In today’s data-driven world, with information pouring in from countless fields, the lines between disciplines are blurring. Recognizing this, the Interdisciplinary Dual Degree in Data Science (IDDD-DS) program at IIT Madras bridges the gap, empowering students from any BTech background to leverage the power of data science and enhance their core research areas.
IIT Madras’s IDDD-DS program stands out as a one-of-a-kind 5-year dual degree opportunity for BTech students. Starting in their 6th semester, students can delve into the world of data science while still pursuing their primary discipline. The program boasts diverse faculty from the School and also other departments, ensuring a comprehensive curriculum and research collaborations with various Centres affiliated with the school. This flexibility allows students to tailor their studies, specializing in specific applications or strengthening their foundational knowledge.
IIT Madras’ IDDD-DS program bridges the gap between Data Science and diverse disciplines. Open to B.Tech students from any field, this 5-year program starting in semester 6 offers:
- Interdisciplinary learning: Courses taught by faculty from various departments
- Exciting industry and socially relevant research projects: Work with researchers across campus in various research centres and the school, on cutting-edge research projects
- Tailored learning paths: Specialize in application areas or delve deeper into fundamentals, by tailoring your electives
- Dual degree advantage: Earn a B.Tech degree in your field and a Master’s in Data Science
More details: https://ioe.iitm.ac.in/program/data-science/
Detailed Curriculum: (160 credits)
Semester 6 (12 credits)
| Course Number | Course Name | L | T | E | P | O | Total Credits |
|---|---|---|---|---|---|---|---|
| DA5000 | Mathematical Foundations of Data Science | 4 | 0 | 0 | 0 | 8 | 12 |
Semester 7 (27 credits)
| Course Number | Course Name | L | T | E | P | O | Total Credits |
|---|---|---|---|---|---|---|---|
| DA5400 | Foundations of Machine Learning | 4 | 0 | 0 | 0 | 8 | 12 |
| DA5401 | Data Analytics Laboratory | 1 | 0 | 0 | 3 | 2 | 6 |
| Elective 1 | 9 |
Semester 8 (36 credits)
| Course Number | Course Name | L | T | E | P | O | Total Credits |
|---|---|---|---|---|---|---|---|
| DA5402 | Machine Learning Operations Lab | 4 | 0 | 0 | 0 | 8 | 12 |
| Elective 2 | 4 | 0 | 0 | 0 | 8 | 12 | |
| Elective 3 | 4 | 0 | 0 | 0 | 8 | 12 |
Summer (20 credits)
| Course Number | Course Name | L | T | E | P | O | Total Credits |
|---|---|---|---|---|---|---|---|
| ID5490 | Project-I | 0 | 0 | 0 | 0 | 20 | 20 |
Semester 9 (30 credits)
| Course Number | Course Name | L | T | E | P | O | Total Credits |
|---|---|---|---|---|---|---|---|
| ID5491 | Project-II | 0 | 0 | 0 | 0 | 30 | 30 |
Semester 10 (35 credits)
| Course Number | Course Name | L | T | E | P | O | Total Credits |
|---|---|---|---|---|---|---|---|
| ID5492 | Project-III | 0 | 0 | 0 | 0 | 35 | 35 |
List of pre-approved electives
| Course Number | Course Name |
| BT5240 | Computational Systems Biology |
| BT5450 | Data-driven Modeling and Optimization of Bioprocesses |
| BT6270 | Computational Neuroscience |
| BT6320 | Protein Interactions: Computational Techniques |
| CE5290 | Transportation Network Analysis |
