  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 approach to
uncertainty quantification is to find similar data sets, and then use the
variability of results between these data sets to capture the uncertainty. Both
approaches allow 'error bars' to be put on estimates obtained from the original
data set, although the interpretations are different. A third approach, that
concentrates on giving a single answer and gives up on uncertainty
quantification, could be considered as Data Engineering, although it has staked
a claim in the Data Science terrain. This article presents a few (actually
nine) statistical principles for data scientists that have helped me, and
continue to help me, when I work on complex interdisciplinary projects.
