Term Structure
Every year is divided into three terms of four months each - January Term, May Term and September Term.
Each term of four months has 12 weeks of coursework (video lectures and assignments), 2 in-person invigilated Quizzes and End Term Exams.
Course Registrations
In each term, learner may register for upto 4 courses.
Foundational courses: The learners must register for the following two Foundational courses in the first term. Additionally, the candidate may choose to register for one or two more courses in the first term.
Diploma in Programming: Computational Thinking and Programming in Python
Diploma in Data Science: Mathematics for Data Science II and Statistics for Data Science II
Projects: Two of the courses have a project component of 2 credits each. Learners registering for these courses will automatically be registered for the respective projects. These projects can be completed alongside their theory courses or in a later term.
Diploma in Programming: Modern Application Development I - Project and Modern Application Development II - Project
Diploma in Data Science: Business Data Management - Project and Machine Learning Practice - Project
Assessments
There are 3 types of assessments for each course:
online Weekly Assignments
monthly in-person Quizzes
in-person End Term Exam
In addition, assessments may include projects, programming exams and vivas.
Exam Cities
The Invigilated Quizzes and End Term exams are conducted in a number of cities spread across India. The map shows our current Exam Cities List. View List
Students residing/physically present in India on exam day
All students residing in India or physically present in India on the day of an in-centre exam must write exams at one of the exam centres in india.
Learners based outside India
We also conduct in-person exams in Bahrain, Kuwait, Oman and UAE.
Learners based out of other countries will be allowed to take up remote proctored exams. On exam day, students writing such internet based exams will be asked to pin the location exam is being taken from.
If any overseas students are planning to be in India on exam day, it is the student's responsibility to notify us ahead of time so that we can arrange for you to write the exam(s) in one of the exam centres in india; hall tickets will also be issued suitably. If any of these norms are violated, it will be considered as malpractice. Exam results may be withheld pending investigation and findings of the exam committee.
Note: Additional Exam Fee applies for all learners opting to write exams outside India.
If you reside outside India and cannot find a centre in your city / country, please write to ge@study.iitm.ac.in for assistance.
Fee Structure
For details about application fees, check Application Process section in Admissions page.
Each term, pay only for the courses you register for in that specific term.
Foundational courses - ₹4,000 per course
Diploma in Programming: Computational Thinking; Programming in Python
Diploma in Data Science: Mathematics for Data Science II; Statistics for Data Science II
Skill Enhancement courses - ₹7,500 per course.
Diploma in Programming: System Commands
Diploma in Data Science: Tools in Data Science
All other Diploma courses - ₹10,000 per course
Projects - ₹2,500 per project
Diploma in Programming: Modern Application Development I - Project; Modern Application Development II - Project
Diploma in Data Science: Business Data Management - Project; Machine Learning Practice - Project
Additional Exam Fee will apply ONLY for learners taking our courses from outside India.
Fee waivers depend on the socio-economic background of the learner.
| Family Income > 5 LPA | Family Income > 1 LPA and <= 5 LPA | Family Income <= 1 LPA | ||||
|---|---|---|---|---|---|---|
| Fees | Docs Required | Fees | Docs Required | Fees | Docs Required | |
| General | Full Fee | NIL | 50% waiver | EWS + Family Income | 75% waiver | EWS + Family Income |
| OBC | Full Fee | NIL | 50% waiver | OBC-NCL + Family Income | 75% waiver | OBC-NCL + Family Income |
| SC / ST | 50% waiver | SC / ST | 50% waiver | SC / ST | 75% waiver | SC / ST + Family Income |
| PwD | 50% waiver | PwD | 50% waiver | PwD | 75% waiver | PwD + EWS / OBC-NCL + Family Income |
| SC / ST + PwD | 75% waiver | SC / ST + PwD | 75% waiver | SC / ST + PwD | 75% waiver | SC / ST + PwD |
*Fee waiver is not be applicable for the International students.
The term
family income for the purpose of availing fee waivers includes the income of the
candidate, the income of his/her parents and spouse, also the income of his/her siblings and children below the age of
18 years. family income certificate is not required while applying for Diploma program, but will be required to avail
fee weiver when joining the program.
Download Family income Certificate format
OBC-NCL / EWS certificate, if applicable, need to be obtained in following format while applying.
Diploma in Programming
| Computational Thinking | Foundational course | 4 credits | Details |
| Programming in Python | Foundational course | 4 credits | Details |
| Database Management Systems | Programming course | 4 credits | Details |
| Programming, Data Structures and Algorithms using Python | Programming course | 4 credits | Details |
| Modern Application Development I | Programming course | 4 credits | Details |
| Modern Application Development I - Project | Project | 2 credits | Details |
| Programming Concepts using Java | Programming course | 4 credits | Details |
| Modern Application Development II | Programming course | 4 credits | Details |
| Modern Application Development II - Project | Project | 2 credits | Details |
| System Commands | Skill Enhancement course | 3 credits | Details |
Diploma in Data Science
| Mathematics for Data Science II | Foundational course | 4 credits | Details |
| Statistics for Data Science II | Foundational course | 4 credits | Details |
| Machine Learning Foundations | Data Science course | 4 credits | Details |
| Business Data Management | Data Science course | 4 credits | Details |
| Business Data Management - Project | Project | 2 credits | Details |
| Machine Learning Techniques | Data Science course | 4 credits | Details |
| Machine Learning Practice | Data Science course | 4 credits | Details |
| Machine Learning Practice - Project | Project | 2 credits | Details |
| Business Analytics | Data Science course | 4 credits | Details |
| Tools in Data Science | Skill Enhancement course | 3 credits | Details |