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!
提供方
加州大学圣地亚哥分校
国立高等经济大学
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 4 小时
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 个视频 （总计 60 分钟）, 7 个阅读材料, 2 个测验
7 个视频
Arrays7分钟
Singly-Linked Lists9分钟
Doubly-Linked Lists4分钟
Stacks10分钟
Queues7分钟
Trees11分钟
Tree Traversal10分钟
7 个阅读材料
Welcome10分钟
Slides and External References10分钟
Slides and External References10分钟
Slides and External References10分钟
Available Programming Languages10分钟
FAQ on Programming Assignments10分钟
Acknowledgements10分钟
1 个练习
Basic Data Structures10分钟
周
2完成时间为 1 小时
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 个视频 （总计 31 分钟）, 1 个阅读材料, 1 个测验
5 个视频
Amortized Analysis: Aggregate Method5分钟
Amortized Analysis: Banker's Method6分钟
Amortized Analysis: Physicist's Method7分钟
Amortized Analysis: Summary2分钟
1 个阅读材料
Slides and External References10分钟
1 个练习
Dynamic Arrays and Amortized Analysis8分钟
周
3完成时间为 6 小时
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 个视频 （总计 129 分钟）, 6 个阅读材料, 4 个测验
15 个视频
Introduction6分钟
Naive Implementations of Priority Queues5分钟
Binary Trees1分钟
Basic Operations12分钟
Complete Binary Trees9分钟
Pseudocode8分钟
Heap Sort10分钟
Building a Heap10分钟
Final Remarks4分钟
Overview7分钟
Naive Implementations10分钟
Trees for Disjoint Sets7分钟
Union by Rank9分钟
Path Compression6分钟
Analysis (Optional)18分钟
6 个阅读材料
Slides10分钟
Tree Height Remark10分钟
Slides and External References10分钟
Slides and External References10分钟
Slides and External References10分钟
Slides and External References10分钟
3 个练习
Priority Queues: Quiz12分钟
Quiz: Disjoint Sets8分钟
Priority Queues and Disjoint Sets6分钟
周
4完成时间为 6 小时
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 个视频 （总计 170 分钟）, 4 个阅读材料, 3 个测验
22 个视频
Analysing Service Access Logs7分钟
Direct Addressing7分钟
List-based Mapping8分钟
Hash Functions3分钟
Chaining Scheme6分钟
Chaining Implementation and Analysis5分钟
Hash Tables6分钟
Phone Book Problem4分钟
Phone Book Problem - Continued6分钟
Universal Family9分钟
Hashing Integers9分钟
Proof: Upper Bound for Chain Length (Optional)8分钟
Proof: Universal Family for Integers (Optional)11分钟
Hashing Strings9分钟
Hashing Strings - Cardinality Fix7分钟
Search Pattern in Text7分钟
Rabin-Karp's Algorithm9分钟
Optimization: Precomputation9分钟
Optimization: Implementation and Analysis5分钟
Instant Uploads and Storage Optimization in Dropbox10分钟
Distributed Hash Tables12分钟
4 个阅读材料
Slides and External References10分钟
Slides and External References10分钟
Slides and External References10分钟
Slides and External References10分钟
2 个练习
Hash Tables and Hash Functions8分钟
Hashing6分钟
4.7
310 个审阅29%
完成这些课程后已开始新的职业生涯
35%
通过此课程获得实实在在的工作福利
12%
加薪或升职
热门审阅
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.
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!
讲师
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
关于 加州大学圣地亚哥分校
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.
关于 国立高等经济大学
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
关于 数据结构与算法 专项课程
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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