// subject deep-dive
Algorithms
Understand complexity analysis and fundamental algorithm design techniques.
📊 Average Weightage: 8–10 Marks (4–6 Questions)
Complexity Analysis
Big-O notation, worst-case time/space complexity, and analyzing algorithmic efficiency.
Searching & Sorting
Binary search and sorting algorithms like Quicksort, Mergesort, and Heapsort (including analysis).
Algorithm Design Techniques
Greedy algorithms (MST, activity selection), Dynamic Programming (Knapsack, LCS), and Divide-and-Conquer (Master theorem).
Graph Algorithms
DFS/BFS traversals, shortest paths (Dijkstra), and minimum spanning trees (MST) using Prim's and Kruskal's.
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High-Yield Topics
Graph algorithms (especially MST and shortest path) and design techniques like Dynamic Programming and Greedy methods are heavily emphasized.