Princeton 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.
Fennie is independent and not affiliated with Princeton University. This is an unofficial study guide.
What makes it hard
It moves fast through a lot of classic algorithms, and the bar is implementation plus analysis: you code each data structure and reason precisely about its running time and correctness. Assignments are autograded on both correctness and performance, so an algorithm that works but is too slow fails, and exams test analysis and tracing, not memorized code.
What you'll cover
- • Analysis of algorithms and Big-O
- • Elementary and advanced sorting
- • Priority queues and symbol tables
- • Balanced search trees and hashing
- • Graph algorithms
- • String algorithms
The COS 226 study guide
How to study for Princeton COS 226, step by step.
- 1
Implement each data structure, don't just read it
COS 226 rewards building the structures yourself. Code each one from the ideas rather than copying, because the assignments and exams test whether you actually understand the mechanics and trade-offs.
- 2
Analyze running time for everything
Assignments are autograded on performance, not just correctness; a too-slow solution fails. Reason about the running time of every algorithm as you write it, and be ready to justify it on exams.
- 3
Practice tracing algorithms by hand
Exams test stepping through sorts, tree operations, and graph traversals on paper. Trace each algorithm's state by hand until you can predict its behavior without running it.
- 4
Start performance-sensitive assignments early
Getting a solution correct is half the work; getting it fast enough is the other half. Begin the day of release so you have time to profile and optimize, not just compile.
Today
Today's COS 226 plan
What a Fennie Daily Plan looks like for COS 226. Yours is built from your own syllabus and adapts every day to your deadlines and progress.
First plan free, no card required. Fennie is independent and unaffiliated with your school.
FAQ
Is COS 226 at Princeton hard?
It's demanding and moves quickly because the bar is implementation plus analysis: you code each data structure and prove it's efficient, and assignments are autograded on performance as well as correctness. Exams test tracing and analysis, so memorizing code doesn't work.
Can I take COS 226 online?
The material is widely available through the Sedgewick and Wayne 'Algorithms' book and its companion online course, so self-learners worldwide use it. You won't earn Princeton credit, but the lectures, exercises, and assignments mirror the on-campus course.
How do I study for COS 226 exams?
Implement each data structure yourself, analyze the running time of everything you write, and practice tracing algorithms by hand, predicting a sort or graph traversal's state without running it. Exams test analysis and tracing, not recall of code.
More Princeton courses
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 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.