This course presents examples from a variety of wavelet analysis applications in the Wolfram Language, including financial time series, edge detection and denoising of images, thresholding, image and data compression, and image fusion. Familiarity with Fourier transforms and data smoothing methods is recommended for this class. Learn to analyze a time series using wavelets for detecting discontinuities, isolating peaks and inspecting nonstationary behavior; apply wavelet analysis to financial data; detect edges and discontinuities in images and other two-dimensional data; reduce noise in images by removing higher-frequency components; and more.