  In these lectures I cover a number of topics in cosmological data analysis. I
concentrate on general techniques which are common in cosmology, or techniques
which have been developed in a cosmological context. In fact they have very
general applicability, for problems in which the data are interpreted in the
context of a theoretical model, and thus lend themselves to a Bayesian
treatment.
  We consider the general problem of estimating parameters from data, and
consider how one can use Fisher matrices to analyse survey designs before any
data are taken, to see whether the survey will actually do what is required. We
outline numerical methods for estimating parameters from data, including Monte
Carlo Markov Chains and the Hamiltonian Monte Carlo method. We also look at
Model Selection, which covers various scenarios such as whether an extra
parameter is preferred by the data, or answering wider questions such as which
theoretical framework is favoured, using General Relativity and braneworld
gravity as an example. These notes are not a literature review, so there are
relatively few references.
