A stack you can
actually exit.
IITM BS DS is not “four years then a degree.” It is a ladder of credentials with load freedom sideways and a prerequisite graph downward. This desk makes that architecture readable before you open the course list.
Credits accumulate. Exits are named.
Level 1
+32 cr
Foundation
Mathematics, statistics, programming, computational thinking, and communication as the academic base.
Eight 4-credit courses. Sequencing exists (Math I → Math II; Stats I with math prerequisites → Stats II).
Foundation complete
32 so far
Level 2
+54 cr
Diploma level
Two parallel diplomas, not one sequential block.
Programming 27 credits and Data Science 27 credits. Both sit on Foundation; they are siblings, not a queue.
Diploma in Programming + Diploma in Data Science
86 so far
Level 3
+28 cr
BSc Degree Level
A real exit. The BSc is not the final undergraduate credential.
Foundation + both diplomas + this 28-credit layer.
BSc in Programming and Data Science
114 so far
Level 4
+28 cr
BS Degree Level
The higher undergraduate degree in the standard ladder.
Another 28-credit layer after the BSc exit.
BS in Data Science and Applications
142 so far
Level 5
+20 cr
PG Diploma in AI & ML
Optional postgraduate stack after the BS.
Not required to hold the BS.
PG Diploma in AI & ML
162 so far
Level 6
+20 cr
MTech in AI & ML
Optional final stack after the PG Diploma.
Not required to hold the BS.
MTech in AI & ML
182 total
The whole ladder on one map.
Poster-style lanes in the spirit of the Embedded Systems Engineering Roadmap you shared — START entry, grouped lanes, and a three-step legend. Credit totals come from the canonical stack above; this section only adds the course, fee, and mechanics detail.
● Start — qualifier join
Clear the qualifier and join immediately to convert that transition into Foundation from Week 5, with qualifier marks counting as Quiz 1 for same-term Foundation registrations — or hold the result inside its validity window. Exact wording lives on the current IITM FAQ.
Time-sensitive, as pasted 2026-09-10: Qualifier applications for the Sep 2026 term were open, last date September 27, 2026. Verify on the live admissions page before acting.
Lane A · Undergraduate walk
Foundation 32 → Diplomas 86 → BSc 114 → BS 142
Foundation · 8 × 4 cr · all 8 gate the diplomas
- Required: Mathematics for Data Science I BSMA1001
- Required: Statistics for Data Science I BSMA1002
- Required: Computational Thinking BSCS1001
- Required: English I BSHS1001
- Required: Mathematics for Data Science II BSMA1003
- Required: Statistics for Data Science II BSMA1004
- Required: Programming in Python BSCS1002
- Required: English II BSHS1002
Exit: Foundation Certificate from CODE, IIT Madras
Diploma twins · 27 + 27 cr · siblings, not a queue
Diploma in Programming
- Required: Database Management Systems BSCS2001
- Required: Programming, Data Structures and Algorithms using Python BSCS2002
- Required: Modern Application Development I BSCS2003
- Required: Modern Application Development I — Project BSCS2003P
- Required: Programming Concepts using Java BSCS2005
- Required: Modern Application Development II BSCS2006
- Required: Modern Application Development II — Project BSCS2006P
- Required: System Commands BSSE2001
Diploma in Data Science
- Required: Machine Learning Foundations BSCS2004
- Required: Business Data Management BSMS2001
- Required: Machine Learning Techniques BSCS2007
- Required: Machine Learning Practice BSCS2008
- Required: Machine Learning Practice — Project BSCS2008P
- Required: Tools in Data Science BSSE2002
- Track choice: Business Data Management — Project BSMS2001P
- Track choice: Business Analytics BSMS2002
Track rule effective September 2025 onwards: trunk + exactly one 6-credit track.
