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The International Journal of Data Science and Analytics is a peer-reviewed scientific journal covering data science. It was established in 2015 and is published by Springer Science+Business Media. The founding editor-in-chief is Longbing Cao (University of Technology Sydney). Current editor-in-chief is João Gama (INESC TEC and University of ...
Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. [ 4 ] Data science is "a concept to unify statistics , data analysis , informatics , and their related methods " to "understand and analyze actual phenomena " with data . [ 5 ]
Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. [1] Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text ...
Business and management research is a systematic inquiry that helps to solve business problems and contributes to management knowledge. It Is an applied research. Four factors (Easterby-Smith, 2008) combine to make business and management a distinctive focus for research : Transdiscipline approach
Data science process flowchart from Doing Data Science, by Schutt & O'Neil (2013) Analysis refers to dividing a whole into its separate components for individual examination. [ 10 ] Data analysis is a process for obtaining raw data , and subsequently converting it into information useful for decision-making by users. [ 1 ]
Scientific Data is a peer-reviewed open access scientific journal published by Nature Research since 2014. [1] It focuses on descriptions of data sets relevant to the natural sciences , medicine , engineering and social sciences , [ 2 ] which are provided as machine-readable data , complemented with a human oriented narrative.
The journal publishes articles under four categories of population data science: (1) Data use for population impact; (2) Bringing together and analysing data from multiple sources; (3) Identifying population level insights; and (4) Developing safe, privacy-sensitive and ethical infrastructure to support research.
The outer circle in the diagram symbolizes the cyclic nature of data mining itself. A data mining process continues after a solution has been deployed. The lessons learned during the process can trigger new, often more focused business questions, and subsequent data mining processes will benefit from the experiences of previous ones.