The International Journal of Data Science and Analytics is dedicated to advancing data science as an important emerging scientific field. This field integrates multiple disciplines, including statistics, computational science, and intelligent science, driving transformative practices across science, engineering, public sectors, business, social sciences, and lifestyle. Data science encompasses a wide range of topics, including artificial intelligence, data analysis, machine learning, pattern recognition, natural language processing, and big data operations. The journal aims to address new scientific challenges related to data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, as well as to conduct comprehensive analyses of complex resources that are heterogeneous and interdependent, in order to make better decisions, foster collaboration, and ultimately achieve value creation. The International Journal of Data Science and Analytics brings together thought leaders, researchers, industry practitioners, and potential users in the field of data science and analytics to explore new trends and opportunities, share ideas and practices, and promote interdisciplinary and cross-sector collaboration. The journal is divided into three sections: the regular section, which communicates original and reproducible theoretical and experimental findings in data science and analytics; the applications section, which reports significant applications of data science in real life; and the trends section, which provides expert opinions and comprehensive surveys and reviews of topics related to data science and analytics. Relevant topics include trends, scientific foundations, technologies, and applications of data science and analytics, with a primary focus on the following areas: the statistical and mathematical foundations of data science and analytics; understanding and analyzing complex data, human behavior, domains, networks, organizations, societies, and system characteristics; discovering, integrating, and modeling complex data, behaviors, knowledge, and intelligence; data analysis, pattern recognition, knowledge discovery, machine learning, deep analysis, and deep learning, as well as various types of data (including transactional, textual, image, video, graphical, and network data), behaviors, and systems; proactive, real-time, personalized, actionable, and automated analysis, learning, computation, optimization, presentation, and recommendations; big data architecture, infrastructure, computation, matching, indexing, query processing, mapping, searching, retrieval, interoperability, exchange, and recommendations; memory, distributed, parallel, scalable, and high-performance computing, analysis, and optimization of big data; reviews, surveys, trends, prospects, and opportunities in data science research, innovation, and applications; applications of data science in science, business, government, culture, behavior, society and economy, health and medicine, humanity, nature, and artificial domains, as well as intelligent devices and services (including online/WEB, cloud, IoT, mobile, and social media); and ethical, quality, privacy, security, trust, and risk issues related to data science and analytics.