Scope and Research Areas

The American Journal of Data Science and Analytics (AJDSA) is a peer-reviewed, open-access international journal dedicated to publishing innovative, original, and high-impact research in data science, analytics, artificial intelligence, and related interdisciplinary domains. The journal serves as a global platform for scholars, researchers, practitioners, and industry professionals to advance data-driven knowledge and develop analytical solutions to complex real-world problems.

AJDSA welcomes contributions that demonstrate methodological rigor, theoretical advancement, and practical relevance. The journal emphasizes interdisciplinary research that integrates data, algorithms, computational tools, and domain expertise to inform decision-making, optimize systems, and drive innovation across sectors.

Core Scope

AJDSA is committed to promoting interdisciplinary and multidisciplinary research that spans, integrates, or transcends traditional disciplinary boundaries. The journal encourages submissions that:

ü  Explore interactions between data science and domain-specific fields.

ü  Address global, regional, or industry-specific challenges using analytical approaches.

ü  Apply theoretical frameworks, statistical models, and computational methods to advance scholarly understanding.

ü  Emphasize real-world applications, policy implications, or industry relevance.

ü  Utilize emerging technologies, large-scale data, and advanced analytical methodologies.

Through this broad scope, AJDSA supports the development of data-driven knowledge that contributes to scientific progress and societal advancement.

Key Research Areas Covered

AJDSA accepts submissions from a wide range of disciplines and application areas, including but not limited to the following categories.

Data Science, Analytics, and Artificial Intelligence

  • Data science methodologies and computational frameworks
  • Statistical analysis, inference, and probabilistic modeling
  • Machine learning, deep learning, and neural networks
  • Big data analytics and scalable data processing
  • Predictive analytics, forecasting, and decision support systems
  • Data mining, pattern recognition, and knowledge discovery
  • Explainable AI, model interpretability, and algorithmic transparency
  • Reinforcement learning and intelligent systems

 

Computer Science, Information Systems, and Engineering

  • Data engineering, pipelines, and data infrastructure
  • Cloud computing and distributed analytics systems
  • Database systems, data management, and information retrieval
  • Natural language processing (NLP) and text analytics
  • Computer vision and image analytics
  • Internet of Things (IoT) data analytics
  • Cybersecurity analytics and data privacy engineering
  • Software engineering for data-driven applications

Business Analytics, Management, and Economics

  • Business intelligence and decision analytics
  • Financial analytics, fintech, and risk modeling
  • Marketing analytics and consumer behavior modeling
  • Operations research and supply chain analytics
  • Organizational analytics and performance measurement
  • Economic modeling, econometrics, and policy analytics
  • Strategic analytics and data-driven management

Healthcare, Life Sciences, and Bioinformatics

  • Healthcare analytics and clinical decision support
  • Biomedical data analysis and bioinformatics
  • Epidemiological modeling and public health analytics
  • Medical imaging and diagnostic analytics
  • Genomics, proteomics, and computational biology
  • Health informatics and electronic health record (EHR) analytics

Social Sciences, Policy, and Human-Centered Analytics

  • Social data analytics and computational social science
  • Behavioral analytics and decision science
  • Public policy modeling and governance analytics
  • Education analytics and learning systems
  • Urban analytics and smart cities
  • Ethical, legal, and societal implications of data and AI

Visualization, Human–Data Interaction, and Communication

  • Data visualization and visual analytics
  • Interactive dashboards and decision interfaces
  • Human–AI interaction and usability studies
  • Information design and analytical storytelling
  • Cognitive aspects of data interpretation

Special Emphasis Areas

AJDSA gives particular consideration to emerging and high-impact interdisciplinary themes, including:

  • Artificial intelligence and responsible data science
  • Ethical AI, bias mitigation, and fairness in analytics
  • Sustainable development and data-driven sustainability solutions
  • Digital transformation and data-driven innovation
  • Smart systems, automation, and Industry 4.0
  • Cybersecurity analytics and data privacy
  • Climate analytics and environmental data science
  • AI applications in healthcare, finance, and public policy

Types of Submissions Accepted

AJDSA accepts a diverse range of scholarly contributions, including:

✓ Original Research Articles
✓ Theoretical and Conceptual Papers
✓ Review and Systematic Review Articles
✓ Case Studies and Applied Research
✓ Methodological and Technical Papers
✓ Short Communications
✓ Book Reviews
✓ Conference and Working Papers

All submissions undergo a rigorous double-blind peer-review process to ensure academic quality, originality, and relevance.

Interdisciplinary Focus

AJDSA strongly encourages manuscripts that:

✓ Combine methods, theories, or data from multiple disciplines.
✓ Address complex problems that cannot be solved by a single field alone.
✓ Offer innovative analytical frameworks for interdisciplinary problem-solving.
✓ Advance understanding of interconnected technological, economic, and social systems.

Authors who are uncertain about the relevance of their manuscript to AJDSA’s scope are encouraged to contact the editorial office for pre-submission guidance.

Email: editor.ajdsa@americanscholarly.us