From some time, I’ve been passionate about understanding how real-world tech works — from AI agents to full-stack apps. But no matter how advanced things get, the core principles never change.
That’s why I’ve made a clear decision today: I’m starting my deep-dive journey into Data Structures and Algorithms (DSA). This isn’t just about cracking interviews — it’s about thinking better, solving problems smarter, and building efficient systems.
Why I’m Doing This
The more I explored advanced technologies, the more I realized that DSA is the foundation. Whether you’re working on backend services, AI pipelines, or any scalable system, your logic and structure come from DSA principles.
So after researching thoroughly, I’ve found a structured roadmap that breaks down the entire subject — step-by-step, beginner-friendly, yet going deep. I’m starting immediately.
My Learning Roadmap
Here’s a breakdown of the areas I’ll be covering:
1. Foundations & Warm-Up
- Programming Basics: functions, if-else, loops
- Practice problems: star patterns, palindromes, digit counting
- Reverse integers, second-largest number, nested loops
2. Time and Space Complexity
- Understanding Big O notation
- Analyzing code performance
- Comparing algorithms using time/space tradeoffs
3. Arrays
- Remove duplicates, reverse arrays, move zeros
- Stock buy/sell problem, missing number, single number
- Max consecutive ones, merge sorted arrays
4. Recursion
- Basics and pitfalls of recursion
- Factorials, power of two, sum problems
- Recursion masterclass and pure recursive strategies
5. Searching & Sorting
- Linear Search, Binary Search
- Bubble Sort, Selection Sort, Insertion Sort
- Merge Sort (Divide & Conquer)
6. Linked Lists
- Designing linked lists: insert, delete, reverse
- Detecting cycles (Hash Table & Floyd’s)
- Merge lists, rotate, swap pairs
- Advanced: palindrome check, intersection, odd-even rearrangement
7. Strings
- Palindromes (two-pointer & extra space)
- Anagrams, isomorphic strings
- Most frequent vowel/consonant
- String patterns, reverse string parts
- Longest common prefix, group anagrams
8. Stack and Queue Problems
- Implement stack using queues & vice versa
- Valid parentheses, min stack
- Evaluate Reverse Polish Notation
- Next greater elements, daily temperatures
- BFS simulation with “Rotting Oranges”
9. Binary Search Algorithms
- Square root of X
- Search in rotated sorted arrays
- Find first/last position of element
- Peak elements, first bad version
- Guess higher or lower problem
10. Two Pointers & Sliding Window
- Techniques for optimizing array/string problems
- Efficient subarray and pattern search solutions
11. Trees
- Core binary tree concepts, traversals
- Binary Search Trees, balanced trees
- Real-world tree use cases
12. Heaps, Graphs, and Tries
- Min/Max heaps, Priority Queues
- Graphs: adjacency list/matrix, traversal, Dijkstra’s
- Trie data structure for autocomplete, search
13. Backtracking, Greedy & Dynamic Programming
- Recursion-based backtracking
- Greedy strategy vs. dynamic programming
- Memoization, tabulation, coin change problem
Final Thoughts
I’ll be learning consistently, sharing what I learn, and applying these concepts in real projects. My goal isn’t just to finish — it’s to master.
If you’re learning DSA too, feel free to connect or follow my journey. Let’s grow together. 🚀
🖥️ Time to open the editor and start typing my first solution…
Thanks for reading!
— Ranjan