Recent ProfessorsNot yet assignedRecent SemestersFall 2023Class Size40 studentsCredits3PrerequisiteSTAT C1000 or STAT C1000H or equivalent or placement as determined by the college's multiple measures assessment process Transfers ToUC and CSUCal-GETC Area 2 · IGETC Area 2A · CSU GE Area B4DescriptionIntroduces statistical learning for data science. Emphasizes the following types of statistical models: Regression (Multiple Linear and Polynomial Regressions), Classification (Naive Bayes, Discriminant Analysis, Logistic Regression), Supervised Machine Learning (K-Nearest Neighbor, Tree models and their extensions), and Unsupervised Machine Learning (Principal Component Analysis, K-Means clustering). Covers applications of statistical programming for data science and the ethical use of data. 