This course provides a strong foundation in the basic concepts of algorithm design and analysis, along with standard data structures, using Python as the primary implementation language.

Lectures-Tutorials-Practicals-Credits: 4-0-0-4

Week 1: Python recap; Jupyter/Colab; Exception handling; OOP; Code implementation and efficiency.

Week 2: Algorithm analysis; Complexity; Searching and sorting (selection, insertion, merge); Implementation.

Week 3: Quicksort - analysis and implementation; Comparison of sorting algorithms; Lists vs arrays; Implementation of lists and dictionaries; Stack and queue.

Week 4: Graphs and representations; Breadth-first and depth-first search; Applications of BFS and DFS; Directed acyclic graphs; Topological sorting; Longest path.

Week 5: Shortest paths (Dijkstra, Bellman-Ford, Floyd-Warshall); Minimum spanning trees (Prim's, Kruskal's).

Week 6: Union-Find data structure; Priority queues and heaps; Heaps in algorithms; Search trees.

Week 7: Balanced search trees; Greedy algorithms - interval scheduling, minimizing lateness, Huffman coding.

Week 8: Divide and conquer - counting inversions, closest pair of points, integer multiplication; Recursion trees; Selection algorithms.

Week 9: Dynamic programming - memoization; Applications - grid paths, subsequences, edit distance, matrix multiplication.

Week 10: String matching algorithms - Boyer-Moore, Rabin-Karp, and KMP algorithm; Automata-based string matching; Regular expressions.

Week 11: Linear programming and applications; Network flows; Reductions; Intractability (P vs NP).

Week 12: Course Summary.