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!
Ce cours fait partie de la Spécialisation Data Structures and Algorithms
Data Structures
proposé par
University of California San Diego
National Research University Higher School of Economics
À propos de ce cours
4.7
1,636 notes
Compétences que vous acquerrez
Binary Search TreePriority QueueHash TableStack (Abstract Data Type)List
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
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 min), 7 lectures, 2 quiz
7 vidéos
Arrays7 min
Singly-Linked Lists9 min
Doubly-Linked Lists4 min
Stacks10 min
Queues7 min
Trees11 min
Tree Traversal10 min
7 lectures
Welcome10 min
Slides and External References10 min
Slides and External References10 min
Slides and External References10 min
Available Programming Languages10 min
FAQ on Programming Assignments10 min
Acknowledgements10 min
1 exercice pour s'entraîner
Basic Data Structures10 min
Semaine
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 min), 1 lecture, 1 quiz
5 vidéos
Dynamic Arrays8 min
Amortized Analysis: Aggregate Method5 min
Amortized Analysis: Banker's Method6 min
Amortized Analysis: Physicist's Method7 min
Amortized Analysis: Summary2 min
1 lecture
Slides and External References10 min
1 exercice pour s'entraîner
Dynamic Arrays and Amortized Analysis8 min
Semaine
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 min), 6 lectures, 4 quiz
15 vidéos
Introduction6 min
Naive Implementations of Priority Queues5 min
Binary Trees1 min
Basic Operations12 min
Complete Binary Trees9 min
Pseudocode8 min
Heap Sort10 min
Building a Heap10 min
Final Remarks4 min
Overview7 min
Naive Implementations10 min
Trees for Disjoint Sets7 min
Union by Rank9 min
Path Compression6 min
Analysis (Optional)18 min
6 lectures
Slides10 min
Tree Height Remark10 min
Slides and External References10 min
Slides and External References10 min
Slides and External References10 min
Slides and External References10 min
3 exercices pour s'entraîner
Priority Queues: Quiz12 min
Quiz: Disjoint Sets8 min
Priority Queues and Disjoint Sets6 min
Semaine
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 min), 4 lectures, 3 quiz
22 vidéos
Analysing Service Access Logs7 min
Direct Addressing7 min
List-based Mapping8 min
Hash Functions3 min
Chaining Scheme6 min
Chaining Implementation and Analysis5 min
Hash Tables6 min
Phone Book Problem4 min
Phone Book Problem - Continued6 min
Universal Family9 min
Hashing Integers9 min
Proof: Upper Bound for Chain Length (Optional)8 min
Proof: Universal Family for Integers (Optional)11 min
Hashing Strings9 min
Hashing Strings - Cardinality Fix7 min
Search Pattern in Text7 min
Rabin-Karp's Algorithm9 min
Optimization: Precomputation9 min
Optimization: Implementation and Analysis5 min
Instant Uploads and Storage Optimization in Dropbox10 min
Distributed Hash Tables12 min
4 lectures
Slides and External References10 min
Slides and External References10 min
Slides and External References10 min
Slides and External References10 min
2 exercices pour s'entraîner
Hash Tables and Hash Functions8 min
Hashing6 min
4.7
281 avis83%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
14%
a obtenu une augmentation de salaire ou une promotion
Meilleurs avis
par 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!
par 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.
Enseignants
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
À propos 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.
À propos 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
À propos de la Spécialisation 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.
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