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  • Book Overview & Buying Learning JavaScript Data Structures and Algorithms
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Learning JavaScript Data Structures and Algorithms

Learning JavaScript Data Structures and Algorithms - Fourth Edition

By : Loiane Groner
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Learning JavaScript Data Structures and Algorithms

Learning JavaScript Data Structures and Algorithms

By: Loiane Groner

Overview of this book

Data structures and algorithms are foundational topics for software developers. This easy-to-follow book from experienced developer and trainer Loiane Groner will help you to fill in the gaps in your knowledge – whether you’re a self-taught developer, you’re preparing for technical interviews, or you just want to write better code and improve your problem-solving skills. This fourth edition covers essential data structures, algorithms, and their usage in the context of JavaScript. You’ll follow examples in both JavaScript and TypeScript, in line with the latest standards and best practices, learning how to do complexity analysis along the way. New to this edition are LeetCode and HackerRank exercises at the end of each chapter, which you'll be guided through solving. You’ll also find brand-new chapters on the tries data structure, and string and math algorithms. By the end of the book, you will know how to develop programs using the best data structures and algorithms for the job.
Table of Contents (9 chapters)
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1
Learning JavaScript Data Structures and Algorithms, Fourth Edition: Enhance your problem-solving skills in JavaScript and TypeScript
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Learning JavaScript Data Structures and Algorithms, Fourth Edition: Enhance your problem-solving skills in JavaScript and TypeScript
2
1 Introducing Data Structures and Algorithms in JavaScript
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1 Introducing Data Structures and Algorithms in JavaScript
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The importance of data structures
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Why algorithms matter
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Why companies ask for these concepts during interviews
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Why choose JavaScript to learn data structures and algorithms?
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Setting up the environment
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JavaScript Fundamentals
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TypeScript fundamentals
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Summary
3
2 Big O notation
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2 Big O notation
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Understanding Big O notation
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Big O time complexities
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Space complexity
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Calculating the complexity of an algorithm
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Big O notation and tech interviews
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Exercises
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Summary
4
3 Arrays
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3 Arrays
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Why should we use arrays?
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Creating and initializing arrays
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Accessing elements and iterating an array
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Adding elements
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Removing elements
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Adding and removing elements from a specific position
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Iterator methods
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Searching an array
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Sorting elements
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Transforming an array
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References for other JavaScript array methods
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Two-dimensional arrays
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Multi-dimensional arrays
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The TypedArray class
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Arrays in TypeScript
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Creating a simple TODO list using arrays
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Exercises
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Summary
5
4 Stacks
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4 Stacks
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The stack data structure
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Creating an array-based Stack class
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Creating a JavaScript object-based Stack class
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Creating the Stack class using TypeScript
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Solving problems using stacks
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Exercises
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Summary
6
5 Queues and Deques
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5 Queues and Deques
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The queue data structure
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Creating the Queue class
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The deque data structure
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Creating the Deque class
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Creating the Queue and Deque classes in TypeScript
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Solving problems using queues and deques
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Exercises
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Summary
7
6 Linked Lists
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6 Linked Lists
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The linked list data structure
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Creating the LinkedList class
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Doubly linked lists
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Circular linked lists
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Creating a media player using a linked list
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Reviewing the efficiency of the linked lists
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Exercises
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Summary
8
7 Sets
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7 Sets
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The set data structure
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Creating the MySet class
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Performing mathematical operations with a set
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The JavaScript Set class
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Reviewing the efficiency of sets
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Exercises
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Summary
9
8 Dictionaries and Hashes
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8 Dictionaries and Hashes
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The dictionary data structure
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Creating the Dictionary class
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The JavaScript Map class
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The hash table data structure
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Creating the HashTable class
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The hash set data structure
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Maps and TypeScript
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Reviewing the efficiency of maps and hash maps
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Exercises
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Summary

Space complexity

Space complexity refers to the amount of memory (or space) an algorithm uses to solve a problem. It is a measure of how much additional storage the algorithm requires beyond the space occupied by the input data itself.

It is important to understand space complexity as real-world computers have finite memory. If the algorithm's space complexity is too high, it might run out of memory on large datasets. And even if we have plenty of memory, an algorithm with a high space complexity can still be slower due to factors like increased memory access times and cache issues. Also, it is all about tradeoffs. Sometimes, we might choose an algorithm with a slightly higher space complexity if it offers a significant improvement in time complexity. This of course, needs to be reviewed case by case.

Big O notation works for space complexity just like it does for time complexity. It expresses the upper bound of how the algorithm's memory usage grows as the input size increases...

CONTINUE READING
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Tech Concepts
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Programming languages
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Learning JavaScript Data Structures and Algorithms
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