Exit: Diploma in Programming and/or Diploma in Data Science from IIT Madras
BSc 28 cr → BS 28 cr · five shared core, electives vary by term
- Required: Software Engineering BSCS3001
- Required: Software Testing BSCS3002
- Required: AI: Search Methods for Problem Solving BSCS3003
- Required: Deep Learning BSCS3004
- Required: Strategies for Professional Growth BSGN3001
Degree electives (35 on the pasted list — availability changes each term)
- Elective: Algorithmic Thinking in Bioinformatics BSBT4001
- Elective: Big Data and Biological Networks BSBT4002
- Elective: Data Visualization Design BSCS4001
- Elective: Speech Technology BSEE4001
- Elective: Design Thinking for Data-Driven App Development BSMS4002
- Elective: Industry 4.0 BSMS4001
- Elective: Market Research BSMS3002
- Elective: Privacy & Security in Online Social Media BSCS4003
- Elective: Introduction to Big Data BSDA5001
- Elective: Financial Forensics BSMS4003
- Elective: Linear Statistical Models BSMA3012
- Elective: Advanced Algorithms BSCS4021
- Elective: Statistical Computing BSMA3014
- Elective: Computer Systems Design BSCS3031
- Elective: Programming in C BSCS3005
- Elective: Mathematical Thinking BSMA2001
- Elective: Large Language Models BSDA5004
- Elective: Introduction to Natural Language Processing (i-NLP) BSDA5005
- Elective: Deep Learning for Computer Vision BSDA5006
- Elective: Managerial Economics BSMS3033
- Elective: Game Theory and Strategy BSMS4023
- Elective: Corporate Finance BSMS3034
- Elective: Deep Learning Practice BSDA5013
- Elective: Operating Systems BSCS4022
- Elective: Mathematical Foundations of Generative AI BSDA5002
- Elective: Algorithms for Data Science (ADS) BSDA5003
- Elective: Machine Learning Operations (MLOps) BSDA5014
- Elective: Data Science and AI Lab BSDA4001
- Elective: App Dev Lab BSCS4010
- Elective: Computer Networks BSCS4024
- Elective: Theory of Computation BSCS3021
- Elective: Reinforcement Learning BSDA5007
- Elective: Compiler Design BSCS4032
- Elective: Discrete Mathematics BSMA3001
- Elective: Sequential Decision Making BSDA6004
Per page: up to 8 credits transferable from NPTEL across BSc/BS; up to 12 credits of apprenticeship transferable at BS level.
Lane B · Postgraduate possibility
PG Diploma 162 → MTech 182
- Track choice: ML Ops BSDA5014
- Track choice: Generative AI BSDA5002
- Track choice: Algorithms for Data Science BSDA5003
PG electives — pick 2 (11 listed)
- Elective: Large Language Models BSDA5004
- Elective: Introduction to Natural Language Processing (i-NLP) BSDA5005
- Elective: Deep Learning for Computer Vision BSDA5006
- Elective: Reinforcement Learning BSDA5007
- Elective: Responsible AI BSDA6001
- Elective: Statistical Learning Theory BSDA6002
- Elective: Deployability Aspects of AI BSDA6003
- Elective: Sequential Decision Making BSDA6004
- Elective: Information Theory and Learning BSDA6005
- Elective: Speech Technology BSEE5001
- Elective: Research Project BSDA6006
- Required: MTech Project BSDA6901
MTech project in broad ML/AI areas, done in a company or research lab; finish within 8 years of starting Foundation.
Lane C · Rules that gate speed
Mechanics
- Terms
- Three 4-month terms a year — January, May, September. Each term: 12 weeks of coursework, 2 in-person invigilated quizzes, end-term exams.
- Assessments
- Weekly online assignments, monthly in-person quizzes, in-person end-term exam. Some courses add programming exams, mini projects, vivas, or take-home assignments.
- Load (CCC)
- Up to 4 courses a term within CCC. Foundation CCC follows qualifier / previous end-term performance; Diploma onward CCC is 4.
- Progression gates
- All 8 Foundation courses before any Diploma course.
- All Diploma courses + projects before any Degree course.
- All BS Degree requirements before PG Diploma.
- All PG Diploma requirements before MTech.
