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Data Structures & Algorithms in JavaScript 专项课程

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Packt

Data Structures & Algorithms in JavaScript 专项课程

Master Data Structures & Algorithms in JavaScript.

Learn to analyze complexity, solve Leetcode problems, and implement algorithms like sorting.

包含在 Coursera Plus

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推荐体验

4 周 完成
在 10 小时 一周
灵活的计划
自行安排学习进度
深入学习学科知识
中级 等级

推荐体验

4 周 完成
在 10 小时 一周
灵活的计划
自行安排学习进度

您将学到什么

  • Understand the principles of time and space complexity, Big O notation, and recursion.

  • Master core data structures like arrays, linked lists, stacks, and queues and their applications.

  • Gain practical experience with sorting algorithms such as bubble sort, quick sort, and merge sort.

  • Solve advanced algorithmic problems using dynamic programming, greedy algorithms, and graph traversal techniques.

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授课语言:英语(English)
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May 2026

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  • 借助实践项目精通一门科目或一个工具
  • 培养对关键概念的深入理解
  • 通过 Packt 获得职业证书

专业化 - 3门课程系列

Foundations of Data Structures and Algorithms in JavaScript

Foundations of Data Structures and Algorithms in JavaScript

第 1 门课程, 小时

您将学到什么

  • Understand time and space complexity to analyze algorithms.

  • Implement and manipulate arrays, linked lists, stacks, and hash tables in JavaScript.

  • Solve Leetcode problems using different data structures and algorithms.

  • Analyze and implement sorting algorithms, recursion, and more advanced topics.

Advanced Data Structures and Problem-Solving Techniques

Advanced Data Structures and Problem-Solving Techniques

第 2 门课程, 小时

您将学到什么

  • Implement advanced data structures such as binary search trees, heaps, and sliding window methods.

  • Solve complex algorithmic problems using efficient techniques like two-pointer and binary search.

  • Optimize problem-solving strategies for performance in coding challenges.

  • Master problem-solving methods essential for technical interviews and real-world applications.

Algorithms and Problem Solving with Advanced Techniques

Algorithms and Problem Solving with Advanced Techniques

第 3 门课程, 小时

您将学到什么

  • Learn how to solve complex problems using dynamic programming techniques.

  • Understand greedy algorithms and their application in optimization problems.

  • Master graph algorithms, including BFS, DFS, and shortest path techniques.

  • Gain expertise in bitwise operations to solve optimization and number-based problems.

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