A few statistical principles for data science

In any other circumstance, it might make sense to define the extent of terrain (Data Science) first, and then locate describe landmarks (Principles). But this data revolution we are experiencing defies a cadastral survey. Areas continually being annexed into Data Science. For example, biometrics was traditionally statistics for agriculture in all its forms but now, Science, means study characteristics that can be used identify an individual. Examples non-intrusive measurements include height, weight, fingerprints, retina scan, voice, photograph/video (facial facial expressions) gait. A multivariate analysis such would complex project statistician, software engineer appear have no trouble with at all. applied-statistics project, statistician worries about uncertainty quantifies by modelling as realisations generated from probability space. Another approach quantification is find similar sets, use variability results between these sets capture uncertainty. Both approaches allow ‘error bars’ put on estimates obtained original set, although interpretations different. third approach, concentrates giving single answer gives up quantification, could considered Engineering, has staked claim Science terrain. This article presents few (actually nine) statistical principles scientists helped me, continue help when I work interdisciplinary projects.