Algorithmic Graph Theory & Data Structures Course | IIT Kharagpur NPTEL
Course Details
| Exam Registration | 1401 |
|---|---|
| Course Status | Ongoing |
| Course Type | Core |
| Language | English |
| Duration | 12 weeks |
| Categories | Computer Science and Engineering |
| Credit Points | 3 |
| Level | Undergraduate/Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 19 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Master Algorithmic Graph Theory & Data Structures: A Comprehensive 12-Week Course by IIT Kharagpur
Are you ready to delve deep into the mathematical heart of computer science? A new, comprehensive course on Algorithmic Graph Theory and Data Structures, offered by the prestigious Indian Institute of Technology Kharagpur, is now available for students and professionals worldwide. This 12-week program is designed to transform your understanding of how complex real-world problems are modeled and solved using elegant algorithmic principles.
Course Overview: Bridging Theory and Practice
This course provides a rigorous introduction to the mathematical modeling and algorithmic analysis of graph-based problems. It moves beyond basic data structure knowledge to focus on the core algorithms and efficient data structures that form the backbone of modern computing, from social network analysis to routing protocols and computational biology.
You will master key algorithmic paradigms including greedy methods, dynamic programming, and the use of augmented data structures. A significant emphasis is placed on analyzing algorithm performance through time and space complexity, equipping you with the critical skill to assess the efficiency and scalability of solutions. This course is the perfect bridge between theoretical computer science and practical implementation.
Learn from an Expert: Prof. Sourav Mukhopadhyay
The course is instructed by Prof. Sourav Mukhopadhyay, a distinguished Professor in the Department of Mathematics at IIT Kharagpur. With a prolific research career spanning cryptography, cloud computing, blockchain, and quantum cryptography, Prof. Mukhopadhyay brings a wealth of knowledge and a unique perspective to algorithmic theory. His guidance ensures the course content is both foundational and cutting-edge.
Who Should Enroll?
- Intended Audience: Undergraduate and Postgraduate students in Computer Science and Engineering.
- Prerequisites: A basic understanding of Data Structures, Graph Theory, and Probability is recommended.
- Industry Support: The curriculum is recognized by leading organizations including Stratign FZE (Dubai), SAG, DRDO, ISRO, WESEE, and NTRO, highlighting its relevance to real-world technological challenges.
Detailed 12-Week Course Curriculum
The course is meticulously structured over 12 weeks to build your expertise from the ground up:
| Week | Topics Covered |
|---|---|
| Week 1-3 | Algorithm Fundamentals, Analysis, Divide & Conquer (Merge Sort, Quicksort), Sorting (Heapsort), Order Statistics. |
| Week 4-7 | Advanced Data Structures: Hashing, Heaps, Binary Search Trees, Red-Black Trees, Augmented Structures (Interval Trees), van Emde Boas Trees, Amortized Analysis. |
| Week 8-10 | Graph Algorithms: Dynamic Programming, Graph Introduction, Minimum Spanning Tree (Prim, Kruskal), Graph Search (DFS, BFS), Shortest Path (Dijkstra, Bellman-Ford). |
| Week 11-12 | Advanced Graph Theory: All-Pairs Shortest Path (Floyd-Warshall), Network Flow (Ford-Fulkerson, Max-Flow Min-Cut), Computational Complexity. |
Key Learning Outcomes
- Design and implement efficient algorithms for complex graph problems.
- Perform rigorous time and space complexity analysis.
- Apply algorithmic paradigms like divide-and-conquer and dynamic programming effectively.
- Understand and implement advanced data structures like balanced trees and hash tables.
- Solve real-world problems modeled as networks using graph algorithms.
Essential Reference Materials
The course draws from authoritative texts to ensure depth and clarity:
- Graph Theory: by D.B. West, F. Harary, and R. Diestel.
- Algorithms: Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein (CLRS).
- Computational Geometry: by de Berg et al.
This course is more than just a syllabus; it's a stepping stone to advanced studies and research in algorithmic graph theory, network science, and computational complexity. Whether you aim to ace competitive coding interviews, contribute to open-source projects, or pursue academic research, the foundational skills imparted here are indispensable.
Enroll today and embark on a journey to master the algorithms that power our digital world.
Enroll Now →