Data Structures Series: A Structured Roadmap with JavaScript

Source: Dev.to

Welcome to the Data Structures Series, a structured, JavaScript‑powered breakdown of the core data structures every developer should know. Whether you’re preparing for interviews, brushing up fundamentals, or leveling up as a software engineer, this series is designed to be:

• Clear — no fluff, just clean explanations.
• Practical — real‑world relevance, not just theory.
• Code‑driven — every concept backed by JavaScript examples.
• Structured — each part builds on the previous one.
• Consistent — two posts per week (Monday & Thursday).

This overview acts as your hub, table of contents, and roadmap. Bookmark it — every future post will link back here.

What You Can Expect from This Series

The series is broken into six major parts, each covering a core data structure family. You’ll learn:

• How each structure works
• Where it’s used in real software
• How to implement it in JavaScript
• Common patterns and mistakes
• Interview‑style applications

Posts are published incrementally as each part is completed.

Series Parts

PART 1 — TREES

Trees are foundational to many systems — the DOM, file systems, compilers, databases, and more.

1. 1.1 Trees Fundamentals (published)
2. 1.2 Tree Traversals (DFS/BFS)
3. 1.3 Binary Search Trees (BST Intro)
4. 1.4 BST Operations — Insert, Search, Delete
5. 1.5 AVL Trees (Balancing Logic)
6. 1.6 Red‑Black Trees (Color & Rotation Rules)
7. 1.7 N‑Ary Trees
8. 1.8 Tries (Prefix Trees)
9. 1.9 Heap / Priority Queue (as Trees)
10. 1.10 Interview‑style Tree Patterns & Templates

PART 2 — HASHING & MAPS

One of the most important foundations in all of programming.

• 2.1 Hash Tables — Intro
• 2.2 Hash Functions — Good vs Bad
• 2.3 Collision Strategies
• 2.4 Implementing a HashMap in JS
• 2.5 Map vs Object vs Dictionary
• 2.6 Interview‑style Hashing Patterns

PART 3 — LINKED LISTS

A staple in interviews, pointer problems, and memory‑based structures.

• 3.1 Singly Linked List — Intro
• 3.2 Doubly & Circular Linked Lists
• 3.3 Fast/Slow Pointer Technique
• 3.4 Linked List Implementation in JS
• 3.5 Interview‑style LL Patterns

PART 4 — STACKS & QUEUES

Core structures behind algorithms, parsing, UI rendering, and more.

• 4.1 Stacks — LIFO Explained
• 4.2 Queues — FIFO, Priority Queue
• 4.3 Implementing Stacks & Queues in JS
• 4.4 Monotonic Stack (Interview Applications)
• 4.5 Interview‑style Patterns

PART 5 — GRAPHS

One of the most powerful and flexible data structures in computer science.

• 5.1 Graph Basics
• 5.2 Representations (Adjacency List / Matrix)
• 5.3 BFS Deep Dive
• 5.4 DFS Deep Dive
• 5.5 Directed vs Undirected Graphs
• 5.6 Weighted Graphs
• 5.7 Dijkstra
• 5.8 A* Pathfinding
• 5.9 Topological Sort
• 5.10 Interview‑style Graph Patterns

PART 6 — ARRAYS & STRINGS

The category where most interview questions live.

• 6.1 Sliding Window
• 6.2 Two Pointers
• 6.3 Prefix Sums
• 6.4 Difference Arrays
• 6.5 Searching Techniques
• 6.6 Sorting Algorithms Overview
• 6.7 String Algorithms
• 6.8 Subarray Patterns
• 6.9 Kadane’s Algorithm
• 6.10 Interview‑style Best Array/String Problems

How to Follow the Series

• Posts drop Monday and Thursday.
• All code examples use JavaScript.
• Each part can be read independently.
• Reading in order builds the strongest understanding.

You can:

• Bookmark this overview
• Comment questions you want answered
• Suggest topics for deeper dives

Why This Series Exists

I’ve learned these concepts through:

• Courses
• Senior engineers
• Papers & articles
• Real‑world debugging
• Interview preparation

This series is my attempt to make the path clearer for the next person: clean explanations, practical code, developer‑focused learning.

Stay Tuned — First Posts Dropping Soon

Follow me to get each part as it releases.

Next post: Part 1.1 — Tree Fundamentals

Let’s master data structures together.