| CE5390 | Analytical Tech. in Transportation Engg |
| CE6051 | Machine Learning in Civil Engineering |
| CH5020 | Statistical Design and Analysis of Experiments |
| CH5170 | Process Optimization |
| CH5230 | Data-driven Modelling of Process Systems |
| CS5691 | Pattern Recognition and Machine Learning |
| CS6023 | GPU Programming |
| CS6024 | Algorithmic Approaches to Computational Biology |
| CS6046 | Multi-armed bandits |
| CS6251 | Computational Models of Cognition |
| CS6300 | Speech Technology |
| CS6350 | Computer Vision |
| CS6370 | Natural Language Processing |
| CS6380 | Artificial Intelligence |
| CS6464 | Concepts in statistical learning theory |
| CS6515 | Stochastic Optimization |
| CS6700 | Reinforcement Learning |
| CS6770 | Knowledge Representation & Reasoning |
| CS6780 | Algorithmic Game Theory |
| CS6852 | Theory and Applications of Ontologies |
| CS6886 | Systems Engineering for Deep Learning |
| CS6910 | Fundamentals of Deep Learning |
| ED6001 | Medical Image Analysis |
| ED6005 | Deep Learning for Medical Image Analysis |
| EE5111 | Estimation Theory |
| EE5121 | Convex Optimization |
| EE5180 | Introduction to Machine Learning |
| EE6112 | Topics in Random Processes and Concentrations |
| EE6132 | Advanced Topics in Signal Processing |
| EE6150 | Stochastic Modeling and the Theory of Queues |
| EE6180 | Advanced Topics in Artificial Intelligence |
| EE6418 | Game Theory with Engineering Applications |
| ID5130 | Parallel Scientific Computing |
| ID6040 | Introduction to Robotics |
| MA5750 | Applied Statistics |
| CH5440 | Multivariate Data Analysis for Process Modeling |
| CH5650 | Molecular Data Science and Informatics |
| CH5710 | Applications of machine learning in reaction engineering |
| EE5178 | Modern Computer Vision |
| EE5179 | Deep Learning for Imaging |
| MA5013 | Applied Regression Analysis |
| ME6324 | Artificial Intelligence in Mfg. |
| EE5176 | Computational Photography |
| CS2050 | Non-Linear Optimization |
In addition, we have prepared a “conflict list” of courses that have significantly overlapping content (note that this is not exhaustive, and if you are doing two related-ish courses, please double validate them with your Faculty Advisor):
| Course 1 | Name | Course 2 | Name |
| CH5170 | PROCESS OPTIMIZATION | EE5121 | CONVEX OPTIMIZATION |
| CH5230 | DATA-DRIVEN MODELLING OF PROCESS SYSTEMS | EE5111 | ESTIMATION THEORY |
| CS6350 | COMPUTER VISION | EE5178 | MODERN COMPUTER VISION |
| CS6780 | ALGORITHMIC GAME THEORY | EE6418 | GAME THEORY WITH ENGINEERING APPLICATIONS |
| ED6001 | MEDICAL IMAGE ANALYSIS | EE5179 | DEEP LEARNING FOR IMAGING |
| ED6001 | MEDICAL IMAGE ANALYSIS | ED6005 | DEEP LEARNING FOR MEDICAL IMAGE ANALYSIS |
| ED6001 | MEDICAL IMAGE ANALYSIS | EE5178 | MODERN COMPUTER VISION |
| EE6112 | TOPICS IN RANDOM PROCESSES AND CONCENTRATIONS | EE6150 | STOCHASTIC MODELING AND THE THEORY OF QUEUES |
| ED6005 | DEEP LEARNING FOR MEDICAL IMAGE ANALYSIS | EE5179 | DEEP LEARNING FOR IMAGING |
| CS6515 | STOCHASTIC OPTIMIZATION | EE6112 | TOPICS IN RANDOM PROCESSES AND CONCENTRATIONS |
| BT5450 | DATA-DRIVEN MODELLING AND OPTIMIZATION OF BIOPROCESSES | CS6515 | STOCHASTIC OPTIMIZATION |
| ED6005 | DEEP LEARNING FOR MEDICAL IMAGE ANALYSIS | EE5178 | MODERN COMPUTER VISION |
Thanks & Regards,