Statistical Foundations for Data Science

Catalog description

This course provides an advanced exploration of the theoretical and mathematical underpinnings of statistics essential for high-level data science and research. Students move beyond computational mechanics to master the principles of probability theory, statistical inference, and mathematical modeling. The curriculum emphasizes the application of advanced methodologies—including Bayesian inference, experimental design, and multivariate analysis—to solve complex, real-world problems. A central focus is placed on the critical evaluation of statistical models, the identification of bias, and the ethical implications of data-driven decision-making. Students will utilize high-level statistical programming to perform diagnostics, validate models, and communicate rigorous quantitative findings to both technical peers and strategic organizational leaders.