Data Science & Engineering

Basic Information

English Title: Data Science & Engineering

ISSN (Print): 3105-7497

ISSN (Online): 3105-7500

CODEN: SKYGAY(International Standard Serial Identifier, a globally unique identifier assigned by the Chemical Abstracts Service (CAS), USA)

Publication Model: Gold Open Access (Gold OA), licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), with permanent free full-text access

Publisher: QUEST PRESS LIMITED

Publication Frequency: Bimonthly

Languages of Publication: Chinese, English

Submission Language: Chinese (English title, abstract, keywords, author names and affiliations required)

Distribution Support: China International Book Trading Corporation (CIBTC)

Import Registration Number: G015Z119

The journal is indexed by numerous authoritative academic databases worldwide. It is committed to promoting the global dissemination of high-quality research and collaborating with scholars across the globe to build an open, collaborative and forward-looking international academic community.

I. Core Positioning

Data Science & Engineering is an international academic journal focusing on data-driven innovation and engineering practice. The journal is dedicated to promoting the in-depth integration of data science theories and engineering applications, with a focus on cutting-edge developments in big data technology, intelligent algorithms and systems engineering. We particularly encourage interdisciplinary research paradigms that integrate computer science, statistics and domain expertise, providing academic support for innovative breakthroughs in the field of data science and engineering.

II. Scope of Submissions

The journal welcomes submissions in the following research areas:

Big Data Architecture and Distributed Systems

Machine Learning and Deep Learning Algorithms

Data Mining and Knowledge Discovery

Data Visualization and Visual Analytics

Database Technology and Data Warehousing

Data Security and Privacy Protection

Natural Language Processing and Text Mining

Recommender Systems and Intelligent Decision-Making

Data Governance and Data Quality

Data Science and Engineering Education

III. Aims and Vision

The journal aims to become an authoritative academic exchange platform in the field of data science and engineering, promoting the collaborative development of theoretical innovation and technological practice. We are committed to fostering in-depth cooperation between academia and industry, providing a high-quality platform for researchers, engineers and educators to share achievements and engage in academic dialogue, and facilitating the transformation of the data-driven research paradigm.