About this Course
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Learner Career Outcomes

33%

started a new career after completing these courses

34%

got a tangible career benefit from this course

17%

got a pay increase or promotion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 33 hours to complete

Suggested: 6 weeks of study, 6–10 hours per week....

English

Subtitles: English, Korean, Russian

Skills you will gain

Data StructureAlgorithmsJava Programming

Learner Career Outcomes

33%

started a new career after completing these courses

34%

got a tangible career benefit from this course

17%

got a pay increase or promotion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 33 hours to complete

Suggested: 6 weeks of study, 6–10 hours per week....

English

Subtitles: English, Korean, Russian

Syllabus - What you will learn from this course

Week
1
10 minutes to complete

Course Introduction

1 video (Total 9 min), 2 readings
1 video
2 readings
Welcome to Algorithms, Part I1m
Lecture Slides
9 hours to complete

Union−Find

5 videos (Total 51 min), 2 readings, 2 quizzes
5 videos
Quick Find10m
Quick Union7m
Quick-Union Improvements13m
Union−Find Applications9m
2 readings
Overview1m
Lecture Slides
1 practice exercise
Interview Questions: Union–Find (ungraded)
1 hour to complete

Analysis of Algorithms

6 videos (Total 66 min), 1 reading, 1 quiz
6 videos
Observations10m
Mathematical Models12m
Order-of-Growth Classifications14m
Theory of Algorithms11m
Memory8m
1 reading
Lecture Slides
1 practice exercise
Interview Questions: Analysis of Algorithms (ungraded)
Week
2
9 hours to complete

Stacks and Queues

6 videos (Total 61 min), 2 readings, 2 quizzes
6 videos
Resizing Arrays9m
Queues4m
Generics9m
Iterators7m
Stack and Queue Applications (optional)13m
2 readings
Overview1m
Lecture Slides
1 practice exercise
Interview Questions: Stacks and Queues (ungraded)
1 hour to complete

Elementary Sorts

6 videos (Total 63 min), 1 reading, 1 quiz
6 videos
Selection Sort6m
Insertion Sort9m
Shellsort10m
Shuffling7m
Convex Hull13m
1 reading
Lecture Slides
1 practice exercise
Interview Questions: Elementary Sorts (ungraded)
Week
3
9 hours to complete

Mergesort

5 videos (Total 49 min), 2 readings, 2 quizzes
5 videos
Bottom-up Mergesort3m
Sorting Complexity9m
Comparators6m
Stability5m
2 readings
Overview
Lecture Slides
1 practice exercise
Interview Questions: Mergesort (ungraded)
1 hour to complete

Quicksort

4 videos (Total 50 min), 1 reading, 1 quiz
4 videos
Selection7m
Duplicate Keys11m
System Sorts11m
1 reading
Lecture Slides
1 practice exercise
Interview Questions: Quicksort (ungraded)
Week
4
9 hours to complete

Priority Queues

4 videos (Total 74 min), 2 readings, 2 quizzes
4 videos
Binary Heaps23m
Heapsort14m
Event-Driven Simulation (optional)22m
2 readings
Overview10m
Lecture Slides
1 practice exercise
Interview Questions: Priority Queues (ungraded)
1 hour to complete

Elementary Symbol Tables

6 videos (Total 77 min), 1 reading, 1 quiz
6 videos
Elementary Implementations9m
Ordered Operations6m
Binary Search Trees19m
Ordered Operations in BSTs10m
Deletion in BSTs9m
1 reading
Lecture Slides
1 practice exercise
Interview Questions: Elementary Symbol Tables (ungraded)8m
4.9
1182 ReviewsChevron Right

Top reviews from Algorithms, Part I

By RMJun 1st 2017

This is a great class. I learned / re-learned a ton. The assignments were challenge and left a definite feel of accomplishment. The programming environment and automated grading system were excellent.

By BJJun 3rd 2018

Good contents and the logic of the whole course structure is very clear for a novice like me. The weekly homework is also awesome. Would recommend to anyone who wants to learn about computer science.

Instructors

Avatar

Kevin Wayne

Phillip Y. Goldman '86 Senior Lecturer
Computer Science
Avatar

Robert Sedgewick

William O. Baker *39 Professor of Computer Science
Computer Science

About Princeton University

Princeton University is a private research university located in Princeton, New Jersey, United States. It is one of the eight universities of the Ivy League, and one of the nine Colonial Colleges founded before the American Revolution....

Frequently Asked Questions

  • Once you enroll, you’ll have access to all videos and programming assignments.

  • No. All features of this course are available for free.

  • No. As per Princeton University policy, no certificates, credentials, or reports are awarded in connection with this course.

  • Our central thesis is that algorithms are best understood by implementing and testing them. Our use of Java is essentially expository, and we shy away from exotic language features, so we expect you would be able to adapt our code to your favorite language. However, we require that you submit the programming assignments in Java.

  • Part I focuses on elementary data structures, sorting, and searching. Topics include union-find, binary search, stacks, queues, bags, insertion sort, selection sort, shellsort, quicksort, 3-way quicksort, mergesort, heapsort, binary heaps, binary search trees, red−black trees, separate-chaining and linear-probing hash tables, Graham scan, and kd-trees.

    Part II focuses on graph and string-processing algorithms. Topics include depth-first search, breadth-first search, topological sort, Kosaraju−Sharir, Kruskal, Prim, Dijkistra, Bellman−Ford, Ford−Fulkerson, LSD radix sort, MSD radix sort, 3-way radix quicksort, multiway tries, ternary search tries, Knuth−Morris−Pratt, Boyer−Moore, Rabin−Karp, regular expression matching, run-length coding, Huffman coding, LZW compression, and the Burrows−Wheeler transform.

  • Weekly exercises, weekly programming assignments, weekly interview questions, and a final exam.

    The exercises are primarily composed of short drill questions (such as tracing the execution of an algorithm or data structure), designed to help you master the material.

    The programming assignments involve either implementing algorithms and data structures (deques, randomized queues, and kd-trees) or applying algorithms and data structures to an interesting domain (computational chemistry, computational geometry, and mathematical recreation). The assignments are evaluated using a sophisticated autograder that provides detailed feedback about style, correctness, and efficiency.

    The interview questions are similar to those that you might find at a technical job interview. They are optional and not graded.

  • This course is for anyone using a computer to address large problems (and therefore needing efficient algorithms). At Princeton, over 25% of all students take the course, including people majoring in engineering, biology, physics, chemistry, economics, and many other fields, not just computer science.

  • The two courses are complementary. This one is essentially a programming course that concentrates on developing code; that one is essentially a math course that concentrates on understanding proofs. This course is about learning algorithms in the context of implementing and testing them in practical applications; that one is about learning algorithms in the context of developing mathematical models that help explain why they are efficient. In typical computer science curriculums, a course like this one is taken by first- and second-year students and a course like that one is taken by juniors and seniors.

More questions? Visit the Learner Help Center.