Analytics | Aims and Scope Update

To further enhance the quality of Analytics and its published research, under the guidance of our Editor-in-Chief, Prof. Dr. Carson K. Leung, the journal has updated and refined its aims and scope. The original scope and the updated version are outlined below:

Aims (new version):

Aims (old version):

Analytics (ISSN: 2813-2203) is an international, open access journal dedicated to publishing high-quality research on theoretical, methodological, and technological aspects of systematic computational data analysis. The journal provides an interdisciplinary forum for discussing data analytics in regard to science and engineering.

The aim is to contribute to consolidating the discipline of data analytics from the following perspectives:

Data analytics science: research and development of formal techniques that contribute to the quality of data analysis.
Data analytics engineering: analysis, design, implementation, and deployment of data analytics projects.

Analytics (ISSN: 2813-2203) is an international, open access journal that publishes high-quality papers on theoretical, methodological, and technological aspects of the systematic computational analysing of data. It provides an interdisciplinary forum to discuss data analytics in regard to science and engineering.

The aim is to contribute to consolidating the discipline of data analytics from the following perspectives:

Data analytics science: research and development of formal techniques that contribute to the quality of data analysis.
Data analytics engineering: analysis, design, implementation, and deployment of data analytics projects.

Scope (new version):

Scope (old version):

The scope of Analytics includes a broad range of topics across the field of data analytics projects, with a focus on both theoretical and applied aspects.

Data analytics science: theoretical, methodological and technological aspects of data analytics, including, but not limited to:

Information access and load: distributed, federated, edge computing, etc.;
Information storage and architecture: database systems, high-performance computing, etc.;
Information fusion: integration of different types of data (numerical, categorical, text, audio, image, video, etc.) and of structures (cross-sectional, time series, panel, data streams, etc.);
Data and information quality: preprocessing, cleansing, imputation, transformation, outlier detection, etc.;
Mathematics and dynamic of machine learning;
Statistical foundations of machine learning, analysis, modeling, and inference (e.g., Bayesian approaches);
Predictive analytics and its models: classification, regression, ensemble methods, etc.;
Descriptive analytics and its models: data lake, data mart, data mesh, data warehouse (DW), online analytical processing (OLAP) operations, clustering, bi-clustering, patterns, etc.;
Associative analytics and its models: associations, graph-based approaches;
Prescriptive analytics and its models: simulation and optimization methods, knowledge-based models for action recommendation, etc.;
Big data analytics: utilization, adaptation, evaluation, and improvement of methods, techniques, algorithms and heuristics specialized for handling and improving big data analytics, etc.;
Visualization;
Quality metrics;
Emergent topics: generative artificial intelligence (AI), AI hardware, quantum computing, etc.
Innovative contributions of data analytics science.

The scope of Analytics includes several areas of interest related to a wide range of topics involved in the success of data analytics projects.

Data analytics science: theoretical aspects of data analytics, including but not limited to:

Information access and load: distributed, federated, edge, etc.;
Information storage and architecture: database systems, high-performance computing, etc.;
Information fusion: combination of different types of data (numerical, categorical, text, sound, image, video, etc.) and of structure (cross-sectional, time series, panel, data streams, etc.);
Information quality: preprocessing, cleansing, imputation, transformation, etc.;
Statistical foundations, analysis, modeling, and inference;
Predictive Models: classification, regression, etc.;
Descriptive Models: clustering, bi-clustering, patterns, etc.;
Associative Models: associations, graph-based approaches;
Prescriptive Models: models to analyze simulated scenarios;
Visualization;
Quality metrics;
Emergent topics: generative artificial intelligence (AI), AI hardware, quantum computing, etc.
Innovative contributions of data analytics science.

Data analytics engineering: Theoretical, methodological and technological aspects of data analytics, including but not limited to:

Project planning and estimation;
Project analysis and design;
Project methodology;
Project validation;
Project deployment.
Frameworks, schemas, and international ISO/IEC/IEEE standards for data analytics engineering.

Data analytics engineering: Methodological and technological aspects of data analytics, including but not limited to:

Project planning and estimation;
Project analysis and design;
Project methodology;
Project validation;
Project deployment.

Data analytics projects: successful applications of data analytics to real-world problems across a wide variety of industries, including, but not limited to:

Biomedicine/health;
Pharmaceutical;
Manufacturing/production;
Agriculture;
Mining;
Finance/business;
Marketing;
Insurance;
Climate/natural hazards;
Sociology/education;
Internet of Things (IoT);
Smart cities;
Information technology (IT) services;
Cybersecurity;
Energy;
Tourism;
Sports.

Data analytics projects: successful application of data analytics to real-world problems, including, but not limited to, the following fields:

Biomedicine/health;
Pharmaceutical;
Manufacturing/production;
Agriculture;
Mining;
Finance/business;
Marketing;
Insurance;
Climate/natural hazards;
Sociology/education;
Internet of Things (IoT);
Smart cities.

For more detailed information, please visit: https://www.mdpi.com/journal/analytics/about.

Analytics Editorial Office