Quantitative Foundation for Statistical Data Science. 3 credits

Catalog description

This course provides an introduction to the quantitative foundations used in statistical methodologies and theory, with a focus on topics necessary for success in graduate-level statistics coursework. Topics such as a review of limits, continuity, polynomial, logarithmic and exponential derivatives and integrals, selected techniques of integration, basic sequences and series, and multivariate vector analysis and differentiation are covered, with a strong emphasis on their application in understanding statistical theory and methodology. This course does not replace a traditional calculus sequence for students interested in a theoretical foundation for these mathematical concepts. Additionally, the course explores likelihood expansions, information theory, matrix algebra for regression, and eigenstructures for multivariate analysis. Through integrated examples, students will gain insight into how these foundational concepts are applied in statistical data science practice. Offered by Statistics . May not be repeated for credit.