Data Structures and Algorithms in JavaScript

Struggling with slow features, tricky edge cases, or interview questions that seem to twist your brain into knots? Turn your JavaScript skills into high-performance solutions with a resource that connects theory to real-world code. Learn how to reason about trade-offs, pick optimal approaches, and ship faster, more reliable software.

Write Efficient Code and Solve Real Problems Using JavaScript

Overview

This IT book and programming guide delivers a practical, JavaScript-first path to mastering Data Structures and Algorithms in JavaScript so you can Write Efficient Code and Solve Real Problems Using JavaScript across both front-end and back-end projects. It thoroughly covers Arrays and built-in methods, Objects and hash maps, Sets and WeakSets, Stacks and queues implementation, Linked lists in JavaScript, Binary trees and tree traversal, Tries and prefix trees, Graph algorithms and traversal, Heaps and priority queues, Binary search implementations, Sorting algorithms comparison, Recursion and divide conquer, and Dynamic programming introduction—implemented with modern ES6+ patterns that feel natural in real apps. You’ll also practice Time complexity analysis, Space complexity optimization, Interview coding strategies, LeetCode problem solving, JavaScript performance optimization, Algorithmic thinking development, and Data structure selection criteria—making it a technical book you’ll reference throughout your career.

Who This Book Is For

• Working JavaScript developers who want to boost performance and reliability. Learn to refactor bottlenecks, choose the right structure for the job, and translate theory into production-ready code you can deploy with confidence.
• Students and bootcamp graduates aiming to connect classroom concepts to idiomatic ES6+. Build a solid foundation in patterns, complexity, and implementation details that map directly to interview success and day-to-day engineering.
• Career switchers and interview candidates ready to level up fast. Follow a focused path of challenges and explanations that help you think clearly under pressure and present clean, optimized solutions.

Key Lessons and Takeaways

• Make smart trade-offs with data structure selection criteria. Understand when arrays outperform linked lists, how sets differ from maps, and why heaps or tries can dramatically simplify problems that seem complex at first glance.
• Apply algorithmic patterns that show up everywhere. Master binary search implementations, recursion and divide-and-conquer strategies, and a dynamic programming introduction that makes tough problems approachable and repeatable.
• Analyze, measure, and optimize your code. Practice time complexity analysis and space complexity optimization, then translate those insights into faster JavaScript using native methods, clear abstractions, and solid testing.

Why You’ll Love This Book

You get step-by-step guidance that stays grounded in the realities of JavaScript—arrays that are more than lists, objects used as hash maps, and ES6+ features that make clean implementations possible. Each topic includes concise explanations, diagrams-in-words, and practical examples tied to everyday scenarios like autocomplete with tries, scheduling with heaps, or traversal with BFS/DFS. With 17 chapters, 5 appendices, a handy cheatsheet, a JavaScript syntax refresher, a glossary, and challenge problems with detailed solutions, it’s both a learning path and a long-term reference.

How to Get the Most Out of It

1. Follow a progressive path: start with arrays, objects, and built-ins; then move to sets, stacks, and queues; continue with linked lists; and finish with trees, tries, graphs, and heaps before tackling algorithms like sorting, binary search, recursion, and dynamic programming. Alternate reading with practice problems for maximum retention.
2. Apply concepts to real code as you learn. Refactor a slow feature by switching to a map-based cache, replace nested loops with a set lookup, or use BFS/DFS to traverse app state. Annotate each solution with Big-O and measure gains with Performance APIs or simple timing utilities.
3. Build mini-projects that cement understanding. Implement an autocomplete using a prefix tree, write a task scheduler with a priority queue (heap), or model routes with a graph and run traversals. Pair these projects with 2–3 LeetCode problems per topic to reinforce interview readiness.

Get Your Copy

Upgrade your problem-solving toolkit, accelerate your apps, and walk into interviews with strategies that work under pressure. Start learning patterns that scale from coding challenges to production systems.

👉 Get your copy now