About this Course

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This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
All the features of this course are available for free.  It does not offer a certificate upon completion.

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Intermediate Level

Approx. 62 hours to complete

English

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Skills you will gain

Graphs
Data Structure
Algorithms
Data Compression

Flexible deadlines

Reset deadlines in accordance to your schedule.

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Approx. 62 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.81/5 (237 Ratings)

Robert Sedgewick

William O. Baker *39 Professor of Computer Science

Computer Science

1,412,325 Learners

7 Courses

Kevin Wayne

Phillip Y. Goldman '86 University Lecturer

Computer Science

1,380,892 Learners

5 Courses

Syllabus - What you will learn from this course

Content Rating96%(5,491 ratings)

Week1

Week 1

10 minutes to complete

Introduction

10 minutes to complete

1 video (Total 9 min), 2 readings

2 hours to complete

Undirected Graphs

2 hours to complete

6 videos (Total 98 min), 2 readings, 1 quiz

10 hours to complete

Directed Graphs

In this lecture we study directed graphs. We begin with depth-first search and breadth-first search in digraphs and describe applications ranging from garbage collection to web crawling. Next, we introduce a depth-first search based algorithm for computing the topological order of an acyclic digraph. Finally, we implement the Kosaraju−Sharir algorithm for computing the strong components of a digraph.

10 hours to complete

5 videos (Total 68 min), 1 reading, 2 quizzes

Week2

Week 2

2 hours to complete

Minimum Spanning Trees

2 hours to complete

6 videos (Total 85 min), 2 readings, 1 quiz

10 hours to complete

Shortest Paths

In this lecture we study shortest-paths problems. We begin by analyzing some basic properties of shortest paths and a generic algorithm for the problem. We introduce and analyze Dijkstra's algorithm for shortest-paths problems with nonnegative weights. Next, we consider an even faster algorithm for DAGs, which works even if the weights are negative. We conclude with the Bellman−Ford−Moore algorithm for edge-weighted digraphs with no negative cycles. We also consider applications ranging from content-aware fill to arbitrage.

10 hours to complete

5 videos (Total 85 min), 1 reading, 2 quizzes

Week3

Week 3

8 hours to complete

Maximum Flow and Minimum Cut

In this lecture we introduce the maximum flow and minimum cut problems. We begin with the Ford−Fulkerson algorithm. To analyze its correctness, we establish the maxflow−mincut theorem. Next, we consider an efficient implementation of the Ford−Fulkerson algorithm, using the shortest augmenting path rule. Finally, we consider applications, including bipartite matching and baseball elimination.

8 hours to complete

6 videos (Total 72 min), 2 readings, 2 quizzes

2 hours to complete

Radix Sorts

In this lecture we consider specialized sorting algorithms for strings and related objects. We begin with a subroutine to sort integers in a small range. We then consider two classic radix sorting algorithms—LSD and MSD radix sorts. Next, we consider an especially efficient variant, which is a hybrid of MSD radix sort and quicksort known as 3-way radix quicksort. We conclude with suffix sorting and related applications.

2 hours to complete

6 videos (Total 85 min), 1 reading, 1 quiz

Week4

Week 4

2 hours to complete

Tries

2 hours to complete

3 videos (Total 75 min), 2 readings, 1 quiz

10 hours to complete

Substring Search

In this lecture we consider algorithms for searching for a substring in a piece of text. We begin with a brute-force algorithm, whose running time is quadratic in the worst case. Next, we consider the ingenious Knuth−Morris−Pratt algorithm whose running time is guaranteed to be linear in the worst case. Then, we introduce the Boyer−Moore algorithm, whose running time is sublinear on typical inputs. Finally, we consider the Rabin−Karp fingerprint algorithm, which uses hashing in a clever way to solve the substring search and related problems.

10 hours to complete

5 videos (Total 75 min), 1 reading, 2 quizzes

Week5

Week 5

2 hours to complete

Regular Expressions

2 hours to complete

5 videos (Total 83 min), 2 readings, 1 quiz

10 hours to complete

Data Compression

10 hours to complete

4 videos (Total 80 min), 1 reading, 2 quizzes

Week6

Week 6

1 hour to complete

Reductions

Our lectures this week are centered on the idea of problem-solving models like maxflow and shortest path, where a new problem can be formulated as an instance of one of those problems, and then solved with a classic and efficient algorithm. To complete the course, we describe the classic unsolved problem from theoretical computer science that is centered on the concept of algorithm efficiency and guides us in the search for efficient solutions to difficult problems.

1 hour to complete

4 videos (Total 40 min), 2 readings, 1 quiz

2 hours to complete

Linear Programming (optional)

2 hours to complete

4 videos (Total 61 min), 1 reading, 1 quiz

2 hours to complete

Intractability

Is there a universal problem-solving model to which all problems that we would like to solve reduce and for which we know an efficient algorithm? You may be surprised to learn that we do no know the answer to this question. In this lecture we introduce the complexity classes P, NP, and NP-complete, pose the famous P = NP question, and consider implications in the context of algorithms that we have treated in this course.

2 hours to complete

6 videos (Total 85 min), 1 reading, 1 quiz

Reviews

4.9

328 reviews

5 stars
93.87%
4 stars
5.02%
3 stars
0.44%
2 stars
0.27%
1 star
0.38%

TOP REVIEWS FROM ALGORITHMS, PART II

by PJAug 29, 2020

"Remind me again why I'm trying to figure out if Detroit was eliminated back in American League '96" - me, at 4:00 AM

"Where is Week 7?" me, a few days later.

by HMMay 30, 2019

This is an excellent course as an introduction to algorithms. Together with part 1, the Princeton Algorithms series is the best MOOC course I've ever done.

by LSMar 4, 2020

I wrote my review on Linkedin. Thank you very much for the great course ! https://www.linkedin.com/pulse/algorithms-part-ii-mission-accomplished-luigi-selmi/

by MAJun 29, 2019

Dr. Sedgewick is simply amazing. I enjoy how he motivates the need for efficient algorithms and applications. I only wish he would offer more courses!

View all reviews

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When will I have access to the lectures and assignments?
Once you enroll, you’ll have access to all videos and programming assignments.
Do I need to pay for this course?
No. All features of this course are available for free.
Can I earn a certificate in this course?
No. As per Princeton University policy, no certificates, credentials, or reports are awarded in connection with this course.
I have no familiarity with Java programming. Can I still take 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.
Which algorithms and data structures are covered in this course?
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.
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.
What kinds of assessments are available in this course?
Weekly programming assignments and interview questions.
The programming assignments involve either implementing algorithms and data structures (graph algorithms, tries, and the Burrows–Wheeler transform) or applying algorithms and data structures to an interesting domain (computer graphics, computational linguistics, and data compression). 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.
I am/was not a Computer Science major. Is this course for me?
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.
How does this course differ from Design and Analysis of Algorithms?
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.

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Algorithms, Part II