The Transactions on Data Science with Applications (TDSA) publishes original research on the theoretical foundations, computational methods, technologies, and practical applications of data science. The journal provides an interdisciplinary forum for researchers and practitioners in computer science, statistics, mathematics, engineering, management, and other fields in which data are used to develop models, generate knowledge, and support decisions. TDSA considers studies covering the complete data science lifecycle, including data acquisition, preparation, integration, management, analysis, modelling, evaluation, interpretation, visualization, and communication. Submissions may present new theories, methods, algorithms, computational frameworks, or data-processing systems. The journal also considers application-oriented research that adapts or integrates established methods to address substantive problems, provided that the study demonstrates methodological rigor, appropriate evaluation, and a clear contribution to the application domain.

The journal welcomes research on topics including, but not limited to:

• Statistical methods and statistical learning

• Machine learning and artificial intelligence

• Deep learning and computational intelligence

• Data mining, pattern recognition, and knowledge discovery

• Big-data analytics and scalable data processing

• Predictive and prescriptive analytics

• Data engineering, database systems, and data architectures

• Data acquisition, preprocessing, cleaning, integration, and transformation

• Optimization and data-driven decision-making

• Natural language processing and text analytics

• Computer vision, image processing, and multimedia analytics

• Time-series analysis, forecasting, and anomaly detection

• Graph analytics, network analysis, and recommender systems

• Data visualization and human–data interaction

• Explainable, interpretable, and responsible artificial intelligence

• Data quality, governance, privacy, security, trust, and ethics

• Cloud, distributed, parallel, and high-performance computing

• Internet of Things, sensor data, and real-time analytics

• Reproducible data science, computational tools, and analytical workflows

• Mathematical and computational modelling of data-intensive systems

• Reproducible data science, computational tools, and analytical workflows

• Mathematical and computational modelling of data-intensive systems

TDSA also publishes research demonstrating the use of data science in engineering, manufacturing, supply chain and logistics, healthcare, finance, business and management, transportation, energy, environmental systems, agriculture, public services, education, and the social sciences. Application papers should explain the relevant domain problem, justify the selected analytical methods, report suitable validation procedures, and discuss the findings within the context in which the data were generated. The journal considers original research articles, review articles, case studies, and technical notes. Review articles should provide a critical and structured synthesis of a defined research area. Case studies should offer findings or methodological insights that extend beyond a single organization or setting. Technical notes should present a specific method, algorithm, dataset, software tool, or computational procedure with sufficient detail to permit assessment and, where possible, reproduction.