Frequently Asked Questions (FAQs)

Q: When does IISc send out offer emails? Is it before the COAP rounds?
A: Yep! IISc usually sends out offer emails a few days before the COAP results are declared — typically 2–3 days in advance. So keep an eye on your inbox!

Q: What’s the intake at CDS?
A: For the M.Tech (Coursework) program, the intake is currently around 37 students. For M.Tech (Research) and PhD, the intake isn't fixed — it varies each year based on lab requirements, available guides, and specific research openings. Checkout the respective Lab pages.

Q: What’s the weightage of the GATE score, written test, and interview? Is the written test compulsory?

A: The GATE score carries the most weight — around 70–80%. The written test and interview usually contribute about 10–15% each. And yes, the written test is absolutely important — you need to qualify in it to even make it to the interview stage. So don’t skip prepping for it! 

Q: How are placements in CDS?

A: Placements at CDS are pretty solid, especially if you're coming from a non-CS background or a 2nd/3rd-tier college — you'll likely see a considerable jump in your salary. Whether you have prior work experience or not doesn’t matter much.  Companies like WellsFargo, Captila One, Meesho, Fujitsu, Netradyne, Goldman Sachs, Lam Research, Kotak Mahindra and many more offer placements. As long as you're genuinely interested in getting placed and put in the effort, you’ll land a good role. The average package is typically around ₹24–25 LPA, sometimes even higher depending on the year and company trends.

Q: Which is better — IIT Bombay IEOR or IISc CDS?
A: Honestly, there’s no one-size-fits-all answer — it really depends on your goals and interests.
That said, here’s my take: If you're mainly interested in Machine Learning, Data Science, or AI roles, and you're looking for strong industry placements with great salary packages, then IISc CDS is a great fit. On the other hand, if you're more inclined toward operations research, optimization, or analytics-heavy roles, IIT Bombay IEOR might be a better choice. At the end of the day, both are top-tier — it just depends on what excites you more.

Interview and Written Test tips:

Pre-requisites (Don't Stress, Just Prep a Bit!) for Written Tests/Interviews:

Since CDS is an interdisciplinary department, we totally get that everyone comes in with different backgrounds. So, to make you transition smoother, here are some super helpful resources to check out before joining.

1. Linear Algebra: Checkout the classic lecture playlist by Prof. Gilber Strang (MIT). It'll really help you in Numerical Linear Algebra Course.

2. Probability: Lectures by Prof. John Tsitsiklis - great for getting a grip on topics in the Introduction to Data Science course.

3. Coding: Make sure you're comfortable with the basics of C/C++ and Python. Even beginner tutorials on YouTube are enough. Also, brush up on Data Structures (Abdul Bari's videos are a solid pick) and Algorithms. For practicing, I would suggest solving problems on Hackerrank, Codechef, LeetCode any other online coding platforms which helps in solving DSA related problems especially on matrix manipulations and arrays (sorting/searching etc..).

Going through these will also give you an edge during the CDS interviews. And hey - if you can, take a quick look at Multivariable Calculus too. It pops up a lot in Machine Learning topics.

Some students have shared their research and coursework interview experiences (search on the net you will find plenty more):

• Interview Experiences - Quora

• Interview Process - Somil Jain Medium

• Gateoverflow - CDS and CSA research Experience 

• CDS Written Test Sample Problems

• Sneha Negi - YouTube CDS Experience

When I was preparing, I didn't know everything either and that's totally okay! I focused on the basis, and practiced coding problems regularly. What matters most is curiosity and the willingness to learn. Trust me, once you're here, you'll figure things out as you go. So realx, enjoy the learning process, and feel free to reach out if you need help!

After accepting offer!!

General Instructions:

Before beginning the degree at CDS, I highly suggest the student to read Student Information Handbook, and also visit the CDS website (the best place to get to know CDS) for much more clarity (things written here are not a substitute to it but is a mere subset). It will clear most of the doubts regarding the types of courses, credit distribution and CGPA calculations etc.

There are three degree programs offered by CDS Dept. MTech coursework (2 years), MTech Research (2.5-3 years), PhD (5-7 Years). The MTech coursework curriculum requires the highest number of credits to be earned through courses (36 credits worth coursework + 28 credits worth MTech Thesis) and is majorly industry focused.

All of this here, is with respect to MTech coursework student. Note that for coursework students, he/she requires to complete 36 Credits worth of courses and 28 credits worth MTech Thesis. The criterion is minimum 12 credits worth of courses per semester and a maximum cap on credits based on CGPA obtained in the previous semester (For students in the first semester, they can take a maximum of 18 credits worth courses).

• If TGPA < 7.0, a maximum credit of 16 is allowed

• If TGPA > 7.0 and < 9.0, a maximum of 18 credits is allowed

• If TGPA > 9.0, a maximum of 21 credits is allowed

There are three types of Courses offered at IISc. 

