Acerca de este Curso

483.944 vistas recientes

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.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Nivel intermedio

Aprox. 54 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Habilidades que obtendrás

Data Structure
Algorithms
Java Programming

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Nivel intermedio

Aprox. 54 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Instructores

Calificación del instructor

4,78/5 (1379 calificaciones)

Kevin Wayne

Phillip Y. Goldman '86 University Lecturer

Computer Science

1.256.524 alumnos

5 cursos

Robert Sedgewick

William O. Baker *39 Professor of Computer Science

Computer Science

1.286.110 alumnos

7 cursos

Programa - Qué aprenderás en este curso

Calificación del contenido97%(61,449 calificaciones)

Semana

1

Semana 1

1 hora para completar

Course Introduction

1 hora para completar

1 video (Total 9 minutos), 2 lecturas, 1 cuestionario

9 horas para completar

Union−Find

9 horas para completar

5 videos (Total 51 minutos), 2 lecturas, 2 cuestionarios

1 hora para completar

Analysis of Algorithms

1 hora para completar

6 videos (Total 66 minutos), 1 lectura, 1 cuestionario

Semana

2

Semana 2

9 horas para completar

Stacks and Queues

We consider two fundamental data types for storing collections of objects: the stack and the queue. We implement each using either a singly-linked list or a resizing array. We introduce two advanced Java features—generics and iterators—that simplify client code. Finally, we consider various applications of stacks and queues ranging from parsing arithmetic expressions to simulating queueing systems.

9 horas para completar

6 videos (Total 61 minutos), 2 lecturas, 2 cuestionarios

1 hora para completar

Elementary Sorts

1 hora para completar

6 videos (Total 63 minutos), 1 lectura, 1 cuestionario

Semana

3

Semana 3

9 horas para completar

Mergesort

9 horas para completar

5 videos (Total 49 minutos), 2 lecturas, 2 cuestionarios

1 hora para completar

Quicksort

1 hora para completar

4 videos (Total 50 minutos), 1 lectura, 1 cuestionario

Semana

4

Semana 4

9 horas para completar

Priority Queues

9 horas para completar

4 videos (Total 74 minutos), 2 lecturas, 2 cuestionarios

2 horas para completar

Elementary Symbol Tables

We define an API for symbol tables (also known as associative arrays, maps, or dictionaries) and describe two elementary implementations using a sorted array (binary search) and an unordered list (sequential search). When the keys are Comparable, we define an extended API that includes the additional methods min, max floor, ceiling, rank, and select. To develop an efficient implementation of this API, we study the binary search tree data structure and analyze its performance.

2 horas para completar

6 videos (Total 77 minutos), 1 lectura, 1 cuestionario

Semana

5

Semana 5

2 horas para completar

Balanced Search Trees

In this lecture, our goal is to develop a symbol table with guaranteed logarithmic performance for search and insert (and many other operations). We begin with 2−3 trees, which are easy to analyze but hard to implement. Next, we consider red−black binary search trees, which we view as a novel way to implement 2−3 trees as binary search trees. Finally, we introduce B-trees, a generalization of 2−3 trees that are widely used to implement file systems.

2 horas para completar

3 videos (Total 63 minutos), 2 lecturas, 1 cuestionario

9 horas para completar

Geometric Applications of BSTs

9 horas para completar

5 videos (Total 66 minutos), 1 lectura, 1 cuestionario

Semana

6

Semana 6

1 hora para completar

Hash Tables

We begin by describing the desirable properties of hash function and how to implement them in Java, including a fundamental tenet known as the uniform hashing assumption that underlies the potential success of a hashing application. Then, we consider two strategies for implementing hash tables—separate chaining and linear probing. Both strategies yield constant-time performance for search and insert under the uniform hashing assumption.

1 hora para completar

4 videos (Total 50 minutos), 2 lecturas, 1 cuestionario

26 minutos para completar

Symbol Table Applications

26 minutos para completar

4 videos (Total 26 minutos), 1 lectura

Reseñas

4.9

2002 reseña

5 stars
90,23 %
4 stars
8,03 %
3 stars
0,96 %
2 stars
0,23 %
1 star
0,53 %

Principales reseñas sobre ALGORITHMS, PART I

por GH2 de jul. de 2019

Excellent course, great material and the structure of the class allowed me to learn in depth and practice independently. I also appreciated the complexity of the automated assignment evaluation.

por JJ8 de jun. de 2020

The course is good and the concepts are clear to understand. Best course to choose if you want to learn Algorithms completely. Each and every concept is explained with good number of examples.

por VP8 de oct. de 2021

If you want to learn and explore more challenging parts of Algorithms, you should definitely learn this course. The experience and knowledge of the teacher is pretty obvious from the way he teaches.

por CS26 de oct. de 2021

Incredible learning experience. Every programmer in industry should take this course if only to dispel the idea that with the advent of cloud computing exponential algorithms can still ruin your day!

Ver todas las reseñas

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
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 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.
Which kinds of assessments are available in this course?
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.
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.

¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.

Algorithms, Part I