We will start from the basics of statistics, so in principle no prior requirements are necessary. Some basic experience with programming in C (C++) would be helpful for some of the homework problems. Bemerkung The basic concepts of statistical data analysis will be introduced in the lecture as well as a selection of modern methods. The first part starts with a repetition of the concepts of probability and the description of data as well as a discussion of the most important theoretical distributions for one or several variables (binomial, Poisson, Gauss). The estimation of parameters and the discussion of uncertainties based on several methods (likelihood, least squares, chi2) will lead into the second part. It covers propagation of uncertainty, interval estimation, limits and hypothesis tests (chi2, likelihood ratio, Kolmogorov-Smirnov). The lecture will be accompanied by an exercise session which will include hands-on applications of the methods discussed. A C++ based tool, ROOT, will be used for concrete applications. A background in the particle physics is not required.