data Fusion in Sensor NetworksHomotopy and Cohomology in Algebraic TopologyMachine Learning and AlgorithmsNeural Networks and Applications