Provides a doctoral-level foundation in probability and statistical inference for data-intensive research. Topics include probability spaces, random variables, common univariate and multivariate distributions, expectation, laws of large numbers, and the Central Limit Theorem; principles of estimation and hypothesis testing, including likelihood-based and Bayesian formulations; and applied techniques used in data science such as resampling (bootstrap, permutation), nonparametric inference, generalized linear models, and probabilistic modeling for complex data. Emphasizes interpretation, reproducible computation, and linking statistical theory to real research problems in science, engineering, and health data. Prerequisite: DATA 5301 Foundations of Data Science or concurrent enrollment, or consent of the instructor.

An introduction to the end-to-end process of going from unstructured, messy data to knowledge and actionable insights. Provides a broad overview of what data science means and systems and tools commonly used for data science and illustrates the principles of data science through several case studies, including business, marketing, medical imaging, and biology, among others. Prerequisite: MATH 3330.