Basic Data Analysis and More - A Guided Tour Using Python

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.