Informações sobre o curso

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Resultados de carreira do aprendiz

32%

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34%

consegui um benefício significativo de carreira com este curso

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100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 33 horas para completar

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

Inglês

Legendas: Inglês, Coreano, Russo

Habilidades que você terá

Data StructureAlgorithmsJava Programming

Resultados de carreira do aprendiz

32%

comecei uma nova carreira após concluir estes cursos

34%

consegui um benefício significativo de carreira com este curso

17%

recebi um aumento ou promoção

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 33 horas para completar

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

Inglês

Legendas: Inglês, Coreano, Russo

Programa - O que você aprenderá com este curso

Classificação do conteúdoThumbs Up98%(45,207 classificações)Info
Semana
1

Semana 1

10 minutos para concluir

Course Introduction

10 minutos para concluir
1 vídeo (Total 9 mín.), 2 leituras
1 vídeos
2 leituras
Welcome to Algorithms, Part I1min
Lecture Slides
9 horas para concluir

Union−Find

9 horas para concluir
5 vídeos (Total 51 mín.), 2 leituras, 2 testes
5 videos
Quick Find10min
Quick Union7min
Quick-Union Improvements13min
Union−Find Applications9min
2 leituras
Overview1min
Lecture Slides
1 exercício prático
Interview Questions: Union–Find (ungraded)
1 hora para concluir

Analysis of Algorithms

1 hora para concluir
6 vídeos (Total 66 mín.), 1 leitura, 1 teste
6 videos
Observations10min
Mathematical Models12min
Order-of-Growth Classifications14min
Theory of Algorithms11min
Memory8min
1 leituras
Lecture Slides
1 exercício prático
Interview Questions: Analysis of Algorithms (ungraded)
Semana
2

Semana 2

9 horas para concluir

Stacks and Queues

9 horas para concluir
6 vídeos (Total 61 mín.), 2 leituras, 2 testes
6 videos
Stacks16min
Resizing Arrays9min
Queues4min
Generics9min
Iterators7min
Stack and Queue Applications (optional)13min
2 leituras
Overview1min
Lecture Slides
1 exercício prático
Interview Questions: Stacks and Queues (ungraded)
1 hora para concluir

Elementary Sorts

1 hora para concluir
6 vídeos (Total 63 mín.), 1 leitura, 1 teste
6 videos
Selection Sort6min
Insertion Sort9min
Shellsort10min
Shuffling7min
Convex Hull13min
1 leituras
Lecture Slides
1 exercício prático
Interview Questions: Elementary Sorts (ungraded)
Semana
3

Semana 3

9 horas para concluir

Mergesort

9 horas para concluir
5 vídeos (Total 49 mín.), 2 leituras, 2 testes
5 videos
Bottom-up Mergesort3min
Sorting Complexity9min
Comparators6min
Stability5min
2 leituras
Overview
Lecture Slides
1 exercício prático
Interview Questions: Mergesort (ungraded)
1 hora para concluir

Quicksort

1 hora para concluir
4 vídeos (Total 50 mín.), 1 leitura, 1 teste
4 videos
Selection7min
Duplicate Keys11min
System Sorts11min
1 leituras
Lecture Slides
1 exercício prático
Interview Questions: Quicksort (ungraded)
Semana
4

Semana 4

9 horas para concluir

Priority Queues

9 horas para concluir
4 vídeos (Total 74 mín.), 2 leituras, 2 testes
4 videos
Binary Heaps23min
Heapsort14min
Event-Driven Simulation (optional)22min
2 leituras
Overview10min
Lecture Slides
1 exercício prático
Interview Questions: Priority Queues (ungraded)
1 hora para concluir

Elementary Symbol Tables

1 hora para concluir
6 vídeos (Total 77 mín.), 1 leitura, 1 teste
6 videos
Elementary Implementations9min
Ordered Operations6min
Binary Search Trees19min
Ordered Operations in BSTs10min
Deletion in BSTs9min
1 leituras
Lecture Slides
1 exercício prático
Interview Questions: Elementary Symbol Tables (ungraded)8min

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Universidade de Princeton

Perguntas Frequentes – FAQ

  • 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.

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