Statistical Analysis and Data Mining is a leading journal published by WILEY, dedicated to exploring the latest advancements in statistical methods and data mining techniques. With an ISSN of 1932-1864 and an E-ISSN of 1932-1872, this journal serves as a significant platform for researchers and professionals in statistical analysis, computer science applications, and information systems. Covering a wide range of topics from innovative analytical methodologies to emerging data mining algorithms, the journal aims to disseminate high-quality research that contributes to the evolving landscape of data science. Ranked in the Q2 category for the fields of Analysis, Computer Science Applications, and Information Systems in 2023, it emphasizes its relevance and impact within academia. While it offers limited Open Access options, the insights shared in this publication are integral for those wishing to stay ahead in fast-paced research and data-driven industries. Since its inception in 2008 and continuing through 2024, Statistical Analysis and Data Mining invites submissions that reflect rigorous empirical research coupled with practical implications, making it a vital resource for the academic community.

Wiley Interdisciplinary Reviews: Computational Statistics is a leading journal published by WILEY, renowned for its influential contributions to the field of statistics and its application in computational studies. With an impressive impact factor reflected in its 2023 categorization as Q1 in Statistics and Probability, this journal ranks among the top in its category, positioned at 20 out of 278 in Scopus, placing it in the 92nd percentile for its discipline. The journal spans from 2009 to 2024 and offers a rich repository of interdisciplinary insights that encompass both theoretical advancements and practical applications of computational statistics, making it an invaluable resource for researchers, professionals, and students alike. While it does not currently offer open access, the journal's commitment to high-quality, peer-reviewed content ensures that it remains a trusted source for cutting-edge developments and methodologies in the rapidly evolving realm of computational statistics.

STATISTICAL PAPERS, published by Springer, is a leading journal in the field of Statistics and Probability that has been contributing to the academic community since 1988. With an impressive track record spanning over three decades, this journal falls within the prestigious Q2 quartile in both the Statistics and Probability and Statistics, Probability and Uncertainty categories, signifying its high-quality research output. It currently ranks #92 out of 278 in the Mathematics - Statistics and Probability category and #61 out of 168 in Decision Sciences - Statistics, Probability and Uncertainty, placing it in the 67th and 63rd percentiles respectively. Although the journal is not open access, it offers a vital platform for researchers, professionals, and students seeking to disseminate their findings and stay abreast of the latest advancements in statistical methods and applications. With its commitment to the highest standards of scholarship, STATISTICAL PAPERS plays a crucial role in shaping contemporary statistical discourse and fostering innovation within the field.

The Journal of Intelligent Information Systems, published by Springer since 1992, is a premier academic journal that offers a multidisciplinary platform in the fields of Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture, Information Systems, and Software. With an impressive impact reflected in its 2023 Q2 category rankings across multiple domains and a commendable standing in the Scopus Rankings—ranking #84 in Computer Networks and Communications and #101 in Artificial Intelligence—the journal is recognized for its contribution to advancing knowledge and innovation. Although it is not an open-access journal, its accessibility through institutional subscriptions ensures that a wide range of researchers, professionals, and students can engage with high-quality, peer-reviewed research that addresses the latest advancements and trends in intelligent systems. For over three decades, this journal has effectively bridged gaps between academia and industry, making it a vital resource for those aiming to push boundaries in intelligent information systems.

ACM Transactions on Knowledge Discovery from Data (TKDD), published by the Association for Computing Machinery, is a prestigious journal at the forefront of the interdisciplinary realm of data mining and knowledge discovery. With an impressive Q1 ranking in Computer Science and a Scopus rank of #43 out of 232, this journal stands out as a top-tier resource for innovative research that addresses complex challenges in data science. Covering impactful studies from 2007 to 2024, TKDD presents cutting-edge algorithms, methodologies, and applications that shape the future of knowledge extraction from vast datasets. While not an open-access journal, it provides a platform for researchers, professionals, and students to disseminate their findings and engage with the latest advancements in this rapidly evolving field. By fostering collaboration and knowledge sharing, TKDD plays a vital role in advancing the understanding and application of data analysis techniques, making it an essential read for anyone involved in the pursuit of knowledge from data.

