1. Data Analysis with MATLAB or Python 2. Systematic explorations of a new dataset 3. Modeling observational noise with random variables 4. Linear models as the foundation of data analysis 5. Least squares with prior information 6. Detecting periodicities with Fourier analysis 7. Modeling time-dependent behavior with filters 8. Undirected data analysis using factors, empirical orthogonal functions and clusters 9. Detecting and understanding correlations among data 10. Interpolation, Gaussian Process Regression and Kriging 11. Approximate methods, including linearization and artificial neural networks 12. Assessing the significance of results