Free online coursePython Data Structures and Algorithms: Learn Efficient Programming

Course Description

Python Data Structures and Algorithms: Learn Efficient Programming is a free online course from IIT Madras designed for learners who want to write cleaner, faster, and more scalable Python code. It builds a strong foundation in programming fundamentals while steadily shifting focus toward algorithmic thinking and performance-aware problem solving.

You will move from practical Python setup and coding workflows into core language concepts such as exceptions and classes, then learn how to measure runtime and understand why efficiency matters in real programs. From there, the course develops a rigorous approach to algorithm analysis, helping you compare growth rates, calculate complexity, and reason about tradeoffs when choosing solutions.

The curriculum explores classic searching and sorting techniques, including merge sort and quicksort, along with the reasoning used to analyze them. You will also study how common data structures are built and used in Python, including list-like structures and dictionaries, so you can select the right tool and understand what happens under the hood.

Graph fundamentals form a major component, covering representations, BFS and DFS, directed acyclic graphs, topological sorting, and paths in DAGs. You then progress to weighted graph problems such as shortest paths with Dijkstra and Bellman-Ford, all-pairs shortest paths with Floyd-Warshall, and spanning tree strategies with Prim and Kruskal.

To deepen your toolkit, the course includes union-find, priority queues, heaps, and search trees, followed by balanced trees and greedy methods for scheduling, lateness minimization, and Huffman coding. You will also work with divide-and-conquer ideas, quickselect, dynamic programming concepts such as memoization, grid paths, and classic string problems including edit distance.

Advanced topics introduce linear programming, network flows, reductions, and the fundamentals of intractability, along with comprehensive week summaries to consolidate learning. By the end, you will be better prepared to design efficient solutions and approach coding interviews, coursework, and real-world engineering tasks with stronger algorithmic confidence.