- Exam cities
- Invigilated centres across India. Outside India: in-person in Bahrain, Kuwait, Oman, UAE; remote proctored elsewhere (pin exam-day location). Extra exam fee outside India. If in India on exam day, notify ahead to sit at an Indian centre. Assistance: ge@study.iitm.ac.in.
- Pace
- Up to 8 years from Foundation start, at the learner’s pace. Page estimates ~10 hrs/course/week (Foundation) and ~15 hrs/course/week (Diploma and above) — planning estimates, not requirements.
Fees · pay per term for courses taken
Revised structure for Foundation joiners, Jan 2026 onwards
| Goal | Credits on ladder | Total fees (INR) |
|---|---|---|
| Foundation Only | 32 | ₹48,000 |
| Foundation + One Diploma | 86 | ₹1,29,000 |
| Foundation + Two Diplomas | 86 | ₹2,10,000 |
| BSc Degree | 114 | ₹2,86,000 – ₹3,10,000 |
| BS Degree | 142 | ₹3,86,000 – ₹4,50,000 |
| PG Diploma in AI & ML | 162 | ₹4,86,000 – ₹5,90,000 |
| MTech in AI & ML | 182 | ₹6,86,000 – ₹7,90,000 |
Students joining at Foundation level from the January 2026 term onwards. Older intakes (till September 2025) follow a previous structure — see the official fee page.
Fail a course → repeat the full course with full fee. Completed everything but missed only the end-term exam → repeat just the end-term exam next term (₹1,000 foundation / ₹2,000 diploma-degree, per page).
Fee waivers depend on category and family income (page table: General/OBC/SC-ST/PwD across income slabs, up to 75%); document verification applies at Diploma/Degree levels. Confirm the current waiver page before planning around it.
Full course tables with codes and prerequisites (Foundation + both Diplomas)
Foundation Level
| Course | Code | Cr | Prerequisites | Corequisites |
|---|---|---|---|---|
| Mathematics for Data Science I | BSMA1001 | 4 | None | None |
| Statistics for Data Science I | BSMA1002 | 4 | None | None |
| Computational Thinking | BSCS1001 | 4 | None | None |
| English I | BSHS1001 | 4 | None | None |
| Mathematics for Data Science II | BSMA1003 | 4 | BSMA1001 | None |
| Statistics for Data Science II | BSMA1004 | 4 | BSMA1002, BSMA1001 | BSMA1003 |
| Programming in Python | BSCS1002 | 4 | BSCS1001 | None |
| English II | BSHS1002 | 4 | BSHS1001 | None |
Diploma in Programming
| Course | Code | Cr | Prerequisites | Corequisites |
|---|---|---|---|---|
| Database Management Systems | BSCS2001 | 4 | None | None |
| Programming, Data Structures and Algorithms using Python | BSCS2002 | 4 | None | None |
| Modern Application Development I | BSCS2003 | 4 | None | BSCS2001 |
| Modern Application Development I — Project | BSCS2003P | 2 | None | BSCS2003 |
| Programming Concepts using Java | BSCS2005 | 4 | None | None |
| Modern Application Development II | BSCS2006 | 4 | BSCS2003 | None |
| Modern Application Development II — Project | BSCS2006P | 2 | BSCS2003P | BSCS2006 |
| System Commands | BSSE2001 | 3 | None | None |
Diploma in Data Science — mandatory trunk (21 credits)
| Course | Code | Cr | Prerequisites | Corequisites |
|---|---|---|---|---|
| Machine Learning Foundations | BSCS2004 | 4 | None | None |
| Business Data Management | BSMS2001 | 4 | None | None |
| Machine Learning Techniques | BSCS2007 | 4 | None | BSCS2004 |
| Machine Learning Practice | BSCS2008 | 4 | BSCS2004, BSCS2007 | None |
| Machine Learning Practice — Project | BSCS2008P | 2 | None | BSCS2008 |
| Tools in Data Science | BSSE2002 | 3 | None | BSCS2008 |
Option 1 — Business Analytics (6 credits)
| Course | Code | Cr | Prerequisites | Corequisites |
|---|---|---|---|---|
| Business Data Management — Project | BSMS2001P | 2 | None | BSMS2001 |
| Business Analytics | BSMS2002 | 4 | BSMS2001 | None |
Option 2 — Introduction to Deep Learning & AI (6 credits)
| Course | Code | Cr | Prerequisites | Corequisites |
|---|---|---|---|---|
| Introduction to Deep Learning and Generative AI | BSDA2001 | 4 | None | BSCS2008 |
| Deep Learning and Generative AI — Project | BSDA2001P | 2 | BSCS2007 | BSCS2008, BSDA2001 |
Sister poster: the Embedded Systems roadmap desk (third-party, CC BY-SA 4.0) uses the same legend language.
