À propos de ce cours : 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!
Les destinataires de ce cours : 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.
Créé par : Université de Californie, San Diego, Université nationale de recherche, École des hautes études en sciences économiques
Enseigné par : Alexander S. Kulikov, Visiting Professor
Department of Computer Science and Engineering
Enseigné par : Michael Levin, Lecturer
Computer Science
Enseigné par : Daniel M Kane, Assistant Professor
Department of Computer Science and Engineering / Department of Mathematics
Enseigné par : Neil Rhodes, Adjunct Faculty
Computer Science and Engineering
| Informations de base | Cours 2 de 6 dans Data Structures and Algorithms Specialization |
| Niveau | Intermédiaire |
| Engagement | 4 weeks of study, 5-10 hours/week |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 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 vidéos, 7 lectures, 1 quiz pour s'exercer
- Leyendo: Welcome
- Vidéo: Singly-Linked Lists
- Vidéo: Doubly-Linked Lists
- Leyendo: Slides and External References
- Vidéo: Stacks
- Vidéo: Queues
- Leyendo: Slides and External References
- Vidéo: Trees
- Vidéo: Tree Traversal
- Leyendo: Slides and External References
- Cuestionario de práctica: Basic Data Structures
- Leyendo: Available Programming Languages
- Leyendo: FAQ on Programming Assignments
- Leyendo: Acknowledgements
Noté: Programming Assignment 1: Basic Data Structures
SEMAINE 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 vidéos, 1 lecture
- Vidéo: Amortized Analysis: Aggregate Method
- Vidéo: Amortized Analysis: Banker's Method
- Vidéo: Amortized Analysis: Physicist's Method
- Vidéo: Amortized Analysis: Summary
- Leyendo: Slides and External References
Noté: Dynamic Arrays and Amortized Analysis
SEMAINE 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 vidéos, 6 lectures, 1 quiz pour s'exercer
- Vidéo: Naive Implementations of Priority Queues
- Leyendo: Slides
- Vidéo: Binary Trees
- Leyendo: Tree Height Remark
- Vidéo: Basic Operations
- Vidéo: Complete Binary Trees
- Vidéo: Pseudocode
- Leyendo: Slides and External References
- Vidéo: Heap Sort
- Vidéo: Building a Heap
- Vidéo: Final Remarks
- Leyendo: Slides and External References
- Vidéo: Overview
- Vidéo: Naive Implementations
- Leyendo: Slides and External References
- Vidéo: Trees for Disjoint Sets
- Vidéo: Union by Rank
- Vidéo: Path Compression
- Vidéo: Analysis (Optional)
- Leyendo: Slides and External References
- Cuestionario de práctica: Priority Queues and Disjoint Sets
Noté: Priority Queues: Quiz
Noté: Quiz: Disjoint Sets
Noté: Programming Assignment 2: Priority Queues and Disjoint Sets
SEMAINE 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 vidéos, 4 lectures, 1 quiz pour s'exercer
- Vidéo: Analysing Service Access Logs
- Vidéo: Direct Addressing
- Vidéo: List-based Mapping
- Vidéo: Hash Functions
- Vidéo: Chaining Scheme
- Vidéo: Chaining Implementation and Analysis
- Vidéo: Hash Tables
- Leyendo: Slides and External References
- Vidéo: Phone Book Problem
- Vidéo: Phone Book Problem - Continued
- Vidéo: Universal Family
- Vidéo: Hashing Integers
- Vidéo: Proof: Upper Bound for Chain Length (Optional)
- Vidéo: Proof: Universal Family for Integers (Optional)
- Vidéo: Hashing Strings
- Vidéo: Hashing Strings - Cardinality Fix
- Leyendo: Slides and External References
- Vidéo: Search Pattern in Text
- Vidéo: Rabin-Karp's Algorithm
- Vidéo: Optimization: Precomputation
- Vidéo: Optimization: Implementation and Analysis
- Leyendo: Slides and External References
- Vidéo: Instant Uploads and Storage Optimization in Dropbox
- Vidéo: Distributed Hash Tables
- Leyendo: Slides and External References
- Cuestionario de práctica: Hashing
Noté: Hash Tables and Hash Functions
Noté: Programming Assignment 3: Hash Tables
SEMAINE 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 vidéos, 2 lectures, 1 quiz pour s'exercer
- Vidéo: Search Trees
- Vidéo: Basic Operations
- Vidéo: Balance
- Leyendo: Slides and External References
- Vidéo: AVL Trees
- Vidéo: AVL Tree Implementation
- Vidéo: Split and Merge
- Leyendo: Slides and External References
- Cuestionario de práctica: Binary Search Trees
SEMAINE 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 vidéos, 2 lectures, 1 quiz pour s'exercer
- Leyendo: Slides and External References
- Vidéo: Splay Trees: Introduction
- Vidéo: Splay Trees: Implementation
- Vidéo: (Optional) Splay Trees: Analysis
- Leyendo: Slides and External References
- Cuestionario de práctica: Splay Trees
Noté: Programming Assignment 4: Binary Search Trees
FAQ
Comment cela fonctionne
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Créateurs
Université de Californie, 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.
Université nationale de recherche, École des hautes études en sciences économiques
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
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
Some more assignments would have been better.
Nice introductory course to data structures
Good introduction to data structures - with a focus on the implementation rather than using premade libraries.
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quite tough!! but enjoyed learning