This Technical Short Course addresses some important methods in advanced Statistics, therefore basic knowledge in statistics and also in programming with R is required. The course includes both introductory lectures concerning theoretical fundamentals as well as practical exercises with a main focus on topics in climate research. There are four parts dealing with i) fundamental techniques in multivariate statistics like empirical orthogonal functions (EOF), principal component analysis (PCA) or common factor analysis (CFA); ii) calibration and validation of multivariate statistical models; iii) different techniques of statistical downscaling (including transfer functions as well as synoptic approaches); iv) statistical bias correction of climate information from global and regional climate models. 

Python is a widely used high-level programming language. In recent years it has developed to become a prevalent modeling and problem solving tool in science and research. Moreover, it has become an industrial strength language. Python supports multiple programming paradigms including object-oriented and functional programming. The success of this programming language is based on a large and comprehensive standard library and thousands of third-party modules. Both Python's standard library and the community-contributed modules provide fast problem solving algorithms for data analysis in all research fields including climate and environmental research.

This course aims to give a comprehensive introduction to Python. It is adapted to applications in climate and environmental research. If possible sample applications are chosen from your field of research. This course is intended for PhD students with none or little previous knowledge of Python. However, to attend this course you should be familiar with the basic concepts of programming and you should know at least one other programming language.