Provenance: course/fee/mechanics detail transcribed from a user-pasted copy of the IITM Academics “Overall Structure” page on 2026-09-10 (docs/sources/2026-09-10-iitm-overall-structure-paste.md). Live portal, Student Handbook, and term grading documents are the authority; elective availability changes each term. Poster layout inspired by the Embedded Systems Engineering Roadmap (M. Parvizi, CC BY-SA 4.0) — visual language only. Stack credits: 32 / 86 / 114 / 142 / 162 / 182.
BSc is not BS.
- 114 credits
- BSc in Programming and Data Science
- 142 credits
- BS in Data Science and Applications
- Diploma split
- Programming 27 + Data Science 27 = 54. Siblings, not a queue.
Sideways load. Downward sequence.
You choose
How many courses this term. Whether to slow down. Whether another degree or a job sits beside this one.
The graph chooses
Prerequisites, level completion, and the next rung. Fastest path is still a walk down the stack.
Facts, then where to verify them.
Student map
Stackable exits, not a single four-year bet
The programme is a stack of credentials. You can stop at Foundation, a diploma, BSc, or BS without pretending the rest never existed.
Plan the next credential, not an imaginary uninterrupted four years.
Verify: IITM programme architecture / admissions pages
Student map
BSc and BS are different exits
114 credits → BSc in Programming and Data Science. 142 credits → BS in Data Science and Applications.
Never treat “the degree” as one object. Name the exit.
Verify: Current IITM degree-structure page
Student map
Load is flexible. Sequence is not.
You choose 1–4 courses a term. Prerequisites and level order still gate the fastest path. Horizontal freedom is not vertical freedom.
Speedrunning is limited by the graph, not only by how many hours you have this week.
Verify: Admissions “1/2/3/4 courses” copy + prerequisite FAQ
Public IITM page
Qualifier can become the first term
Clearing the qualifier and joining immediately can convert that transition into Foundation from Week 5, with qualifier marks counting as Quiz 1 for Foundation courses registered in that same term. Results can also be held and used later within the published validity window.
“Join after qualifier into Quiz 1 or Quiz 2” is a real mechanism, but the precise wording lives on the current FAQ.
Verify: Current IITM FAQ + Academics page before any registration decision
Public IITM page
End-Term eligibility is not the course score
Missing one quiz can still leave End-Term eligibility if the other quiz and assignment gates are met, but an unattempted quiz is typically scored as zero with no makeup. Missing both quizzes fails the End-Term gate.
A skipped quiz is not a free checkbox. Check the current term’s Academics page and grading document.
Verify: Current Academics page + term grading document
Term document
Some terms publish damage-control scoring
Jan 2026 Foundation grading documents used a max of two regimes so a weaker quiz did not automatically dominate. That is a term-document pattern, not a universal 25/25/50 law.
Never reuse last term’s weights. Open this term’s grading document.
Verify: Current term grading document (student version)
Student map
Process friction and academic rigor are independent
Three terms a year, travel for invigilated assessment, registration windows, and a prerequisite graph create process friction even when course content is the thing you wanted. Flexibility and rigor are not opposites.
When something feels brutal, name whether it is content difficulty or calendar/process load.
Verify: Handbook + your own term calendar; do not treat this as IITM policy