A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures.
A few examples of questions that we are going to cover in this class are the following:
1. What is a good strategy of resizing a dynamic array?
2. How priority queues are implemented in C++, Java, and Python?
3. How to implement a hash table so that the amortized running time of all operations is O(1) on average?
4. What are good strategies to keep a binary tree balanced?
You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!
Este curso forma parte de Programa Especializado - Data Structures and Algorithms
Data Structures
ofrecido por
University of California San Diego
National Research University Higher School of Economics
Acerca de este Curso
4.7
1,638 calificaciones
Habilidades que obtendrás
Binary Search TreePriority QueueHash TableStack (Abstract Data Type)List
Programa - Qué aprenderás en este curso
Semana
1Basic Data Structures
In this module, you will learn about the basic data structures used throughout the rest of this course. We start this module by looking in detail at the fundamental building blocks: arrays and linked lists. From there, we build up two important data structures: stacks and queues. Next, we look at trees: examples of how they’re used in Computer Science, how they’re implemented, and the various ways they can be traversed. Once you’ve completed this module, you will be able to implement any of these data structures, as well as have a solid understanding of the costs of the operations, as well as the tradeoffs involved in using each data structure.
7 videos (Total 60 minutos), 7 readings, 2 quizzes
7 videos
Arrays7m
Singly-Linked Lists9m
Doubly-Linked Lists4m
Stacks10m
Queues7m
Trees11m
Tree Traversal10m
7 lecturas
Welcome10m
Slides and External References10m
Slides and External References10m
Slides and External References10m
Available Programming Languages10m
FAQ on Programming Assignments10m
Acknowledgements10m
1 ejercicio de práctica
Basic Data Structures10m
Semana
2Dynamic Arrays and Amortized Analysis
In this module, we discuss Dynamic Arrays: a way of using arrays when it is unknown ahead-of-time how many elements will be needed. Here, we also discuss amortized analysis: a method of determining the amortized cost of an operation over a sequence of operations. Amortized analysis is very often used to analyse performance of algorithms when the straightforward analysis produces unsatisfactory results, but amortized analysis helps to show that the algorithm is actually efficient. It is used both for Dynamic Arrays analysis and will also be used in the end of this course to analyze Splay trees.
5 videos (Total 31 minutos), 1 reading, 1 quiz
5 videos
Amortized Analysis: Aggregate Method5m
Amortized Analysis: Banker's Method6m
Amortized Analysis: Physicist's Method7m
Amortized Analysis: Summary2m
1 lectura
Slides and External References10m
1 ejercicio de práctica
Dynamic Arrays and Amortized Analysis8m
Semana
3Priority Queues and Disjoint Sets
We start this module by considering priority queues which are used to efficiently schedule jobs, either in the context of a computer operating system or in real life, to sort huge files, which is the most important building block for any Big Data processing algorithm, and to efficiently compute shortest paths in graphs, which is a topic we will cover in our next course. For this reason, priority queues have built-in implementations in many programming languages, including C++, Java, and Python. We will see that these implementations are based on a beautiful idea of storing a complete binary tree in an array that allows to implement all priority queue methods in just few lines of code. We will then switch to disjoint sets data structure that is used, for example, in dynamic graph connectivity and image processing. We will see again how simple and natural ideas lead to an implementation that is both easy to code and very efficient. By completing this module, you will be able to implement both these data structures efficiently from scratch.
15 videos (Total 129 minutos), 6 readings, 4 quizzes
15 videos
Naive Implementations of Priority Queues5m
Binary Trees1m
Basic Operations12m
Complete Binary Trees9m
Pseudocode8m
Heap Sort10m
Building a Heap10m
Final Remarks4m
Overview7m
Naive Implementations10m
Trees for Disjoint Sets7m
Union by Rank9m
Path Compression6m
Analysis (Optional)18m
6 lecturas
Slides10m
Tree Height Remark10m
Slides and External References10m
Slides and External References10m
Slides and External References10m
Slides and External References10m
3 ejercicios de práctica
Priority Queues: Quiz12m
Quiz: Disjoint Sets8m
Priority Queues and Disjoint Sets6m
Semana
4Hash Tables
In this module you will learn about very powerful and widely used technique called hashing. Its applications include implementation of programming languages, file systems, pattern search, distributed key-value storage and many more. You will learn how to implement data structures to store and modify sets of objects and mappings from one type of objects to another one. You will see that naive implementations either consume huge amount of memory or are slow, and then you will learn to implement hash tables that use linear memory and work in O(1) on average! In the end, you will learn how hash functions are used in modern disrtibuted systems and how they are used to optimize storage of services like Dropbox, Google Drive and Yandex Disk!
22 videos (Total 170 minutos), 4 readings, 3 quizzes
22 videos
Analysing Service Access Logs7m
Direct Addressing7m
List-based Mapping8m
Hash Functions3m
Chaining Scheme6m
Chaining Implementation and Analysis5m
Hash Tables6m
Phone Book Problem4m
Phone Book Problem - Continued6m
Universal Family9m
Hashing Integers9m
Proof: Upper Bound for Chain Length (Optional)8m
Proof: Universal Family for Integers (Optional)11m
Hashing Strings9m
Hashing Strings - Cardinality Fix7m
Search Pattern in Text7m
Rabin-Karp's Algorithm9m
Optimization: Precomputation9m
Optimization: Implementation and Analysis5m
Instant Uploads and Storage Optimization in Dropbox10m
Distributed Hash Tables12m
4 lecturas
Slides and External References10m
Slides and External References10m
Slides and External References10m
Slides and External References10m
2 ejercicios de práctica
Hash Tables and Hash Functions8m
Hashing6m
83%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
14%
consiguió un aumento de sueldo o ascenso
Principales revisiones
por DG•May 24th 2016
I like this course very much! Rope is the cleverest task I have ever done! Of course, I hope in future I will work on even more difficult problems, but this is pretty good already for me as a student!
por TT•Apr 6th 2018
Data Structures was really interesting over all, also assignments are quite challenging. It's important to consult the external references & discussion forums if you want to get the best of it.
Instructores
Alexander S. Kulikov
Visiting ProfessorDepartment of Computer Science and Engineering
Michael Levin
LecturerComputer Science
Daniel M Kane
Assistant ProfessorDepartment of Computer Science and Engineering / Department of Mathematics
Neil Rhodes
Adjunct FacultyComputer Science and Engineering
Acerca de University of California San Diego
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
Acerca de National Research University Higher School of Economics
National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communications, IT, mathematics, engineering, and more.
Learn more on www.hse.ru
Acerca del programa especializado Data Structures and Algorithms
This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice. No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs. Sorry, we do not believe in multiple choice questions when it comes to learning algorithms...or anything else in computer science! For each algorithm you develop and implement, we designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming. The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
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