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Use of data to study problems unique to agricultural and biological engineering, identify patterns, and make actionable insights. Course includes (1) exploratory data analysis including data profiling, missing data, description, and data visualization; (2) data processing techniques including singular value decomposition, dimensionality reduction, and Fourier and wavelet transforms; (3) machine learning techniques including regression, classification, feature selection, clustering, and neural networks. Course Information: 3 undergraduate hours. 3 graduate hours. Prerequisite: CS 101 or equivalent and MATH 257.