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Data Structures (DS) are ways of organizing and storing data efficiently to perform operations effectively. Common types include: Arrays – Fixed-size, sequentially stored elements. Linked Lists – Dynamic memory allocation, elements linked via pointers. Stacks – Last-In-First-Out (LIFO) structure. Queues – First-In-First-Out (FIFO) structure. Trees – Hierarchical structures with nodes. Graphs – Nodes and edges representing complex relationships. Hash Tables – Key-value pair storage with fast lookups.
Introduction to DSA
Understanding Data
Structures & Algorithms Data Structures:
Algorithms:
Algorithms are step-by-step procedures for solving computational problems. Types of algorithms include: Sorting (QuickSort, MergeSort, Bubble Sort, etc.) Searching (Binary Search, Linear Search, etc.) Graph Algorithms (DFS, BFS, Dijkstra's Algorithm, etc.) Dynamic Programming (Knapsack, Fibonacci, etc.) Divide & Conquer (MergeSort, QuickSort, etc.)
Big-O Notation (Time & Space
Complexity)
Big-O Notation expresses the worst-case scenario of an algorithm's performance in terms of input size n: O(1) – Constant Time: Execution time does not depend on input size. O(log n) – Logarithmic Time: Performance grows logarithmically (e.g., Binary Search). O(n) – Linear Time: Performance increases proportionally to input size. O(n log n) – Linearithmic Time: Efficient sorting algorithms (MergeSort, QuickSort) fall here. O(n^2) – Quadratic Time: Nested loops (e.g., Bubble Sort, Insertion Sort). O(2^n) – Exponential Time: Recursive problems (e.g., Fibonacci via naive recursion).
Graphical Representation of Time Complexity:
Summary for Quick Revision
DSA is crucial for coding interviews and optimizing software performance. Common Data Structures: Arrays, Linked Lists, Stacks, Queues, Trees, Graphs, Hash Tables. Algorithm Complexity: Big-O notation helps analyze efficiency. Best Practices: Read problems carefully, optimize code, and choose appropriate data structures. Example Problem: First non-repeating character in a string with an optimized O(n) solution. By mastering these fundamentals and practicing regularly, you can significantly improve problem-solving skills and crack technical interviews efficiently.
