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!
This course is part of the Data Structures and Algorithms Specialization
Data Structures
Offered By
University of California San Diego
National Research University Higher School of Economics
87,022 enrolled
About this Course
109,278 recent views
Skills you will gain
Binary Search TreePriority QueueHash TableStack (Abstract Data Type)List
Syllabus - What you will learn from this course
Week
14 hours to complete
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 videos (Total 60 min), 7 readings, 2 quizzes
7 videos
Arrays7m
Singly-Linked Lists9m
Doubly-Linked Lists4m
Stacks10m
Queues7m
Trees11m
Tree Traversal10m
7 readings
Welcome10m
Slides and External References10m
Slides and External References10m
Slides and External References10m
Available Programming Languages10m
FAQ on Programming Assignments10m
Acknowledgements10m
1 practice exercise
Basic Data Structures10m
Week
21 hour to complete
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 videos (Total 31 min), 1 reading, 1 quiz
5 videos
Amortized Analysis: Aggregate Method5m
Amortized Analysis: Banker's Method6m
Amortized Analysis: Physicist's Method7m
Amortized Analysis: Summary2m
1 reading
Slides and External References10m
1 practice exercise
Dynamic Arrays and Amortized Analysis8m
Week
36 hours to complete
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 videos (Total 129 min), 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 readings
Slides10m
Tree Height Remark10m
Slides and External References10m
Slides and External References10m
Slides and External References10m
Slides and External References10m
3 practice exercises
Priority Queues: Quiz12m
Quiz: Disjoint Sets8m
Priority Queues and Disjoint Sets6m
Week
46 hours to complete
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 videos (Total 170 min), 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 readings
Slides and External References10m
Slides and External References10m
Slides and External References10m
Slides and External References10m
2 practice exercises
Hash Tables and Hash Functions8m
Hashing6m
Week
52 hours to complete
Binary Search Trees
In this module we study binary search trees, which are a data structure for doing searches on dynamically changing ordered sets. You will learn about many of the difficulties in accomplishing this task and the ways in which we can overcome them. In order to do this you will need to learn the basic structure of binary search trees, how to insert and delete without destroying this structure, and how to ensure that the tree remains balanced.
7 videos (Total 55 min), 2 readings, 1 quiz
7 videos
Search Trees5m
Basic Operations10m
Balance5m
AVL Trees5m
AVL Tree Implementation9m
Split and Merge9m
2 readings
Slides and External References10m
Slides and External References10m
1 practice exercise
Binary Search Trees20m
Week
64 hours to complete
Binary Search Trees 2
In this module we continue studying binary search trees. We study a few non-trivial applications. We then study the new kind of balanced search trees - Splay Trees. They adapt to the queries dynamically and are optimal in many ways.
4 videos (Total 36 min), 2 readings, 2 quizzes
4 videos
Applications10m
Splay Trees: Introduction6m
Splay Trees: Implementation7m
(Optional) Splay Trees: Analysis10m
2 readings
Slides and External References10m
Slides and External References10m
1 practice exercise
Splay Trees6m
4.7
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Top Reviews
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!
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.
Instructors
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
About 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.
About 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
About the Data Structures and Algorithms Specialization
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
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