Data Structures

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About this course: 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 …

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There are no frequently asked questions yet. Send an Email to info@springest.com

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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!

Who is this class for: Programmers with basic experience looking to understand the practical and conceptual underpinnings of algorithms, with the goal of becoming more effective software engineers. Computer science students and researchers as well as interdisciplinary students (studying electrical engineering, mathematics, bioinformatics, etc.) aiming to get more profound understanding of algorithms and hands-on experience implementing them and applying for real-world problems. Applicants who want to prepare for an interview in a high-tech company.

Created by:  University of California, San Diego, Higher School of Economics
  • Taught by:  Alexander S. Kulikov, Visiting Professor

    Department of Computer Science and Engineering
  • Taught by:  Michael Levin, Lecturer

    Computer Science
  • Taught by:  Daniel M Kane, Assistant Professor

    Department of Computer Science and Engineering / Department of Mathematics
  • Taught by:  Neil Rhodes, Adjunct Faculty

    Computer Science and Engineering
Basic Info Course 2 of 6 in the Data Structures and Algorithms Specialization Level Intermediate Commitment 4 weeks of study, 5-10 hours/week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.6 stars Average User Rating 4.6See what learners said 课程作业

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Syllabus


WEEK 1


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, 7 readings, 1 practice quiz expand


  1. 阅读: Welcome
  2. Video: Arrays
  3. Video: Singly-Linked Lists
  4. Video: Doubly-Linked Lists
  5. 阅读: Slides and External References
  6. Video: Stacks
  7. Video: Queues
  8. 阅读: Slides and External References
  9. Video: Trees
  10. Video: Tree Traversal
  11. 阅读: Slides and External References
  12. 练习测验: Basic Data Structures
  13. 阅读: Available Programming Languages
  14. 阅读: FAQ on Programming Assignments
  15. 阅读: Acknowledgements

Graded: Programming Assignment 1: Basic Data Structures

WEEK 2


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, 1 reading expand


  1. Video: Dynamic Arrays
  2. Video: Amortized Analysis: Aggregate Method
  3. Video: Amortized Analysis: Banker's Method
  4. Video: Amortized Analysis: Physicist's Method
  5. Video: Amortized Analysis: Summary
  6. 阅读: Slides and External References

Graded: Dynamic Arrays and Amortized Analysis

WEEK 3


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, 6 readings, 1 practice quiz expand


  1. Video: Introduction
  2. Video: Naive Implementations
  3. 阅读: Slides
  4. Video: Binary Trees
  5. 阅读: Tree Height Remark
  6. Video: Basic Operations
  7. Video: Complete Binary Trees
  8. Video: Pseudocode
  9. 阅读: Slides and External References
  10. Video: Heap Sort
  11. Video: Building a Heap
  12. Video: Final Remarks
  13. 阅读: Slides and External References
  14. Video: Overview
  15. Video: Naive Implementations
  16. 阅读: Slides and External References
  17. Video: Trees
  18. Video: Union by Rank
  19. Video: Path Compression
  20. Video: Analysis (Optional)
  21. 阅读: Slides and External References
  22. 练习测验: Priority Queues and Disjoint Sets

Graded: Priority Queues: Quiz
Graded: Quiz: Disjoint Sets
Graded: Programming Assignment 2: Priority Queues and Disjoint Sets

WEEK 4


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, 4 readings, 1 practice quiz expand


  1. Video: Applications of Hashing
  2. Video: Analysing Service Access Logs
  3. Video: Direct Addressing
  4. Video: List-based Mapping
  5. Video: Hash Functions
  6. Video: Chaining Scheme
  7. Video: Chaining Implementation and Analysis
  8. Video: Hash Tables
  9. 阅读: Slides and External References
  10. Video: Phone Book Problem
  11. Video: Phone Book Problem - Continued
  12. Video: Universal Family
  13. Video: Hashing Integers
  14. Video: Proof: Upper Bound for Chain Length (Optional)
  15. Video: Proof: Universal Family for Integers (Optional)
  16. Video: Hashing Strings
  17. Video: Hashing Strings - Cardinality Fix
  18. 阅读: Slides and External References
  19. Video: Search Pattern in Text
  20. Video: Rabin-Karp's Algorithm
  21. Video: Optimization: Precomputation
  22. Video: Optimization: Implementation and Analysis
  23. 阅读: Slides and External References
  24. Video: Instant Uploads and Storage Optimization in Dropbox
  25. Video: Distributed Hash Tables
  26. 阅读: Slides and External References
  27. 练习测验: Hashing

Graded: Hash Tables and Hash Functions
Graded: Programming Assignment 3: Hash Tables

WEEK 5


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, 2 readings, 1 practice quiz expand


  1. Video: Introduction
  2. Video: Search Trees
  3. Video: Basic Operations
  4. Video: Balance
  5. 阅读: Slides and External References
  6. Video: AVL Trees
  7. Video: AVL Tree Implementation
  8. Video: Split and Merge
  9. 阅读: Slides and External References
  10. 练习测验: Binary Search Trees


WEEK 6


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, 2 readings, 1 practice quiz expand


  1. Video: Applications
  2. 阅读: Slides and External References
  3. Video: Splay Trees: Introduction
  4. Video: Splay Trees: Implementation
  5. Video: (Optional) Splay Trees: Analysis
  6. 阅读: Slides and External References
  7. 练习测验: Splay Trees

Graded: Programming Assignment 4: Binary Search Trees
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