You have the data structures (the ingredients); now you need the Algorithms (the recipes) to manipulate them. This course is about solving problems efficiently. It's not enough to solve the problem; can you solve it before the sun explodes? We study how to measure speed (Time Complexity) and standard strategies (Divide & Conquer, Greedy, DP) to tackle the hardest problems in CS.
Google Maps finding the shortest path using Dijkstra's algorithm.
How Amazon sorts millions of products by "Price: Low to High" instantly.
Using Dynamic Programming to align gene sequences efficiently.
What: Covers Asymptotic Notation (Big-O), Recurrences (Master Theorem), and Amortized Analysis.
Why: You need a ruler to measure code. Big-O gives us a universal language to discuss performance.
What: Covers Merge Sort, Quick Sort, Heap Sort, Binary Search, and Linear Sorting.
Why: Sorting is the most fundamental operation. Quick Sort is the industry standard.
What: Covers Huffman Coding, Activity Selection, Union-Find, Prim's, and Kruskal's.
Why: Sometimes being "Greedy" (taking the best immediate option) works. Huffman coding compresses your ZIP files.
What: Covers Dijkstra's Algorithm, Bellman-Ford, and DAG shortest paths.
Why: Finding the shortest path is crucial for GPS, routing internet packets, and social networks.
What: Covers Knapsack, LCS, Matrix Chain Multiplication, and Floyd-Warshall.
Why: DP teaches you to cache results of sub-problems so you never solve the same thing twice. Pure optimization magic.
What: Covers P vs NP, Polynomial Reductions, and NP-Hard problems.
Why: Knowing what can't be solved efficiently prevents you from wasting years on impossible problems.
What: Algorithm Design Challenges and GATE-style problems.
Why: Practice helps algorithmic patterns (Greedy vs DP) become intuitive.