Master In Data Science
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Master In Data Science
A Master of Science in Data Science is an Interdisciplinarity, interdisciplinary degree program designed to provide studies in scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. Overview As an area of expertise and field, data science is defined as a "concept to unify statistics, data analysis and their related quantitative and qualitative methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, statistical classification, cluster analysis, data mining, databases, and Data visualization, visualization. The degree is relatively new, with graduate schools, business schools, and data science centers often housing the programs. Data science degree programs ...
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Interdisciplinarity
Interdisciplinarity or interdisciplinary studies involves the combination of multiple academic disciplines into one activity (e.g., a research project). It draws knowledge from several other fields like sociology, anthropology, psychology, economics, etc. It is about creating something by thinking across boundaries. It is related to an ''interdiscipline'' or an ''interdisciplinary field,'' which is an organizational unit that crosses traditional boundaries between academic disciplines or schools of thought, as new needs and professions emerge. Large engineering teams are usually interdisciplinary, as a power station or mobile phone or other project requires the melding of several specialties. However, the term "interdisciplinary" is sometimes confined to academic settings. The term ''interdisciplinary'' is applied within education and training pedagogies to describe studies that use methods and insights of several established disciplines or traditional fields of study. Interd ...
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