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 faz parte do Programa de cursos integrados Estruturas de dados e algoritmos
estrutura de dadosestrutura de dados
oferecido por
Universidade da Califórnia, San Diego
National Research University Higher School of Economics
Informações sobre o curso
4.7
1,782 classificações
Habilidades que você terá
Binary Search TreePriority QueueHash TableStack (Abstract Data Type)List
Programa - O que você aprenderá com este curso
Semana
14 horas para concluir
Basic 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 vídeos (total de (Total 60 mín.) min), 7 leituras, 2 testes
7 videos
Arrays7min
Singly-Linked Lists9min
Doubly-Linked Lists4min
Stacks10min
Queues7min
Trees11min
Tree Traversal10min
7 leituras
Welcome10min
Slides and External References10min
Slides and External References10min
Slides and External References10min
Available Programming Languages10min
FAQ on Programming Assignments10min
Acknowledgements10min
1 exercício prático
Basic Data Structures10min
Semana
21 hora para concluir
Dynamic 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 vídeos (total de (Total 31 mín.) min), 1 leitura, 1 teste
5 videos
Dynamic Arrays8min
Amortized Analysis: Aggregate Method5min
Amortized Analysis: Banker's Method6min
Amortized Analysis: Physicist's Method7min
Amortized Analysis: Summary2min
1 leituras
Slides and External References10min
1 exercício prático
Dynamic Arrays and Amortized Analysis8min
Semana
36 horas para concluir
Priority 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 vídeos (total de (Total 129 mín.) min), 6 leituras, 4 testes
15 videos
Introduction6min
Naive Implementations of Priority Queues5min
Binary Trees1min
Basic Operations12min
Complete Binary Trees9min
Pseudocode8min
Heap Sort10min
Building a Heap10min
Final Remarks4min
Overview7min
Naive Implementations10min
Trees for Disjoint Sets7min
Union by Rank9min
Path Compression6min
Analysis (Optional)18min
6 leituras
Slides10min
Tree Height Remark10min
Slides and External References10min
Slides and External References10min
Slides and External References10min
Slides and External References10min
3 exercícios práticos
Priority Queues: Quiz12min
Quiz: Disjoint Sets8min
Priority Queues and Disjoint Sets6min
Semana
46 horas para concluir
Hash 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 vídeos (total de (Total 170 mín.) min), 4 leituras, 3 testes
22 videos
Analysing Service Access Logs7min
Direct Addressing7min
List-based Mapping8min
Hash Functions3min
Chaining Scheme6min
Chaining Implementation and Analysis5min
Hash Tables6min
Phone Book Problem4min
Phone Book Problem - Continued6min
Universal Family9min
Hashing Integers9min
Proof: Upper Bound for Chain Length (Optional)8min
Proof: Universal Family for Integers (Optional)11min
Hashing Strings9min
Hashing Strings - Cardinality Fix7min
Search Pattern in Text7min
Rabin-Karp's Algorithm9min
Optimization: Precomputation9min
Optimization: Implementation and Analysis5min
Instant Uploads and Storage Optimization in Dropbox10min
Distributed Hash Tables12min
4 leituras
Slides and External References10min
Slides and External References10min
Slides and External References10min
Slides and External References10min
2 exercícios práticos
Hash Tables and Hash Functions8min
Hashing6min
27%
comecei uma nova carreira após concluir estes cursos
33%
consegui um benefício significativo de carreira com este curso
12%
recebi um aumento ou promoção
Melhores avaliações
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.
por VK•Jul 16th 2018
Amazing course.\n\nInteresting exercises help with learning and understanding of internal mechanisms basic data structures, how some of them work, the pros and cons of each.\n\nThanks!
Instrutores
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
Sobre Universidade da Califórnia, 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.
Sobre 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 communicamathematics, engineering, and more.
Learn more on www.hse.ru
Sobre o Programa de cursos integrados Estruturas de dados e algoritmos
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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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