Probability and Statistics

Probability and Statistics teaches you to reason rigorously about uncertainty: how to build mathematical models of random phenomena, extract expectations and distributions from them, and then run that machinery in reverse to draw conclusions from data. You'll work through weekly problem sets and quizzes out of Bertsekas-Tsitsiklis, moving from coin-flip combinatorics to joint densities, limit theorems, and Bayesian inference, with two midterms and a final testing whether you can actually set up the models rather than just plug into formulas. As a junior-year requirement it sits at the hinge between your calculus-heavy lower division and the statistics, stochastic processes, machine learning, and signal-processing courses that assume you can already speak fluently in PDFs, conditional expectations, and the CLT.