Data science is an interdisciplinary field that utilizes scientific methods, algorithms, and systems to extract knowledge and insights from data. It combines elements of statistics, machine learning, and data analysis to transform raw data into valuable business intelligence. In essence, data science helps organizations make better decisions, predict future trends, and solve complex problems by uncovering hidden patterns and insights within data.
Data science is an interdisciplinary academic field[1] that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms and systems to extract or extrapolate knowledge from potentially noisy, structured, or unstructured data.[2]
Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine).[3] 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] It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge.[6] However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.[7][8]
A data scientist is a professional who creates programming code and combines it with statistical knowledge to summarize data.
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