This course covers theoretical and practical basic concepts about signal processing (Fourier spectrum, sampling, filtering, convolution, correlation) of timeseries with illustration on various real datasets from geosciences (seismology, gravimetry, meteorological data, …) and everyday life.

• Understand the concept of Fourier series decomposition (for periodic functions) and Fourier transform (for non-periodic functions);
• interpret an amplitude spectrum and a phase spectrum;
• link the frequency step of a Fourier transform and the duration of the observation;
• understand the effects of data sampling and aliasing;
• be able to filter a dataset (low/high/band pass butterworth filters);
• understand the concept of convolution and its link with convolution;
• compute and interpret the correlation between two datasets.

• Integral calculation
• Fourier series decomposition
• python programming (basic concepts)