Data Structures & Algorithms
Data Structures and Algorithms (DSA) form the foundation of computer science and software engineering. Understanding these concepts is essential for:
- Writing efficient code
- Passing technical interviews
- Solving complex problems
- Building scalable systems
Track Your Progress
Mark topics as completed as you work through the curriculum. Your progress is saved automatically.
Roadmap
Follow this recommended learning path:
Phase 1: Foundations (Week 1-2)
- Big O Notation - Analyze algorithm efficiency
- Time Complexity - Measure execution time
- Space Complexity - Measure memory usage
Phase 2: Linear Data Structures (Week 3-5)
- Arrays - Sequential storage, two pointers, sliding window
- Strings - Text manipulation, pattern matching
- Linked Lists - Dynamic node-based storage
- Stacks - LIFO operations, monotonic stacks
- Queues - FIFO operations, priority queues
Phase 3: Non-Linear Data Structures (Week 6-8)
- Hash Tables - Key-value storage, O(1) lookups
- Trees - Hierarchical data, BST, traversals
- Heaps - Priority operations, top-k problems
- Graphs - Networks, paths, connectivity
Phase 4: Algorithms (Week 9-12)
- Sorting - Bubble, merge, quick, heap sort
- Searching - Binary search and variations
- Recursion - Divide and conquer
- Backtracking - Exhaustive search with pruning
- Dynamic Programming - Optimal substructure
- Greedy - Local optimal choices
Algorithm Pattern Matcher
Describe your problem to instantly identify the right algorithmic pattern. Try typing “find pair with target sum” or “shortest path in grid”.
Complexity Cheatsheet
| Data Structure | Access | Search | Insert | Delete |
|---|---|---|---|---|
| Array | O(1) | O(n) | O(n) | O(n) |
| Linked List | O(n) | O(n) | O(1)* | O(1)* |
| Stack | O(n) | O(n) | O(1) | O(1) |
| Queue | O(n) | O(n) | O(1) | O(1) |
| Hash Table | - | O(1)* | O(1)* | O(1)* |
| BST | O(log n)* | O(log n)* | O(log n)* | O(log n)* |
| Heap | - | O(n) | O(log n) | O(log n) |
*Average case. Worst case may differ. Linked list insert/delete is O(1) only with a reference to the node; O(n) to find the position first.
| Algorithm | Best | Average | Worst | Space |
|---|---|---|---|---|
| Bubble Sort | O(n) | O(n²) | O(n²) | O(1) |
| Merge Sort | O(n log n) | O(n log n) | O(n log n) | O(n) |
| Quick Sort | O(n log n) | O(n log n) | O(n²) | O(log n) |
| Binary Search | O(1) | O(log n) | O(log n) | O(1) |
| BFS/DFS | O(V+E) | O(V+E) | O(V+E) | O(V) |
Knowledge Check
Test your understanding of DSA fundamentals.
How to Study DSA
- Understand the concept - Don’t just memorize, understand WHY
- Visualize - Use our interactive visualizers to see algorithms in action
- Implement from scratch - Code it yourself without looking
- Analyze complexity - Always calculate Big O
- Practice problems - Apply concepts to real problems
- Review and repeat - Spaced repetition helps retention
Ready to Start?
Big O Notation Begin with understanding how to measure algorithm efficiency
Algorithm Pattern Matcher Learn the 15 essential patterns for solving coding problems