Founded in 2024, the Wadhwani School of Data Science and AI (WSAI) is home to the Department of Data Science and AI (DSAI) — the 18th and newest department at IIT Madras. The department offers a B.Tech in AI and Data Analytics (AIDA), making IITM one of the very few institutions in India to offer a dedicated undergraduate AI degree through the JEE Advanced and JoSAA route. The first batch joined in 2024 and has now completed two years on campus.
What is AIDA?
AIDA is an interdisciplinary program that sits at the intersection of mathematics, computing, and domain application. It is not a renamed computer science degree — the curriculum is built ground-up around AI and data science as primary disciplines, drawing from mathematics, statistics, computational science, biology, physics, and chemistry in an integrated way. The goal is to produce graduates who can not only build AI systems but understand them deeply — mathematically, computationally, and ethically.
Nine core learning outcomes anchor the program: mathematical foundations, ML/AI model development, learning algorithms and statistical inference, programming for AI, data acquisition and pre-processing, systems thinking for ML deployment, mathematical modelling and simulation, application to real-world problems, and fair and responsible AI.
Let's break this down into what you will study each year.
Year 1: Semester 1 and 2 — Foundations
The first year is distinct from other B.Tech programs at IITM. Rather than starting with general engineering physics and chemistry, AIDA students begin with the mathematical and computational tools specific to their discipline:
Semester 1 covers Foundations of Linear Algebra (9 credits), Calculus for Engineers (9 credits), Programming and Data Structures (9 credits), a Programming Lab (6 credits), and Basics of Engineering Principles (9 credits) — alongside the standard first-year components of Life Skills I, Workshop I, Recreation, NSO/NCC/NSS, and Ecology and Environment.
Semester 2 brings Probability and Statistics for Engineers (10 credits), Optimisation for Engineers (9 credits), Optimisation Lab (6 credits), Computational Methods for Data Science (10 credits), and a notable addition — Introduction to Computational Chemistry (9 credits) — reflecting the program's interdisciplinary breadth from the very beginning.
Semester 3 — Breadth Across Sciences
Semester 3 is where AIDA's interdisciplinary character becomes most visible. Students take Introduction to Computational Physics (9 credits), Introduction to Computational Biology (9 credits), Machine Learning I (9 credits) with an ML Lab (6 credits), Data Curation and Visualisation (9 credits), and an Entrepreneurship course (9 credits), the last being a compulsory component introduced as part of the 2024 curriculum reforms.
Semester 4 — Core AI
By Semester 4, the curriculum tightens into core AI: Algorithms for Data Science (9 credits), Introduction to Computer Systems (9 credits), Artificial Intelligence (9 credits) with an AI Lab (6 credits), alongside a Physics Elective (9 credits) and the first Free Elective (9 credits). This is the semester where students can begin shaping their individual direction.
Semester 5 — Machine Learning Depth
Semester 5 delves deep: Databases (9 credits), Machine Learning II (9 credits), MLOps Lab (6 credits), Deep Learning (9 credits), a DL Lab (6 credits), a Core Basket Elective (9 credits), and a Department Elective (9 credits). By this point, students have a comprehensive ML stack — data, models, deployment, and deep learning — all covered within a single semester.
Semesters 6, 7 and 8 — Electives, Projects and Specialisation
From Semester 6 onwards, the curriculum is largely elective-driven. Semester 6 is the designated Elective Semester, with no compulsory courses. Students may take electives from any department, pursue a full-semester industry internship, or participate in a semester exchange at a partner university. Semesters 7 and 8 include the Bachelor's Thesis Project (BTP) and further electives from the following specialisation baskets:
Speech and Language Technology
Computer Vision
Applications in Control and Detection
Time-Series Analysis
AI for Science (Biology, Chemistry, Physics)
Reinforcement Learning
Human-Computer Interaction
Students can also earn a Minor in any other department by taking 4 free electives from that department, or switch to an IDDD (Interdisciplinary Dual Degree) after their 5th semester to graduate with a B.Tech in AI and Data Analytics and an M.Tech in another discipline.
Research Ecosystem
AIDA students have direct access to WSAI's affiliated research centres from their first year — RBCDSAI (AI research), AI4Bharat (Indian language AI), CeRAI (responsible AI), and Bodhan AI (education AI). Summer internship projects at WSAI span polymer informatics, federated learning, generative AI, IoT with machine learning, and brain-computer interface research - all available to undergraduate students.
Career Paths
Graduates of AIDA are positioned for roles in AI research at companies like Google, Microsoft, Amazon, and top global AI labs; applied AI roles in healthcare, manufacturing, and finance; data science and analytics; and entrepreneurship in the AI space. For students interested in research, WSAI's MS and PhD programs offer a natural progression, as does the Prime Minister's Research Fellowship (PMRF) route.
If you have more questions about AIDA or WSAI, visit our community — AskIITM community