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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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Learning JavaScript Data Structures and Algorithms, Fourth Edition: Enhance your problem-solving skills in JavaScript and TypeScript

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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