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, your table of contents, and your roadmap. Bookmark it — every future post will link back here.
What You Can Expect from This Series
This 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
Published incrementally as each part is completed.
PART 1 — TREES
Trees are foundational to many systems — the DOM, file systems, compilers, databases, and more.
1.1 Trees Fundamentals (published)
1.2 Tree Traversals (DFS/BFS)
1.3 Binary Search Trees (BST Intro)
1.4 BST Operations — Insert, Search, Delete
1.5 AVL Trees (Balancing Logic)
1.6 Red-Black Trees (Color & Rotation Rules)
1.7 N-Ary Trees
1.8 Tries (Prefix Trees)
1.9 Heap / Priority Queue (as Trees)
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.
- Following 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.
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