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This course provides a detailed overview of the processes and techniques used in creating data science applications. Emphasis is placed on popular algorithms for the analysis, classification, and mining of relational data. Students learn to implement data science algorithms and techniques over real-world data sets through assignments and projects in Python. Topics include data preparation and cleaning, summary statistics, basic data visualization techniques, feature selection, discretization, k nearest neighbors, naive bayes, decision trees, ensemble methods, apriori rule mining, and k-means clustering. Fall. Prerequisite: CPSC 122 or CPSC 222