1. Basics of Bayesian statistics
2. Parameter Estimation: coin example, gaussian noise and averages, change of variables (the light house example), central limit theorem, amplitude of a signal in presence of background, marginal distributions, binning of data, reliabilities, best estimates, error bars and correlations, maximum likelihood and least squares approximations
3. Cosmic Microwave Background (CMB) mapmaking: from time ordered data to CMB-sky maps, lossless mapmaking
4. The CMB likelihood function
5. Galaxy surveys
6. Computing the signal covariance matrix for a given theoretical model: window functions
7. Estimating the likelihood functions for CMB and galaxy surveys: Karhunen-Loeve method, optimal quadratic estimators
8. The Fisher matrix: limits and applications, forecasting
9. Markov chain monte carlo: principles and the Metropolis algorithm, parameter estimation, cmbeasy
10. Model selection, evidence, hypothesis testing
11. Assigning probabilities: entropy and its application in astronomy