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What is Data Science 36 about?

Linear algebra is essential in data science for data representation, dimensionality reduction, regression, and machine learning algorithms. Distance measures are crucial for clustering, classification, recommendation systems, anomaly detection, and similarity analysis. Hyperplanes, halfspaces, eigenvalues, and eigenvectors are utilized in classification, dimensionality reduction, graph analysis, and recommender systems, while Probability Mass Functions (PMFs) and Probability Density Functions (PDFs) describ

Author

Sohel

Language

EN