The course will provide an introduction to Data Analysis techniques. The aim is to develop the basics of probabilities and statistics as needed to exploit experimental data, once at hand.
High Energy experiments may involve either precise measurements based on large data sample, or searches for discovery relying on small data sample. In both cases, a sound understanding of statistical methods is mandatory, both for experimentalists and theoreticians, in order to be able to produce and interpret correctly experimental results.

After a review of the basic concepts of statistics and probability, we will start with parameter estimation, including standard methods such as chi2 minimisation or maximum likelihood, as well as data combination. We will then then study interval estimation, including limit setting, and hypothesis testing (including the special case of goodness-of-fit testing). We will end with introductions to multivariate analysis and unfolding.
The mathematics needed is mostly elementary. Examples of applications of the techniques will be taken from current and past experiments.

Bibliographical reference:
- "Statistical Methods in Experimental Physics”, Second Edition, Frederick James (CERN) World Scientific (2006).
- "Statistical Data Analysis”, Glen Cowan (London) Clarendon Press, Oxford (1998).