A bit about me
Literally 3 years ago, I was patting myself on the back for having secured a seat in Mechanical Engineering, IIT Bombay. “WOAH! Finally!! I am gonna be a mechanical engineer”. As physics was my favorite subject (especially the topic- mechanics) during classes 11 and 12, you could say I was very fortunate to get into this program (at least I thought so!). Little did I know, life would have a lot of twists and turns in the next few months and I would be hopping on a completely different journey than I expected.
Hi! I am Nabajyoti Majumdar, a to-be fourth-year undergraduate in the Mechanical Engineering Department. Hobbies include cricket, computer gaming, coding; I have even tried dancing after coming to insti. I didn’t participate in many extracurriculars (I am lazy, I admit it :P; which I regret) nor have I had a stellar performance in academics. In this blog, I have penned down my unusual journey to getting this internship and also the congenial experience I had during the virtual internship, which if any of you likes or benefits from, I shall be glad.
P.S. If this seems lengthy, head on to the last part.
Getting into Coding
Getting accustomed to programming was really a hectic journey for me. Being completely new to programming, I struggled all through the first semester and ended up getting a DD grade in CS101 (and I was just happy that I passed). Needless to say, I was embarrassed to see myself fail so badly at this, even after working harder in it than in any of my other courses; so I made a point to learn programming at the basic level which was taught in the course that I couldn’t grasp. After the first semester, I took some coaching on C language, and surprisingly I liked it once I understood the fundamentals. “After all, it’s not that bad”. However, I had no wish to pursue a career in computer science.
Fast forward to the end of the spring sem >> couldn’t make a team for ITSP but as I had a liking for statistics and probability from my school days, I registered for a machine learning project in Summer Of Science from Maths and Physics Club during my summer break. It covered the famous Andrew Ng’s machine learning lecture series. I got an idea about machine learning algos but couldn’t code them on my own. The third sem was hectic for our department, so no coding. Up to that point, life was chill.
In the fourth sem, the situation changed drastically. I was looking for internships that came through Placement Cell but wasn’t even shortlisted in any of those. My DAMP mentor, who had already gotten placed and was in his 8th and last sem, suggested that I try coding. Given my average CPI and no good PORs, that seemed to be the best option to explore. Thanks to him, I started practicing on hackerrank and eventually got a lot better . I remember that sometimes I had to sit for hours just to solve simple problems on arrays. I couldn’t secure an internship for my second-year summer break, but I knew that if I went along and kept practicing, it wouldn’t be long before I landed my first internship.
Second Year Summer Break
I made a point to undertake projects in Machine Learning. It was a crucial time but I managed to complete a few ML projects by the time I came back to insti. They weren’t very sophisticated, mostly the implementation of important algorithms. However, I want to mention two major mistakes I made during my summer break:
- I didn’t practice DSA questions well. I knew about basic data structures and some important algorithms due to which I cared less about practicing them thinking “Ye to ho hi jayega”. This was a huge mistake, as all of the coding companies be it ML, Web dev, or anything else, companies always take a test where they ask data structures and algorithm questions. You may have a tonne of domain knowledge, but if you don’t clear the test, it’s of no use. It seems unfair, but that’s how it is. So to clear those tests, you need to have deliberate practice on DSA.
- As for the projects I did in Machine Learning, most of them were done using python libraries. But in interviews, they may ask questions around the mathematics involved in the algorithms. I made the mistake of not practicing the mathematics behind those. I would suggest before going to interviews, get a good grip on the mathematics behind the libraries you used for your projects. There are a lot of good youtube videos that explain numerous ML algorithms in a very easy and lucid fashion. Just make sure you can explain the concept to the interviewer.
The Internship season started as soon as the Autumn Semester started. First, there were the resume making and submission deadlines followed by verification, after which came the Day-1 companies.
There are PPTs held by the companies where they inform about their brand, work culture, etc.
I remember the first coding test I attempted—it was for Uber. It didn’t go well. I understood the mistake I made -> the first mistake I mentioned above. This led me to practice DSA questions every day; I also took an interview preparation course from Udemy. It turns out to be hectic for all the students — there were classes, then PPTs on some days, online coding or quant tests, interviews, etc. It takes some time to get used to it. The pompous exhibition of the Day 1 selection can be very overwhelming at times, but the secret is to move on and wait for the right fit.
After nearly 1.5 months I got much better at the coding questions and started getting shortlisted. I got shortlisted at KLA Tencor but the interview was a disaster, which made me realize my second mistake. A few days later I was shortlisted for the OYO-Rooms Data Science profile. By then, I had brushed up the mathematics part of important algorithms, especially those I mentioned in my resume.
For OYO, there were two profiles, one was Project Associate and the other was Data Science. For Data Science there was one technical and one HR interview. I was asked questions on my projects (the mathematical models behind them, some short questions on implementations, and data cleaning) and some puzzles. Overall, it went well. After a few hours, the final list of selected students was posted on the Internship Blog.
Due to the ongoing pandemic, we were forced to shift to the online mode of internship. In those unprecedented times, at least doing the internship was our main priority. However, our two-month-long internship was cut down to one month. The internship started with an introduction session with all the students interning at OYO; followed by which we were given individual projects. I was given a project named “Drop-Offs in Web Platform”; which basically means I had to study the day to day customer journeys on the website as well as the app, make a model to analyze them using python3 and SQL, and suggest ways to improve the conversion rate. The deployment of the model would be done by some other team. The major takeaway of this project was that I learned to work in Google Bigquery with the industry data.
Talking about the work-culture, I would say it was pretty chill. I was assigned a mentor and a senior with whom my co-intern Ayush and I had daily meetings (on working days) . Apart from that, we obviously missed what we could have done if it was an onsite internship in a world devoid of coronavirus————- getting to know other interns, knowing more about company culture and mode of work, maybe weekend trips and whatnot.
Till now, insti life for me has been a journey of ups and downs for me. What I learned is jotted below along with an excerpt from another text.
“On the first day of class, Jerry Uelsmann, a professor at the University of Florida, divided his film photography students into two groups. Everyone on the left side of the classroom, he explained, would be in the ‘quantity’ group. They would be graded solely on the amount of work they produced. On the final day of class, he would tally the number of photos submitted by each student. One hundred photos would rate an ‘A’, ninety would rate ‘B’, and so on. Meanwhile, everyone on the right side of the room would be in the ‘quality’ group. They would be graded only on the excellence of their work. They would only need to produce one photo during the semester but to get an ‘A’, it had to be a nearly perfect image. At the end of the term, he was surprised to find that all the best photos were produced by the quantity group. During the semester, these students were busy taking photos, experimenting with composition and lighting, learning from their mistakes, while the other group was busy speculating about perfection–in the end they had nothing to show other than unveiled stories and one mediocre photo.”
The thing I would say is– just keep doing the right things. There’s no ‘best way to build muscles’, no ‘perfect idea for a side hustle’, no ‘best coding course’, no ‘best internship/job’. And there’s also no need to be very good at everything we do. No need to get bogged down by peer pressure, build your own path, and develop yourself in the process.