Stanford study guides, course by course
Stanford runs on the quarter system: ten weeks from first lecture to final, with midterms landing as early as week four, so falling a week behind costs a tenth of the course. The intro CS sequence and MATH 51 carry the heaviest workload folklore, and several flagship courses (CS 106A, CS 229, CS 231N) publish lectures and assignments publicly, which means half the people searching these codes aren't enrolled at Stanford at all.
Stanford courses use a subject abbreviation plus number (CS 106A, MATH 51, PHYSICS 41), with letter suffixes marking sequence variants (106A/106B, CHEM 31A/31B) and 200-level numbers shared between advanced undergrads and grad students. The same codes appear in ExploreCourses and on the public course websites that make several of these classes famous far beyond campus.
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Computer Science
CS 106A: Programming Methodology
CS 106A is Stanford's famous introduction to programming, taught in Python. It covers control flow, functions, decomposition, lists, dictionaries, and graphics, and assumes zero prior experience. Its lectures and assignments are public, and through Code in Place it has been taught free to hundreds of thousands of people, so it's studied worldwide by enrolled students and self-learners alike.
CS 106B: Programming Abstractions
CS 106B follows 106A with programming abstractions in C++: recursion, ADTs and the standard collections, big-O, linked structures, trees, and hashing. It's the course where Stanford CS gets real, and like 106A its materials are public and heavily used by self-learners.
CS 107: Computer Organization and Systems
CS 107 takes students from C++ down to the machine: C programming, pointers and memory, bit-level representation, x86-64 assembly, and how the heap actually works, culminating in the famous heap allocator assignment. It's the systems gateway of the Stanford CS core.
CS 103: Mathematical Foundations of Computing
CS 103 is Stanford's discrete math and theory gateway: proof techniques, set theory, induction, graph basics, then finite automata, regular languages, and the first look at computability and P vs NP. For most students it's the first course where the deliverable is a proof, not a program.
CS 109: Probability for Computer Scientists
CS 109 is probability built for CS: counting, conditional probability, random variables and distributions, the central limit theorem, then maximum likelihood and the first real machine learning algorithms. It bridges the core sequence to CS 221 and CS 229 and is many students' favorite course in the major.
CS 111: Operating Systems Principles
CS 111 is Stanford's operating systems course, following CS 107 in the systems core: processes, multithreading and synchronization, scheduling, virtual memory, and file systems. Assignments put you on the implementation side of OS abstractions students previously only consumed.
CS 161: Design and Analysis of Algorithms
CS 161 is Stanford's core algorithms course: asymptotic analysis, divide and conquer, randomized algorithms, sorting and selection, hashing, trees, dynamic programming, greedy algorithms, and graph algorithms. It's the course technical interviews are downstream of, and a hinge point of the CS major.
CS 221: Artificial Intelligence: Principles and Techniques
CS 221 is Stanford's broad AI foundations course: search, Markov decision processes, reinforcement learning, games, constraint satisfaction, Bayesian networks, and a taste of logic, with homeworks mixing math and substantial coding. It's the survey that maps the whole field before the deeper 22x courses.
CS 229: Machine Learning
CS 229 is Stanford's graduate-level machine learning course, made world-famous by Andrew Ng's recorded lectures: generalized linear models, SVMs and kernels, deep learning foundations, unsupervised learning, and learning theory. It's simultaneously a campus rite of passage and one of the most self-studied courses on the internet.
CS 231N: Deep Learning for Computer Vision
CS 231N (historically 'Convolutional Neural Networks for Visual Recognition') covers image classification, backpropagation, CNN architectures, training at scale, and transformers for vision, with assignments implementing it all from NumPy up to PyTorch and a substantial final project. Its public notes and lectures made it the world's default deep learning curriculum.
CS 224N: Natural Language Processing with Deep Learning
CS 224N covers modern NLP from word vectors through recurrent networks, attention, and transformers to pretrained language models, with PyTorch assignments and a research-style final project. Chris Manning's recorded lectures made it the standard NLP curriculum worldwide, studied by far more people than ever enroll.
Mathematics
MATH 51: Linear Algebra, Multivariable Calculus, and Modern Applications
MATH 51 is Stanford's famous hybrid: linear algebra and multivariable differential calculus taught as one integrated course from an in-house textbook, required for CS, engineering, and most quantitative majors. It's many students' first encounter with college math at Stanford pace.
MATH 19: Calculus
MATH 19 opens Stanford's single-variable calculus sequence: limits, continuity, and differential calculus with a careful treatment of the functions underneath. It's built for students starting calculus at Stanford rather than placing past it, and it runs on the same ten-week clock as everything else.
MATH 20: Calculus
MATH 20 is the integral-calculus quarter of Stanford's single-variable sequence: the definite integral, the fundamental theorem, integration techniques, and applications. It sits between MATH 19's derivatives and MATH 21's series, and many students enter via AP credit placement rather than MATH 19.
MATH 21: Calculus
MATH 21 completes Stanford's single-variable sequence with sequences and series: convergence tests, power series, and Taylor series. It's the standard final gate before MATH 51 and widely considered the most conceptually demanding quarter of the three.
Physics
PHYSICS 41: Mechanics
PHYSICS 41 is Stanford's calculus-based mechanics course: kinematics, Newton's laws, energy, momentum, and rotation. It's the first course of the introductory sequence for engineers and physical science majors, and exams center on multi-step problems built from unfamiliar scenarios.
PHYSICS 43: Electricity and Magnetism
PHYSICS 43 is the electricity and magnetism quarter of Stanford's introductory physics sequence for engineers and physical science majors: fields, Gauss's law, circuits, magnetism, and induction. Sequence folklore consistently ranks it the hardest of the intro physics courses.
Chemistry
Economics
Statistics
Psychology
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