About Analytics

Aims

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

The aim is to contribute to consolidate 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

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.

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 in 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.

MDPI Publication Ethics Statement

MDPI is a member of the Committee on Publication Ethics (COPE). MDPI takes the responsibility to enforce a rigorous peer-review together with strict ethical policies and standards to ensure to add high quality scientific works to the field of scholarly publication. Unfortunately, cases of plagiarism, data falsification, inappropriate authorship credit, and the like, do arise. MDPI takes such publishing ethics issues very seriously and our editors are trained to proceed in such cases with a zero tolerance policy. To verify the originality of content submitted to our journals, we use iThenticate to check submissions against previous publications.

Book Reviews

Authors and publishers are encouraged to send review copies of their recent related books to the following address. Received books will be listed as Books Received within the journal's News & Announcements section.

MDPI
St. Alban-Anlage 66
CH-4052 Basel
Switzerland

Copyright / Open Access

Articles published in Analytics will be Open-Access articles distributed under the terms and conditions of the Creative Commons Attribution License (CC BY). The copyright is retained by the author(s). MDPI will insert the following note at the end of the published text:

© 2024 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).

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Reprints may be ordered. Please contact for more information on how to order reprints.

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