DSA Mastery Series

Most DSA content online is written for people cramming before an interview: memorize the pattern, solve a few LeetCode problems, then forget it a week later.

This series is built differently.

Instead of treating DSA as interview trivia, every post focuses on three things:

• Implementing the data structure or algorithm from scratch in both Python and Node.js

• Showing where it appears in production systems, not just coding interview questions

• Backing every complexity claim with actual benchmark results instead of stopping at theoretical Big-O notation

The roadmap

The topics are ordered by dependency, not alphabetically.

We start with complexity analysis, because everything else depends on understanding how algorithms scale. Then we move through linear data structures, searching and sorting, non-linear structures, graphs, and finally the algorithmic techniques that usually cause the most trouble in interviews and system design.

1. Big-O / Complexity Analysis

2. Arrays & Strings

3. Recursion Fundamentals

4. Linked Lists

5. Stacks & Queues

6. Searching (Linear & Binary Search)

7. Sorting (Merge Sort, Quick Sort, Timsort vs V8's sort())

8. Hash Maps

9. Trees I — BST + Traversals

10. Heaps / Priority Queues

11. Tries

12. Graphs I — Representation + BFS/DFS

13. Graphs II — Dijkstra & Shortest Paths

14. Greedy Algorithms

15. Backtracking

16. Dynamic Programming I — Foundations

17. Dynamic Programming II — Production Problems

18. Advanced Structures — Union-Find & Segment Trees

Note: Arrays and Linked Lists may each become two posts (Fundamentals + Patterns), so the final series will probably end up with more than 18 articles.

Every article includes:

• Python and Node.js implementations

• Time and space complexity for every operation

• Benchmark results on real hardware

• Production use cases

• Sandbox-tested code you can run yourself