Course Structure:

1. Core Courses (Compulsory Courses offered by their home department)

2. Soft Core Courses (To complete a minimum number of credits (For CDS: 10 Credits minimum) from pool of courses offered by their home department)

3. Elective Courses (Courses offered by other departments at IISc)

NOTE: A masters student is not allowed to take 100 level courses (UG level). He/She can take only 200 or above level course in order for the course credits to count towards degree completion.

For MTech Coursework student, he/she is required to complete 4 Core Courses in the tenure of two years, which are,

1. Introduction to Scalable Systems (4 Credits)

2. Introduction to Data Science (3 Credits, Prof. Anirban Chakrobarty)

3. Numerical Linear Algebra (3 Credits, Prof. Phani Motamarri)

4. Numerical Methods (3 Credits, Prof. Ratikanta Behra)

Note that Core courses are compulsory courses and there is no substitution to these courses i.e. these are must to do if you are to graduate. In general, a wise decision would be to at least complete all the four core courses in the first semester itself. Daring students may go on to take one additional course which could be a soft course or an elective from other departments. A good suggestion for those who are interested in Machine Learning and Deep Learning is to take Stochastic Models and Applications course by Prof. Shastry which serves as a fundamental course in probability and statistics for subjects in second semester like Machine Learning, Deep Learning for Computer Vision, Natural Language Processing (NLP) and many more ML/DL oriented courses.

Each professor here heads a lab in which he works on areas of his/her interest. At the end of your first semester, you would be required to choose professors for your MTech Thesis (You may even work on some collaborative project if the professors agree). Some of the high scoring students might be approached by professors to join their labs, others might be required to find a professor/lab based on their interest. Normally, a student is required to do thesis work on topics based on the requirements of the professor/lab, but if you are willing and interested/motivated enough by some research topic you may approach a faculty which is most relevant and working in that area for guidance, and based on his interest and your dedication the proposal might get accepted.

On a sidenote, regarding selecting guides for your MTech Thesis, labs in CDS comprises of two parts. One is CS labs like DREAM, MARS, etc.. which work on core CS research topics and the other are computational labs like MATRIX, FLAME etc... Normally, the trend has been that professors from CS labs prefer students with their bachelor's in computer science, or at least have given GATE in CS. For the other students, i.e. from non-CS branches generally prefer and easily get into the computational labs. But, for those who are from non-CS branches but are interested in CS labs, it is difficult to get into those labs for non-CS student, but still they can if they are highly interested and are capable and could convince professors their worth. For students from CS branches interested in working in computational labs, are accepted based on the vacancy and professor's interest. At last, it highly depends on professor and vacancy available in the lab.

In the second semester it is highly advised to take courses relevant to your Thesis and (if you are very much interested in Job) courses which are in high demand for jobs such as Pattern Recognition in Neural Networks (EE Dept, Prof. Pratosh AP), Natural Language Processing (CDS Dept, Prof. Danish Pruthi), Parallel Programming (CDS Dept, Prof. Satish Vadhiyar), Deep Learning for Computer Vision (CDS Dept, Prof. Venkatesh Babu) and many more. It is advised to credit courses after discussing with your guide and based on your interest.

Credit Calculation is as shown:

CGPA= (sum (Course credit * Grade)) / (sum (Course Credit))

Here Grade is converted as:

A+ = 10

A =9

B+=8

and so on...

Refer to this document which is a guidance documents for juniors on how things work here at CDS.

First Sem Recommendations

1. Introduction to Scalable Systems: For the part of Computer Architecture, I recommend you read the relevant part from the book: "Parallel Computing Architecture. A Hardware/Software Approach. David Culler, Jaswant Singh. Publisher: Morgan Kauffman. ISBN: 981-4033-103. 1999" and Relevant Lectures by Professor Mathew Jacob (Lec 1 - 4, 25 - 31). For the Data Structures and Algorithms any YouTube Lectures would be sufficient (I preferred Data Structures lectures by Abdul Bari on Udemy and Algorithms lectures from YouTube). For the parallel programming part, I recommend reading my notes along with attending professors lectures and his slides and the references given on the Introduction to Scalable Systems website and last four lectures by Prof. Mathew Jacob for introduction to parallel architecture (Lec 38 - 41).

2. Introduction to Data Science: I recommend lectures by MIT Prof. John Tsitsiklis

3. Numerical Linear Algebra: The Class notes along with previous year papers and assignments are sufficient.

4. Numerical Methods: Lecture Slides along with reading recommended book: Burden R.L., Faires J.D. - Numerical analysis-Brooks Cole (2010) is sufficient.

For the courses on the second semester, I credited Numerical Solutions of Differential Equations, Deep Learning for Computer Vision, Machine Learning for Data Science and random Variates for Computation. For the references to these materials, look at the Pdf by one of our seniors (it will also help as guidance for placement preparation). For projects, you can add Assignments/Projects done as part of courses. For additional projects checkout projects on Kaggle (It is recommended to at least participate in competitions on Kaggle for improving your ML and DL knowledge (you can also earn money)).

