  In these lecture notes, a selection of frequently required statistical tools
will be introduced and illustrated. They allow to post-process data that stem
from, e.g., large-scale numerical simulations (aka sequence of random
experiments). From a point of view of data analysis, the concepts and
techniques introduced here are of general interest and are, at best, employed
by computational aid. Consequently, an exemplary implementation of the
presented techniques using the Python programming language is provided. The
contents of these lecture notes is rather selective and represents a
computational experimentalist's view on the subject of basic data analysis,
ranging from the simple computation of moments for distributions of random
variables to more involved topics such as hierarchical cluster analysis and the
parallelization of Python code.
