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Abstract
In any other circumstance, it might make sense to define the extent of the terrain (Data Science) first, and then locate and describe the landmarks (Principles). But this data revolution we are experiencing defies a cadastral survey. Areas are continually being annexed into Data Science. For example, biometrics was traditionally statistics for agriculture in all its forms but now, in Data Science, it means the study of characteristics that can be used to identify an individual. Examples of non-intrusive measurements include height, weight, fingerprints, retina scan, voice, photograph/video (facial landmarks and facial expressions) and gait. A multivariate analysis of such data would be a complex project for a statistician, but a software engineer might appear to have no trouble with it at all. In any applied-statistics project, the statistician worries about uncertainty and quantifies it by modelling data as realisations generated from a probability space. Another app ....

Data Science , Data Engineering , Applied Statistics , Ierarchical Statistical Models , Measurement Error , Spatial Statistics , தகவல்கள் அறிவியல் , தகவல்கள் பொறியியல் , பயன்படுத்தப்பட்டது புள்ளிவிவரங்கள் ,