Princeton study guides, course by course
Princeton's intro sequences are rigorous and precept-driven: large lectures are paired with small precepts (Princeton's discussion sections) where engagement is expected, and grading leans on midterms, finals, and substantial programming or problem assignments. The COS and MAT gateways set a fast pace, and because several courses — COS 126, COS 226 — double as globally used online curricula, this guide speaks to both Princeton undergraduates and the self-learners working through the same materials.
Princeton courses use a three-letter department code plus a three-digit number — COS 126, MAT 202, ORF 245 — where the first digit roughly tracks level (100s introductory, 200s intermediate). Several intro courses, especially COS 126 and COS 226, are also famous worldwide through their open booksite and online versions, so search interest comes from both enrolled students and self-learners.
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COS 126 — Computer Science: An Interdisciplinary Approach
COS 126 is Princeton's introductory computer science course — Java programming, then a tour of the field from algorithms and data abstraction to theory of computation and machine architecture. Built around the Sedgewick and Wayne curriculum, it's used worldwide through its open booksite, so both Princeton students and self-learners follow it.
COS 217 — Introduction to Programming Systems
COS 217 is Princeton's systems programming course — C programming, the Unix toolchain, memory and pointers, modular design, assembly and machine architecture, and how programs actually run on hardware. It's a required CS core course that takes students from high-level coding down to the machine.
COS 226 — Algorithms and Data Structures
COS 226 is Princeton's renowned algorithms and data structures course — sorting, searching, trees and balanced trees, hashing, graphs, and string algorithms, all in Java with rigorous performance analysis. Through the Sedgewick and Wayne book and online course it has a global audience of self-learners alongside Princeton undergraduates.
COS 240 — Reasoning About Computation
COS 240 is Princeton's foundational theory course — mathematical proof, combinatorics, probability, graph theory, and an introduction to theoretical computer science including computability, complexity, and cryptography. It's a required CS core course that builds the rigorous reasoning the upper-level theory courses assume.
COS 324 — Introduction to Machine Learning
COS 324 is Princeton's introductory machine learning course — supervised and unsupervised learning, regression, classification, neural networks, optimization, and the underlying linear algebra and probability. It's a popular upper-level course with substantial mathematical prerequisites.
Mathematics
MAT 103 — Calculus I
MAT 103 is Princeton's introductory Calculus I — limits, derivatives, applications of differentiation, and an introduction to integration — for students who need a calculus foundation before continuing in the sequence or supporting science and economics coursework.
MAT 104 — Calculus II
MAT 104 is Princeton's Calculus II — integration techniques, applications of integrals, sequences and series, and parametric and polar topics — continuing from MAT 103 for students across the sciences, economics, and engineering-adjacent tracks.
MAT 201 — Multivariable Calculus
MAT 201 is Princeton's multivariable calculus course — vectors and the geometry of space, partial derivatives, multiple integrals, and vector calculus including line and surface integrals and the major theorems. It follows the single-variable sequence for students in quantitative fields.
MAT 202 — Linear Algebra with Applications
MAT 202 is Princeton's applied linear algebra course — systems of equations, matrices, vector spaces, linear transformations, eigenvalues and eigenvectors, and orthogonality, with applications across science and engineering. It's a common requirement for quantitative majors.
MAT 175 — Mathematics for Economics/Life Sciences
MAT 175 is Princeton's calculus course tailored for economics and life-science students — differentiation, optimization, integration, and selected multivariable and applied topics with examples drawn from those fields. It satisfies calculus requirements for majors that don't need the full math-track sequence.
Physics
PHY 103 — General Physics I
PHY 103 is Princeton's calculus-based introductory mechanics course — kinematics, Newton's laws, energy, momentum, and rotation — required for science and engineering tracks, with labs and problem sets alongside the exam-driven lecture core.
PHY 104 — General Physics II
PHY 104 is Princeton's calculus-based electromagnetism course — electric fields and potential, Gauss's law, circuits, magnetic fields, induction, and an introduction to electromagnetic waves — following PHY 103 for science and engineering students.
Chemistry
CHM 201 — General Chemistry I
CHM 201 is Princeton's first general chemistry course — atomic and molecular structure, bonding, stoichiometry, thermochemistry, and states of matter — required for science and pre-health tracks, with lab and exam-driven grading.
CHM 202 — General Chemistry II
CHM 202 continues Princeton's general chemistry sequence — chemical kinetics, equilibrium, acids and bases, thermodynamics, and electrochemistry — following CHM 201 with the same lab and exam-driven format.
Economics
ECO 100 — Introduction to Microeconomics
ECO 100 is Princeton's introductory microeconomics course — supply and demand, elasticity, consumer and producer behavior, market structures, and welfare and policy — taught in large lectures with precepts and required for the economics concentration and many other tracks.
ECO 101 — Introduction to Macroeconomics
ECO 101 is Princeton's introductory macroeconomics course — GDP, inflation, unemployment, aggregate demand and supply, fiscal and monetary policy, and growth — taught in large lectures with precepts and exams carrying most of the grade.
Operations Research & Financial Engineering
Molecular Biology
Neuroscience
Psychology
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