Data Structures are ways of organizing and storing data in a computer so it can be used efficiently. Examples include:
- Arrays: Sequential storage of elements.
- Linked Lists: Elements connected via pointers.
- Stacks and Queues: LIFO and FIFO data management.
- Trees: Hierarchical data storage (e.g., Binary Trees, Binary Search Trees).
- Graphs: Nodes and edges representing relationships.
- Hash Tables: Key-value pair storage for fast lookups.
Algorithms are step-by-step procedures or formulas for solving problems. Common types include:
- Searching Algorithms: E.g., Linear Search, Binary Search.
- Sorting Algorithms: E.g., Bubble Sort, Quick Sort, Merge Sort.
- Graph Algorithms: E.g., Dijkstra’s, BFS, DFS.
- Dynamic Programming: E.g., Fibonacci Sequence, Knapsack Problem.
- Divide and Conquer: E.g., Merge Sort, Quick Sort.
- Greedy Algorithms: E.g., Huffman Coding, Kruskal’s Algorithm.
Importance of DSA:
- Efficiency: Helps write optimized code.
- Scalability: Ensures solutions work well for large datasets.
- Problem Solving: Fundamental in competitive programming and software development.
- Foundation for Development: Essential for understanding advanced topics in computer science like databases, AI, and machine learning.
Mastering DSA is crucial for building efficient software and excelling in technical interviews.
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