Data Structures & ALgorithms
Mahesh Kumar•
DSACPProgramming
Introduction
Data Structures and Algorithms (DSA) form the foundation of problem-solving, optimization, and system design.
They help developers write programs that are efficient, scalable, and reliable.
Arrays & Strings
- Index-based data storage
- Constant-time access
- Used in:
- Searching
- Sliding window problems
- Prefix / Suffix operations
int arr[5] = {1,2,3,4,5};
Recursion
- Solving problems by breaking them into subproblems
- Useful for:
- Tree traversal
- Backtracking
- Divide & Conquer
A function calling itself until a base condition is reached.
Linked Lists
- Dynamic memory structure
- Efficient insertions & deletions
- Types:
- Singly Linked List
- Doubly Linked List
- Circular List
Stacks & Queues
Stack — LIFO
- Function calls
- Undo operations
- Expression evaluation
Queue — FIFO
- Scheduling
- BFS traversal
- Buffer management
Trees
- Hierarchical data representation
- Used in:
- File systems
- Databases
- Compilers
Types
- Binary Tree
- Binary Search Tree
- AVL / Red-Black Tree
- Heap / Priority Queue
Graphs
- Networks of connected nodes
- Used in:
- Maps & navigation
- Social networks
- Recommendation systems
Traversal
- BFS — shortest path in unweighted graphs
- DFS — path exploration & backtracking
Dynamic Programming
- Solves problems through overlapping subproblems
- Improves performance using memoization / tabulation
Examples:
- Knapsack
- LIS
- Coin Change
- Fibonacci Optimization
Searching & Sorting
Sorting
- Bubble / Selection / Insertion
- Merge Sort
- Quick Sort
- Heap Sort
Searching
- Linear Search
- Binary Search (sorted arrays)
Time & Space Complexity
- Big-O notation expresses algorithm efficiency
Examples:
- O(1) — constant
- O(log n) — binary search
- O(n) — linear scan
- O(n log n) — sorting
- O(n²) — nested loops
Conclusion
DSA strengthens logical thinking, optimization, and problem-solving ability.
Mastering these concepts is essential for:
- coding interviews
- competitive programming
- scalable system design