University of California San Diego

Data Structures

This course is part of Data Structures and Algorithms Specialization

Taught in English

Some content may not be translated

Neil Rhodes
Daniel M Kane
Michael Levin

Instructors: Neil Rhodes

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Course

Gain insight into a topic and learn the fundamentals

4.6

(5,384 reviews)

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93%

Intermediate level

Recommended experience

24 hours (approximately)
Flexible schedule
Learn at your own pace

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Assessments

9 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(5,384 reviews)

|

93%

Intermediate level

Recommended experience

24 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Data Structures and Algorithms Specialization
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There are 6 modules in this course

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.

What's included

7 videos7 readings1 quiz1 programming assignment

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.

What's included

5 videos1 reading1 quiz

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.

What's included

15 videos6 readings3 quizzes1 programming assignment1 plugin

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!

What's included

20 videos4 readings2 quizzes1 programming assignment

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.

What's included

7 videos2 readings1 quiz

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.

What's included

4 videos2 readings1 quiz1 programming assignment

Instructors

Instructor ratings
4.5 (674 ratings)
Neil Rhodes
University of California San Diego
7 Courses690,430 learners
Daniel M Kane
University of California San Diego
5 Courses673,035 learners
Michael Levin
University of California San Diego
7 Courses710,443 learners

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