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Data science, across its variety of forms, is rooted in statistical calculations—involving both the technical knowledge and skill to assess the validity and applicability of these calculations, and the knowledge and skill to implement software or programming functions that execute the calculations

Data science, across its variety of forms, is rooted in statistical calculations—involving both the technical knowledge and skill to assess the validity and applicability of these calculations, and the knowledge and skill to implement software or programming functions that execute the calculations. Underpinning the application of statistical calculations are assumptions about systemic structures and their dynamics—e.g., whether or not entities or events operate independently from one another, whether the variability of measurements, relative to an assumed or imputed trend or structure, is “noise” adhering to a separate set of rules (or not), and so on. Historically, these skill sets and conceptions of reality have been most heavily utilized in scientific inquiry, in finance and insurance, and business operations research (e.g., supply chain management and resource allocation). More recently, data science has expanded into a much larger set of domains: marketing, medicine, entertainment, education, law, etc. This expansion has shifted a large portion of data scientists toward data about people—some of that data is directly generated, like emails and web searches, some of it is sensed, like location or physical activity. — https://www.epicpeople.org/data-science-and-ethnography/
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