Data Science & Analytics (M.S.)

The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions.

DKE publishes original research results, technical advances and news items concerning data engineering, knowledge engineering, and the interface of these two fields.

Data is a peer-reviewed, open access journal on data in science, with the aim of enhancing data transparency and reusability. The journal publishes in two sections: a section on the collection, treatment and analysis methods of data in science; a section publishing descriptions of scientific and scholarly datasets (one dataset per paper). The journal is published monthly online by MDPI. (2016 - present)

The CODATA Data Science Journal is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains, including science, technology, the humanities and the arts.

A forum for the examination of issues related to the research and applications of artificial intelligence techniques in data analysis across a variety of disciplines.

Publishes research works on a wide range of topics of applied statistics.

Lifetime Data Analysis is the only journal dedicated to statistical methods and applications for lifetime data. The journal advances and promotes statistical science in various applied fields that deal with lifetime data, including actuarial science, economics, engineering, environmental sciences, management, medicine, operations research, public health, and social and behavioral sciences.

The journal publishes original technical papers in both the research and practice of data mining and knowledge discovery, surveys and tutorials of important areas and techniques, and detailed descriptions of significant applications.

The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems.