Statistics and Data Science

Topic Information

Dear Colleagues,

We are pleased to invite you to contribute to a Topic on “Statistics and Data Science”. In the era of big data and artificial intelligence, statistics and data science have become foundational disciplines driving innovation across science, engineering, economics, healthcare, and social sciences. These fields provide essential methodologies for data collection, analysis, interpretation, and decision-making under uncertainty.

Statistics offers rigorous theoretical frameworks for inference, modeling, and uncertainty quantification, while data science integrates statistical thinking with computational techniques and domain knowledge to extract meaningful insights from complex and large-scale data. Recent years have witnessed rapid developments in areas such as high-dimensional statistics, machine learning, causal inference, and data-driven methodologies, significantly expanding the scope and impact of these disciplines.

This Topic aims to highlight recent advances in both theoretical and applied aspects of statistics and data science, emphasizing their interdisciplinary nature and real-world applications. It will serve as a platform for researchers to present novel methodologies, innovative applications, and emerging challenges in modern data analysis. This Topic includes, but is not limited to, the following topics:

• Statistical inference and theory for complex and high-dimensional data;
• Machine learning and statistical learning methods;
• Big data analytics and scalable algorithms;
• Bayesian methods and computational statistics;
• Causal inference and experimental design;
• Time series analysis and stochastic processes;
• Functional data analysis and longitudinal data modeling;
• Robust statistics and uncertainty quantification;
• Data visualization and interpretability;
• Statistical methods in artificial intelligence;
• Applications in finance, economics, healthcare, engineering, and social sciences.

We invite researchers to submit original research articles, comprehensive reviews, and perspective papers that contribute to advancing the theory and practice of statistics and data science. The objective of this Topic is to foster interdisciplinary collaboration, promote methodological innovation, and address emerging challenges in data-driven research.

Prof. Dr. Jin-Ting Zhang
Dr. Tianming Zhu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	2.1	4.5	2008	19.2 Days	CHF 1800	Submit
AppliedMath appliedmath	0.7	1.1	2021	20.6 Days	CHF 1200	Submit
Big Data and Cognitive Computing BDCC	4.4	9.8	2017	23.1 Days	CHF 1800	Submit
Entropy entropy	2.0	5.2	1999	21.5 Days	CHF 2600	Submit
Mathematics mathematics	2.2	4.6	2013	17.3 Days	CHF 2600	Submit
Stats stats	1.0	1.8	2018	22.3 Days	CHF 1600	Submit
Sustainability sustainability	3.3	7.7	2009	17.9 Days	CHF 2400	Submit
Symmetry symmetry	2.2	5.3	2009	15.8 Days	CHF 2400	Submit

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Published Papers