Statistics for Applications
Philippe Rigollet teaches this MIT course on the theoretical foundations of statistical methods, aimed at understanding the mathematics behind efficient statistical techniques rather than just applying them by rote. The course provides a syllabus, lecture slides, lecture videos, and problem sets, moving through estimation theory, hypothesis testing, and regression with the mathematical rigor of a graduate-level treatment. A nonparametric regression plot from Rigollet's own slides illustrates the course's data-fitting focus on the course page. It sits in MIT's Mathematics department under Probability and Statistics and works as a natural next step after an introductory probability and statistics course like 18.05 for students who want the underlying theory. The course assumes comfort with calculus and linear algebra and rewards you with the mathematical grounding to derive, not just use, common statistical procedures.