International Journal of Data Science and Analytics

Aims & Scope

Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social sci­ence, and lifestyle.

The field encompasses the larger ar­eas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation.

It also tackles related new sci­entific chal­lenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and vis­ualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.

The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations.

First scientific journal in data science and analytics science Publishes original, fundamental and applied research outcomes in data and analytics theories, technologies and applications Promotes new scientific and technological approaches to strategic value creation in data-rich applications

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.8
2024	3.40

Journal Rank

Year	Value
2024	8416

Journal Citation Indicator

Year	Value
2024	976

SJR (SCImago Journal Rank)

Year	Value
2024	0.678

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	36

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Big data analytics on Apache Spark

  Citation: 326

  Authors: Salman, Ruslan, Xiaojun, Patrick Xiaogang, Joshua Zhexue

• Measuring objective and subjective well-being: dimensions and data sources

  Citation: 240

  Authors: Vasiliki, Lorenzo, Ioanna, Stefano, Rajesh, Maurizio, Luca

• A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images

  Citation: 214

  Authors: Joseph, Madelyn, Ruben, Clark

• Fake news detection based on news content and social contexts: a transformer-based approach

  Citation: 207

  Authors: Shaina, Chen

• The disaster of misinformation: a review of research in social media

  Citation: 202

  Authors: Sadiq, Saji K.

• Interpreting tree ensembles with inTrees

  Citation: 167

  Authors: Houtao

• A novel ensemble deep learning model for stock prediction based on stock prices and news

  Citation: 147

  Authors: Yang, Yi

• Automatic emotion detection in text streams by analyzing Twitter data

  Citation: 128

  Authors: Maryam, Elke, Emmanuel

• Data science and AI in FinTech: an overview

  Citation: 126

  Authors: Longbing, Qiang, Philip S.

• Sports analytics and the big-data era

  Citation: 115

  Authors: Elia, Ofer H., Ronnie

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