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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, Aris Markogiannakis, Daniel Ostrovsky
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Learning JavaScript Data Structures and Algorithms

Learning JavaScript Data Structures and Algorithms

By: Loiane Groner, Aris Markogiannakis, Daniel Ostrovsky

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

Big O time complexities

Big O notation uses capital O to denote upper bound. It signifies that the actual running time could be less than but not greater than what the function expresses. It does not tell us the exact running time of an algorithm. Instead, it tells us how bad things could get as the input size grows large.

Imagine you have a messy room and need to find a specific sock. In the worst case, you have to check each item of clothing one by one (this is like a linear time algorithm). Big O tells you that even if your room gets super messy, you will not need to look at more items than are actually there. You might get lucky and find the sock quickly! The actual time might be much less than the Big O prediction.

When analyzing algorithms, the following classifications of time and space complexities are most encountered:

Notation Name Explanation
O(1) Constant The algorithm's runtime or space usage remains the same regardless of the input size (n).
O(log(n)) Logarithmic...
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Learning JavaScript Data Structures and Algorithms
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