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Master data management (MDM) is a discipline in which business and information technology collaborate to ensure the uniformity, accuracy, ...
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]
Frederick Taylor (1856–1915), leading proponent of scientific management. Scientific management is a theory of management that analyzes and synthesizes workflows.Its main objective is improving economic efficiency, especially labor productivity.
A high school student explains her engineering project to a judge in Sacramento, California, in 2015.. Science, technology, engineering, and mathematics (STEM) is an umbrella term used to group together the distinct but related technical disciplines of science, technology, engineering, and mathematics.
Systems management is enterprise-wide administration of distributed systems including (and commonly in practice) computer systems. [citation needed] Systems management is strongly influenced by network management initiatives in telecommunications.
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.
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 ...
Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]