What it is about
You will learn fundamental methods for analyzing experimental data: concepts of probability, distributions, error propagation, parameter estimation (Least Squares and Maximum Likelihood) and significance estimation. In the end you will be able to apply these methods to suitable data and estimate uncertainties as well as the significance of signals.
What you will be able to do
- understand and apply fundamental statistical concepts
- apply basic data analysis methods to suitable data
- apply first-order error propagation in the general case
- estimate and interpret statistical and systematic uncertainties
- estimate model parameters by fitting to (high-dimensional) data
- estimate the statistical significance of signals with non-vanishing background
- develop Python programs for data analysis problems of moderate complexity (participation in the tutorials)
What the module consists of
- VorlesungConveys the theoretical foundation; derivation of methods and concepts from principles
- ÜbungApplication of lecture content to examples; development of short Python programs in group work
Teaching method
- VorlesungSystematic conveyance of the theoretical foundations; derivations from fundamental principles
- Übung / GruppenarbeitApplication of the concepts to concrete examples and development of practical Python solutions
- Beamer, Tafel, Smartboard, ÜbungsblätterTo support presentation, visualization and exercise work