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.

ofrecido por

## Algorithms, Part I

## Acerca de este Curso

### Resultados profesionales del estudiante

## 32%

## 34%

## 17%

#### 100 % en línea

#### Fechas límite flexibles

#### Nivel intermedio

#### Aprox. 53 horas para completar

#### Inglés (English)

### Habilidades que obtendrás

### Resultados profesionales del estudiante

## 32%

## 34%

## 17%

#### 100 % en línea

#### Fechas límite flexibles

#### Nivel intermedio

#### Aprox. 53 horas para completar

#### Inglés (English)

### ofrecido por

#### Universidad de Princeton

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.

## Programa - Qué aprenderás en este curso

**10 minutos para completar**

## Course Introduction

Welcome to Algorithms, Part I.

**10 minutos para completar**

**1 video**

**2 lecturas**

**9 horas para completar**

## Union−Find

We illustrate our basic approach to developing and analyzing algorithms by considering the dynamic connectivity problem. We introduce the union−find data type and consider several implementations (quick find, quick union, weighted quick union, and weighted quick union with path compression). Finally, we apply the union−find data type to the percolation problem from physical chemistry.

**9 horas para completar**

**5 videos**

**2 lecturas**

**1 ejercicio de práctica**

**1 hora para completar**

## Analysis of Algorithms

The basis of our approach for analyzing the performance of algorithms is the scientific method. We begin by performing computational experiments to measure the running times of our programs. We use these measurements to develop hypotheses about performance. Next, we create mathematical models to explain their behavior. Finally, we consider analyzing the memory usage of our Java programs.

**1 hora para completar**

**6 videos**

**1 lectura**

**1 ejercicio de práctica**

**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**

**2 lecturas**

**1 ejercicio de práctica**

**1 hora para completar**

## Elementary Sorts

We introduce the sorting problem and Java's Comparable interface. We study two elementary sorting methods (selection sort and insertion sort) and a variation of one of them (shellsort). We also consider two algorithms for uniformly shuffling an array. We conclude with an application of sorting to computing the convex hull via the Graham scan algorithm.

**1 hora para completar**

**6 videos**

**1 lectura**

**1 ejercicio de práctica**

**9 horas para completar**

## Mergesort

We study the mergesort algorithm and show that it guarantees to sort any array of n items with at most n lg n compares. We also consider a nonrecursive, bottom-up version. We prove that any compare-based sorting algorithm must make at least n lg n compares in the worst case. We discuss using different orderings for the objects that we are sorting and the related concept of stability.

**9 horas para completar**

**2 lecturas**

**1 ejercicio de práctica**

**1 hora para completar**

## Quicksort

We introduce and implement the randomized quicksort algorithm and analyze its performance. We also consider randomized quickselect, a quicksort variant which finds the kth smallest item in linear time. Finally, we consider 3-way quicksort, a variant of quicksort that works especially well in the presence of duplicate keys.

**1 hora para completar**

**1 lectura**

**1 ejercicio de práctica**

**9 horas para completar**

## Priority Queues

We introduce the priority queue data type and an efficient implementation using the binary heap data structure. This implementation also leads to an efficient sorting algorithm known as heapsort. We conclude with an applications of priority queues where we simulate the motion of n particles subject to the laws of elastic collision.

**9 horas para completar**

**4 videos**

**2 lecturas**

**1 ejercicio de práctica**

**1 hora 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.

**1 hora para completar**

**6 videos**

**1 lectura**

**1 ejercicio de práctica**

### Revisiones

##### Principales revisiones sobre ALGORITHMS, PART I

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.

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.

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!

An amazing course which enables one to appreciate and analyze various algorithms used in various applications. Must take for anyone who wants to build a career in computer science or related fields.

Extremely well designed course. The assignments touch all the concepts taught in the class. Lot of concepts get clarified when you try to reach 100% on each assignment. Highly recommend this course.

Best algorithm course ever seen. The assignments were awesome. Problem statements of assignments are well written. And the best thing is grading system. I loved the course and now going for part 2.

The best online course I've taken so far. The autograder really does its job! The tests are so thorough that it always takes me several attempts to finish an assignment, but it is always worth it!

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.

The material and the teaching method are absolutely amazing. Not just a course for algorithms, but a course on "how to teach a course right" .. very thankful to the instructors and facilitators.

Very clear and good course. Highly recommended. Besides learning about algorithms you'll learn about clean, clear and bug free code as well because of the very strict and high grading criteria.

The course lost a lot without tests. Theory is great. Assignments are pain in the azz - too much is assumed here and there. You must resubmit like a dozen of times to figure out what is wrong.

This Algorithms, Part 1 course is excellent. The best thing in the course was the autograder which tests assignment submissions very thoroughly and this really improved my programming skills.

fantastic intro course to start exploring the world of 'real' programming, with a focus on memory and clever processing, this gives you the first steps on the way to more powerful programming

This is an amazing course and it opened my eyes to seeing algorithms in a different perspective. I would recommend this course to anyone looking to learn algorithms or to optimize their own.

Extremely high quality content with clear explanations provided in a sensible order. I just wish Coursera would migrate the exercises so that I could have some more content alined practice.

Great program.! I pursued lots of tectonics and ideas in one place. Well organized materials and practical implementation of algorithm assignment makes us happy to involve in this program.

Learning algorithm has been experience of life time. Content of the course is well thought and structured. Thank you Princeton University for making these videos available free of cost.

This a challenging and rewarding course. The exercises include a ready project some of them with visualization clients and tests ready and that helps a lot. I can't wait to start part 2

This course is highly interesting and requires really rarely background in programming.The assignments is extremely fun and give a lot of insight when you crack it.Highly recommended.

This is one of the courses on Coursera that changed how I looked at problem solving in computer programming at a massive degree! My sincere thanks to all those who made this happen :)

## 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.