Recent ProfessorsNot yet assignedRecent SemestersNo scheduled sections on recordClass SizeNot publishedCredits4PrerequisiteIntermediate Algebra or equivalent or higher, or placement into any transfer-level math/statistics Transfers ToUC and CSUCal-GETC Area 2DescriptionThis course examines fundamental concepts that are the building blocks for data science work, include gathering and summarizing data (descriptive statistics) and relationships between variables, probability techniques, and sampling distributions, hypothesis testing, chi-square, t- tests, analysis of variance, and predictive techniques to facilitate decision- making (inferential statistics). Students will study correlation and regression analyses such as linear models for data science and multivariate regression and the application of technology for statistical analysis including the interpretation of the relevance of the statistical findings to data science. The course will examine applications using data from disciplines including engineering, business, economics, natural and social sciences, psychology, health science, information technology, and education. There will be a hands-on approach to statistical analysis using Python and R. 