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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]
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]
Data engineering refers to the building of systems to enable the collection and usage of data. This data is usually used to enable subsequent analysis and data science, which often involves machine learning. [1] [2] Making the data usable usually involves substantial compute and storage, as well as data processing.
This page was last edited on 22 February 2016, at 06:39 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
A Master of Science in Data Science is an 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, [1] [2] similar to data mining.
Scientists typically are careful in recording their data, a requirement promoted by Ludwik Fleck (1896–1961) and others. [102] Though not typically required, they might be requested to supply this data to other scientists who wish to replicate their original results (or parts of their original results), extending to the sharing of any ...
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