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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]
The significantly reorganized revised edition of the book (2023) [2] expands and modernizes the presented mathematical principles, computational methods, data science techniques, model-based machine learning and model-free artificial intelligence algorithms. The 14 chapters of the new edition start with an introduction and progressively build ...
The Fourth Paradigm: Data-intensive Scientific Discovery is a 2009 anthology of essays on the topic of data science.Editors Tony Hey, Kristin Michele Tolle, and Stewart Tansley claim in the book's description that it presents the first broad look at the way that increasing use of data is bringing a paradigm shift to the nature of science.
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 ]
File formats often have a published specification describing the encoding method and enabling testing of program intended functionality. Not all formats have freely available specification documents, partly because some developers view their specification documents as trade secrets, and partly because other developers never author a formal specification document, letting precedent set by other ...
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Computational statistics, or statistical computing, is the study which is the intersection of statistics and computer science, and refers to the statistical methods that are enabled by using computational methods. It is the area of computational science (or scientific computing) specific to the mathematical science of statistics. This area is ...
Social data scientists use both digitized data [22] (e.g. old books that have been digitized) and natively digital data (e.g. social media posts). [23] Since such data often take the form of found data that were originally produced for other purposes (commercial, governance, etc.) than research, data scraping, cleaning and other forms of preprocessing and data mining occupy a substantial part ...