Quant Methods of Data Analysis

Prerequisites: calculus. Recommended preparation: linear algebra, statistics, computer programming. Introduction to the fundamentals of quantitative data analysis in Earth and environmental sciences. Topics: review of relevant probability, statistics and linear algebra; linear models and generalized least squares; Fourier analysis and introduction to spectral analysis; filtering time series (convolution,deconvolution,smoothing); factor analysis and empirical orthogonal functions; covariance and correlation; methods of interpolation; statistical significance and hypothesis testing; introduction to Monte Carlo methods for data analysis. Problem sets and term project require use of MATLAB or Python.