Data Structures & Algorithms

DSA knowledge is tested in coding interviews alongside system design and language depth. Understanding time and space complexity, knowing which data structure to reach for in a given scenario, and being able to implement common algorithms in Java are skills that distinguish candidates at competitive companies.

What You'll Find Here

Notes are being added. Planned topics:

Topic	Description
Complexity Analysis	Big-O notation; time vs. space trade-offs; amortized analysis.
Arrays & Strings	Two-pointer, sliding window, prefix sums — core array techniques.
Linked Lists	Singly/doubly linked; fast-slow pointer; reverse; detect cycle.
Stacks & Queues	Deque as stack/queue; monotonic stack pattern.
Trees & BST	Binary tree traversals (DFS/BFS); BST operations; balanced trees.
Heaps & Priority Queue	Min-heap, PriorityQueue<> in Java; k-th largest, merge k sorted.
Hash Maps & Sets	Hash collision; open addressing vs. chaining; frequency counting.
Graphs	BFS, DFS, topological sort, Dijkstra, union-find.
Sorting Algorithms	QuickSort, MergeSort, HeapSort; Arrays.sort (TimSort); stability.
Dynamic Programming	Memoization vs. tabulation; classic problems (LCS, 0/1 knapsack, coin change).

Learning Path

1. Complexity Analysis — you can't discuss solutions without understanding time/space trade-offs.
2. Arrays & Strings — two-pointer and sliding window patterns cover 30%+ of coding interview problems.
3. Hash Maps & Sets — constant-time lookup unlocks optimal solutions for frequency and grouping problems.
4. Trees — BFS/DFS traversal is tested in both tree and graph problems.
5. Dynamic Programming — the hardest category; start with memoization before tabulation.

Related Domains

• Collections Framework — Java's ArrayList, HashMap, PriorityQueue, and ArrayDeque implement these data structures.
• Functional Programming — streams offer declarative alternatives to imperative algorithms.
• System Design — algorithm choice affects system-level performance and scalability.