Data Structures & Algorithms
A complete reference guide with C++ and Java code examples, pattern recognition strategies, and interview tips.
What is DSA?
Data Structures organize and store data efficiently. Algorithms are step-by-step procedures to solve problems.
Together they form the foundation of software engineering and technical interviews.
Why Learn DSA?
- Solve complex problems efficiently
- Crack top-tier tech interviews (FAANG, startups)
- Write optimized, scalable code
- Think like a computer scientist
How to Use This Guide
Each section covers a topic with:
- Concept explanation
- C++ implementation
- Java implementation
- Pattern recognition — how to identify when to use it
- Classic problems to practice
Roadmap
Complexity Analysis
↓
Arrays → Strings
↓
Recursion → Backtracking
↓
Searching → Sorting
↓
Linked List → Stack → Queue → Hashing
↓
Trees → Heap → Trie
↓
Graphs
↓
Dynamic Programming → Greedy
↓
Bit Manipulation → Advanced Topics
↓
Pattern Recognition (Interview Guide)
Complexity Quick Reference
| Complexity | Name | Example |
|---|---|---|
| O(1) | Constant | Array access |
| O(log n) | Logarithmic | Binary search |
| O(n) | Linear | Linear scan |
| O(n log n) | Linearithmic | Merge sort |
| O(n²) | Quadratic | Bubble sort |
| O(2ⁿ) | Exponential | Fibonacci recursive |
| O(n!) | Factorial | Permutations |
Interview Tips
- Always clarify constraints before coding
- State your approach before writing code
- Think about edge cases: empty input, single element, all same
- Start with brute force, then optimize
- Communicate your time and space complexity
Start from the Big-O & Complexity Analysis section in the sidebar.