For ML/DL/AI (During December break)

If you are particularly interested in ML here are links to some good lectures on the following courses, Machine Learning by Andrew Ng, Deep Learning by Mitesh Khapra, Computer Vision, Natural Language Processing by Prof. Christopher Manning, Deep Learning for Computer Vision by Justin Johnson, Machine Learning with Graphs and many more. There are also good lectures on Reinforcement Learning (also Reinforcement Learning by David Silver, Google Deepmind), Artificial Intelligence by Stanford on YouTube are highly recommended. Also, do not forget to checkout their corresponding websites for each of these courses (links are given in description of YouTube lectures) which has assignments (very important), notes and project ideas, links to published papers and many more.

Second Sem Recommendations:

The second semester is a great opportunity to bounce back, recover, or build strong momentum—especially if the first semester didn’t go as planned. Here are a few important points to keep in mind: 

• Improve Your CGPA if Needed: If your CGPA is below 8.0, aim to push it above that mark. While not all companies enforce a CGPA cutoff, many do shortlist candidates with a CGPA above 7.0 or 8.0. A higher CGPA simply opens up more opportunities, increasing your chances of being shortlisted by a larger pool of companies.

• Don’t Rely Solely on CGPA: That said, CGPA isn't everything. I've seen students with CGPAs below 7.0 land excellent placements because of their skills and project work. So, don’t be discouraged—but do use your CGPA as a metric to expand your options. 

• Choose Subjects Strategically: Post-first semester, the smartest move is to balance your subject choices. If your CGPA is already strong (say, above 9.0), you can afford to challenge yourself with tougher, more competitive, placement-relevant electives. If you're aiming to improve your CGPA, consider mixing difficult subjects (to build skills and competitiveness) with a few easier ones (to help stabilize or boost your GPA). Essentially, strike balance in choosing subjects in your second semester subjects which are a blend of placement relevant tough competitive subjects and a few easy subjects which would help you boost your CGPA.

• Aim for Balance: Avoid choosing only easy subjects just to inflate your CGPA. Companies also assess your core competencies and practical skills. A high CGPA alone won’t guarantee a good job or package—what matters is a combination of strong academics, relevant skills, and real-world readiness. 

In summary: the second semester is your golden chance to strike the right balance—between GPA improvement and subject depth. Use it wisely.

A Wild but Strategic Approach

If you're confident in your abilities and have a solid CGPA, consider taking a bold step: choose all tough, high-impact subjects—like DLCV, NLP, MLDS, Parallel Programming, or NSDE. This route is intense and might put your CGPA at risk, but the trade-off is a major boost in practical skills and competitiveness. These subjects are highly relevant for placements, and mastering them can set you apart from your peers. Even if your CGPA takes a slight dip, your technical edge could make you eligible for most companies—especially those that prioritize skills over strict CGPA cutoffs.

Note: This strategy is primarily for students focused on placements for students interested in AI/ML roles.

For M.Tech Students Aiming for a PhD

If your goal is to pursue a PhD, the approach should be different. Focus on subjects that align with your research interests, not just those that are placement-oriented. It’s important to maintain a CGPA above 9.0, as many top PhD programs—both in India and abroad—consider it a key selection criterion.

Aim to strike a balance here too: choose a mix of subjects that are academically rigorous yet manageable (most importantly are of your interest), so you can explore your interests deeply while keeping your GPA strong. This balance will strengthen both your academic profile and your research preparation.

Internship

One of the important things to keep in mind during your second semester is the internship season. Most companies begin sharing opportunities around February, and by the end of the month, interviews and tests will start rolling in. This usually continues till late April or early May. It can feel overwhelming with coursework going on, but if you're aiming for placements, internships are highly recommended. They not only boost your resume but can also lead to a Pre-Placement Offer (PPO) if you perform well. To help you out, we’ve prepared an Internship Guide with commonly asked questions, key topics to prepare, and tips to crack interviews and tests.

Now, if you don’t manage to land an internship—don’t worry at all. Think of it as a blessing in disguise. You’ll have the entire May to July free to focus completely on building your skillset. Use this time to do solid projects, get active on Kaggle or GitHub, practice DSA, and dive into placement-relevant subjects. There are tons of amazing lectures from places like Stanford and MIT available online. Make the most of this time to sharpen your edge and stand out during placements. Not getting an internship isn’t the end—it can actually be the perfect opportunity.

Bonus Tips:

For doubts of students regarding Medical Certificate and Verification: It is compulsory. For the part regarding vaccination, you can complete the requirements of vaccination after joining IISc. You will be given a date to report to Health center at IISc for your medical verification and it is during that verification, your medical reports would be thoroughly checked and it would be preferred if you would have vaccination certificate from the doctor. If you don't have your vaccination certificate you will be asked to submit them as soon as possible.

Vehicles are strictly not allowed on campus for M.Tech coursework and research students. Only PhD students are allowed to use vehicles inside the campus after an application.

For any further doubts, feel free to connect to me via mail or on any one of the platforms below. 🙂