Ruchi Bhatia is a Graduate Student at Carnegie Mellon University, a Data Scientist at OpenMined and a Data Science Global Ambassador at Z by HP. She is a 2x Kaggle Grandmaster and a firm believer in the potential of AI to solve day-to-day problems. “Consistently capturing trends in the data industry has been my source of motivation throughout my journey,” she said.
In an exclusive interview with Analytics India Magazine, Ruchi discussed her Kaggle journey and how she got interested in the field of data science.
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AIM: When did you start taking interest in Computer Science?
Ruchi: I was fascinated by technological advancements from an early age. Over the years, Mathematics and Science became my strongest subjects, so I was inclined towards engineering. Coding my first 1000+ lines project in Java at the age of 13 was an experience of its own, and that’s when I knew that this was the area I wanted to specialise in.
AIM: How did your internship help you land a job?
Ruchi: During my undergraduate program, I wanted to observe how each domain of Computer Science functions in the real world and learn more about various technology stacks used to tackle existing problems. Therefore, I shortlisted fields of my choice, gathered intermediate-level knowledge and worked on several projects before applying to these internships. As a result, I ended up spending most of my internship period working on real-world projects and learning about the nitty-gritty of several different technologies. This helped me shape my profile uniquely and effectively.
Naturally, I had a lot to share about my learnings and relevant projects, which helped me land my first job at a multinational company, Colgate-Palmolive.
AIM: What made you interested in Kaggle?
Ruchi: The Machine Learning and Deep Learning Specialisation courses (on Coursera) taught by Andrew Ng were the starting point of my Data Science learning journey. The potential of AI to solve day-to-day problems piqued my interest. With my constant zest for learning and exploring newer topics, I was self-motivated to go above and beyond.
The next milestone was joining Kaggle. Little did I know that one day could’ve had such a lasting impact on my professional career! It all started with a data scraping project and a project idea that I wanted to pursue. The community support was overwhelming and positive.
The constructive feedback I received helped me do better and kept me motivated at the same time. Initially, I was hesitant to be actively involved in the community, but that changed when I realised everyone here had an uplifting mindset. Over time, I grew comfortable with the platform and decided it was time to contribute not just datasets but also code and compete. Staying active on the platform has helped me more than I can mention. The practical insights of Kagglers are something we won’t find anywhere else! It opened up several avenues for me. From doing courses to project-based learning, from Data Scraping to Private AI, from being a newbie to becoming a Data Scientist, I’ve enjoyed every part of my journey!
AIM: What makes a good data scientist?
Ruchi: Technical expertise + business acumen + 2 x patience + curiosity + ability to simplify = A good data scientist.
I’d like to add that this is just my opinion, and everyone in the field is doing a great job at making the world a better place!
AIM: What does it feel like to become a Kaggle Grandmaster twice?
Ruchi: Consistently learning and continuous improvement were my only two goals when I first joined Kaggle. Even if that meant sparing only a few minutes a day. In 2021, I met that goal by contributing to Kaggle for all 365 days of the year! In the process, becoming a 2x Grandmaster (Datasets in 2020 and Notebooks in 2021) was a cherry on top. I was extremely humbled and ecstatic both times!
AIM: What is your advice for beginners in Kaggle?
Ruchi: Innovate, persevere, take a break, repeat. This was my go-to mantra as a beginner in the field, and it helped me immensely. However, something entirely different might work out better for you. Find your mantra. Find your passion. Do great things, and don’t be too hard on yourself!
We should hone our competitive side, but at the same time not forget about the underlying goal: learning.
AIM: What’s your thoughts on the future of data science?
Ruchi: The best part about Data Science as a field is that it’s an intersection of researchers, engineers, and a whole lot of people, wanting to solve real-world problems and bring a positive change in society. There are so many untapped problems from several domains that could be addressed using Data Science. I’m thrilled about what’s to come.