Canadian Journal of Statistics - Revue Canadienne de Statistique is a prestigious publication in the field of statistics, managed by Wiley. Since its inception in 1973, this journal has served as an essential resource for researchers, practitioners, and students, offering insights into a diverse range of statistical methodologies and applications. With its impact reflected in its 2023 categorization as Q2 in Statistics and Probability and Q3 in Statistics, Probability and Uncertainty, the journal stands out among its peers, exemplifying rigorous standards in empirical research. The journal's ISSN is 0319-5724 and its E-ISSN is 1708-945X, providing a robust platform for the dissemination of knowledge in the field. While it does not offer open access, the journal remains highly regarded and well-cited, contributing significantly to the advancement of statistical theory and practice. As it continues to publish cutting-edge research through to 2024, the Canadian Journal of Statistics is a must-read for anyone seeking to stay informed on the latest trends and developments in statistics.

ADVANCES IN APPLIED MATHEMATICS, published by ACADEMIC PRESS INC ELSEVIER SCIENCE, is a prestigious journal that has served the mathematical community since 1980. With its ISSN 0196-8858 and E-ISSN 1090-2074, the journal is based in the United States, specifically in San Diego, CA. As a leading periodical in the field, it holds a notable Q2 ranking in Applied Mathematics and has been consistently ranked in the 43rd percentile among similar journals, illustrating its relevance and impact within the discipline. Although not an Open Access journal, ADVANCES IN APPLIED MATHEMATICS plays a crucial role in disseminating significant research findings, theoretical studies, and innovative applications of mathematics that address real-world problems. Researchers, professionals, and students alike will find valuable insights in its carefully curated publications, making it an essential resource for those looking to advance their understanding and application of mathematics.

Data Science and Engineering is a premier open access journal published by SPRINGERNATURE, dedicated to advancing the fields of data science, artificial intelligence, computational mechanics, and information systems. Since its inception in 2016, this journal has rapidly established itself as a leader in the academic community, boasting an impressive Q1 ranking in multiple computer science categories, including Artificial Intelligence, Software, and Information Systems. With a commitment to disseminating high-quality research, it caters to a diverse audience of researchers, professionals, and students eager to explore the intersection of data and technology. The journal's robust global reach, combined with its respected reputation, empowers authors to share their findings widely, facilitating breakthroughs and innovations across the digital landscape. Join the vibrant community of scholars contributing to this integral field of study, and stay informed with the latest research by accessing the journal freely online.

COMPUTATIONAL STATISTICS, published by Springer Heidelberg, is a prominent international journal that bridges the fields of computational mathematics and statistical analysis. Since its inception in 1996, this journal has served as a critical platform for disseminating high-quality research and advancements in statistical methodologies and computational techniques. Operating under Germany's esteemed scholarly tradition, it holds a commendable Q2 ranking in key categories such as Computational Mathematics and Statistics and Probability, reflecting its significant impact and relevance in the academic community. Although it does not offer Open Access, the journal remains a vital resource for researchers, professionals, and students seeking to enhance their understanding of the intricate interplay between computation and statistical inference. Each issue features rigorously peer-reviewed articles that contribute to the development of innovative methodologies and applications, thereby solidifying its role in shaping the future of computational statistics.

SIAM Journal on Mathematics of Data Science is an esteemed publication within the fields of applied mathematics and data science, published by SIAM PUBLICATIONS. This journal serves as a vital platform for researchers and practitioners, dedicated to disseminating high-quality research that addresses complex mathematical problems arising in the context of data science. The journal aims to bridge the gap between rigorous mathematical theory and practical applications, fostering interdisciplinary collaboration among mathematicians, data scientists, and statisticians. With its commitment to excellence, the SIAM Journal on Mathematics of Data Science contributes significantly to advancing the understanding and development of mathematical methodologies that analyze and interpret large datasets effectively. Researchers and professionals will find it an invaluable resource with its comprehensive articles, insightful reviews, and original research papers, which represent the forefront of innovative mathematical approaches in the evolving landscape of data science. For those interested in contributing to this dynamic field, the journal provides an array of access options tailored to diverse audiences.