The BSDS First Semester Guide

Hello everyone! Since the counselling rounds are nearing their end, a lot of you have been asking me questions about the curriculum and how academics are going to look here. This post is for exactly that. After reading it, you should have a decent idea of what studying in BSDS looks like, regardless of your center. I hope it helps the new folks joining us on campus in the fall of 2026, as well as anyone still deciding whether to apply to the program in upcoming years.

I'll go course by course for the first semester and try to give you a proper idea of how to study for each one and what to keep in mind. But before that, here are a few general points everyone should read first.

A Few General Points

1. Cyclotron Pulsar is the most comprehensive list of resources compiled by your seniors across every center. Make good use of it.
2. Make a LinkedIn and a GitHub account if you don't already have one.
3. Know your professors. It's worth looking up their research interests and profiles beforehand. IRINS is a good place to start.
4. Explore beyond just academics. Remember, you're in college now, not school. Use this time to explore different domains, you might end up liking finance, banking, tech, or research, or you might decide you'd rather go for higher studies later on. Try a bit of everything before you settle on one path.
5. Please try to attend classes. Bunking is bad, and attendance has generally shown a positive correlation with marks. If you do end up missing a class, make sure you cover everything that was taught in it.
6. There are no better resources than your class notes, assignments, and quiz questions. Revise them properly. This isn't true for every single course, but it holds generally.
7. The batch average is usually near 65, with the highest going beyond 90, though that's typically just one or two students across all centers. Scoring in exams is generally difficult but it sometimes depends highly on the professor as well

Now I'll go through all the courses you'll have in your first semester. You should also refer to the course descriptions in the curriculum linked above.

The First Semester Courses

Statistics I: Data Exploration

Taught by Dr. Kaushik Jana, from the Delhi center

This is probably the easiest course you'll take in your first semester. You'll get an informal introduction to R along with the basics of Simple Linear Regression (SLR) and Simple Random Sampling (SRS). These sound like big words, but they're genuinely easy once you put in a little effort. Some resources worth looking at:

• Simple Linear Regression Analysis notes by Prof. Shalabh at IIT Kanpur. It's a 40 page PDF, but you only need to study the first 8 to 10 pages.
• Simple Random Sampling notes by Prof. Shalabh at IIT Kanpur. It's a 23 page PDF, and skimming the first 5 pages should be more than enough.
• The Art of Statistics: Learning from Data by David Spiegelhalter. This book reads almost like a novel. It explains various concepts in descriptive statistics through real life data and cases in R, and it gets used a lot before the mid semester exams.

To learn R, just attend classes and try to code along with whatever you can. After class, put your code into an AI tool like Gemini or ChatGPT and ask it about anything you're unsure of, then go through the code again yourself. It helps a lot. You'll likely only be introduced to SLR and SRS properly after the mid sems.

Keep in mind this course isn't meant to be an exhaustive R course. That comes in second semester, likely taught by Prof. Deepayan Sarkar. Great guy, by the way.

Probability I

Taught by Prof. Abhay Gopal Bhatt, from the Delhi center

Abhay sir is a genuinely good and experienced professor, so I'll repeat the point about attendance here, and more strongly this time. This course is difficult, since it introduces a lot of new and important concepts that form the basis of everything you'll study later in statistics and data science. Don't bunk his classes, and make sure you take proper notes, at least for this one. Some resources to look at:

• Introduction to Probability Theory by Hoel, Port and Stone. This is concise and works well as a reference alongside your lecture notes and classes. It's not meant for self study on its own, or you'll end up putting in a lot more effort than necessary.
• A First Course in Probability Theory by Sheldon Ross, or Probability by J. Pitman. Both are good alternatives for reference.
• Probability and Statistics with Examples using R by Siva Athreya, Deepayan Sarkar, and Steve Tanner. Another solid option if you want to look further.

My honest suggestion is to attend his classes and study from the lecture notes. They're the most effective resource for this course. Also make sure to solve the assignments, which you'll find on the Cyclotron website.

Mathematics I

Offered from the Bangalore center; professor unfortunately not known to me

This course introduces you to Real Analysis, and it has a steep learning curve. It's completely fine if you don't understand more than half of what's happening in class sometimes. You'll need to read through the texts and spend real time working through proofs. That's the only way analysis clicks. Worth looking at:

• Elementary Analysis by Kenneth A. Ross. This was the officially recommended text for our Math I course, and we covered almost the entire book. It can be tough at first, but once you spend time with it, it starts to make sense.
• Introduction to Real Analysis by Bartle and Sherbert. Another good book that a lot of people at ISI recommend.

Alongside analysis, you'll also be introduced to Linear Algebra. For that:

• MIT OCW 18.06, Linear Algebra by Prof. Gilbert Strang. This alone can carry you through first semester. Watch the series, at least through lecture 13.
• Linear Algebra Done Right by Sheldon Axler. A pretty standard introductory book that teaches linear algebra without leaning on matrices. Worth reading if you have the time.

Real Analysis is going to take up a major chunk of your first semester, so make it your priority.

Introduction to Computing

Taught by Prof. Mandar Mitra, from the Kolkata center

This will be your first real introduction to Python, unless you've studied it before. You'll go through the basics, move into Object Oriented Programming, cover functions, and toward the end get introduced to data structures and some sorting techniques. This is just an introduction, not an exhaustive course, especially for the data structures and sorting parts. Prof. Malay Bhattacharyya from ISI Kolkata was our professor for this one, and his notes are really good. You'll also find them on the Cyclotron website.

Before I Go

That's really all I have to offer for now. Academics are, without a doubt, going to be the most important part of your life at ISI, so there's no point neglecting them. You'll be able to enjoy everything else here as long as your academics are in line.

If you have any other doubts that haven't been answered above, feel free to reach out and I'll try my best to help.

Thanks for reading, and all the best! 😊