Probability and Statistics for Data Science II

(3 Credits)

This course is the sequel to Probability and Statistics for Data Science I ( DS-GA 1002 ). We cover more advanced topics in probability and statistics, with an emphasis on how these concepts arise in modern applied settings. The goal is to develop a deeper understanding of why these methods work: when should we rely on them, and when should we be more cautious? The course is a progression through the second half of Probability and Statistics for Data Science (Carlos Fernandez-Granda). Expected Topics: Correlation, simple regression, point estimation, probabilistic inequalities, the law of large numbers, the central limit theorem, confidence intervals, the bootstrap, hypothesis testing and p-values, principal component analysis, low-rank models, and regression and classification.