Data Science
This topical collection belongs to the section “Mathematics & Computer Science“.
Topical Collection Information
Dear Colleagues,
The topic collection Data Science has helped us to develop interdisciplinary linkages between the computer, statistics, mathematics, information and intelligence sciences, and it has fostered cross-domain interactions between academia and industry for data science and big data analytics. The topic collection Data Science welcomes contributions related, but not limited, to the following topics of interest:
- Data science and analytical methods;
- The machine learning foundations of data science;
- Infrastructures, tools and systems focusing on data processing and analytics;
- Real-world data science applications and case studies;
- Learning from data with domain knowledge;
- Emerging data science applications;
- Human-centric data science;
- Data science for the next digital frontier (telecommunications and 5G, predictive maintenance, sustainability and the environment, etc.);
- Systems for practical applications of data science, data analytics and applied machine learning, demonstrating real-world impact;
- Solutions or advances towards understanding the issues related to deploying data science technologies/solutions in the real world;
- Processes and Methodologies related to Data Science.
Prof. Dr. Kamran Munir
Prof. Dr. José Raúl Romero
Prof. Dr. Khalid Hafeez
Collection Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Encyclopedia is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- data science
- big data management
- applied machine learning
- predictive analytics
- data with domain knowledge

