The SciMatic Journal of Data Science and Big Data Analytics (SJDSBD) publishes original research at the forefront of data science, big data technologies, and analytical methods. The journal promotes the development and application of data-driven approaches across diverse domains.

Topics covered include: machine learning and deep learning algorithms, statistical modeling and inference, data mining and knowledge discovery, big data infrastructure and processing frameworks, data visualization and exploratory analysis, natural language processing, predictive analytics and forecasting, recommender systems, time series analysis, feature engineering and dimensionality reduction, graph analytics and network science, and ethical AI and responsible data use.

The journal accepts original research articles, review papers, case studies, short communications, and benchmark studies. All submissions undergo thorough peer review by data science experts to ensure methodological soundness and contribution to the field.

SJDSBD serves as an essential outlet for data scientists, statisticians, and analytics professionals to disseminate their findings and advance the understanding of complex data-driven phenomena.