Lavanya Gupta

Lavanya Gupta’s interest in data science started while completing her Bachelor’s of Engineering in Computer Engineering from Thapar Institute of Engineering & Technology in India, where she worked on projects that involved data analysis. It was furthered while working various project management roles at Ericsson Global India, where she was more interested in analyzing data and translating it into insights that could support decisions and be communicated clearly to stakeholders.

“It was this combination of academic and industry exposure that ultimately led me toward data science,” she explained.

With this realization, Gupta began researching graduate programs and UBC’s Master of Data Science (MDS) program stood out for her because the curriculum strikes a balance between statistical foundations and practical application. As well, the capstone project, where you work on a real problem with an actual industry partner, was a big draw.

“I also reached out to a few alumni before applying, and the consistency in what they said was hard to ignore. 

Everyone spoke about how the program genuinely prepares you for industry, not just academically but in terms of how you think and approach problems,” she added. 

If a program like MDS did not exist, Gupta saw herself picking up data science skills on the side through courses and self-study. 

“You can teach yourself to use the tools, but developing the intuition to know when and why to use them is a different thing entirely. MDS compressed what would have taken years of fragmented learning into an immersive, rigorous experience, and that was not something I was willing to pass up,” Gupta said.

Once settled in the program, Gupta listed DSCI 522 (Data Science Workflows) and DSCI 524 (Collaborative Software Development) amongst her favourite classes. “Before taking them, I focused mostly on getting an analysis to work. Daniel (Chen) and Ilya (Musabirov) showed us that writing code is only part of the job. Making your work reproducible, well-documented, testable, and easy for others to use is just as important.”

In addition, Gupta said the program has been thoughtful about integrating LLMs and AI into the curriculum, particularly through courses such as Privacy, Ethics, and Security. 

“What I found most valuable was the focus on treating LLMs as support tools rather than substitutes for critical thinking. We were consistently encouraged to question outputs, understand their limitations, and be intentional about when they genuinely add value versus when they become a crutch,” she elaborated. “That mindset will be important in my career as these tools continue to evolve. The ability to use them responsibly while maintaining strong judgment will matter more than simply knowing how to use them, especially as outputs become increasingly convincing on the surface.”

With the program now over, Gupta reflected that what helped her succeed was staying curious and consistent, but also learning from the people around her. She mentioned how MDS brought together individuals from a wide range of professional and academic backgrounds, and those perspectives often challenged how she approached problems. “Some of my most valuable learning came not from the coursework itself, but from seeing how others thought through the same problem differently.”

After graduation, Gupta is pursuing Data Scientist, Machine Learning Engineer, or AI Engineer roles. 

“What draws me to these positions is working at the intersection of data, technology, and decision-making, whether that means building machine learning systems, evaluating AI models, or translating complex data into insights that can guide real actions. I am particularly interested in roles where there is a balance between analytical rigor and practical implementation,” she noted.

Lavanya’s Top 3 Tips on Succeeding in the MDS Program:

1. Read the lecture notes. Start building cheat sheets early instead of leaving them for later. The program moves fast, and you will not have time to create them from scratch when things get busy.
2. Say yes to things. Ask questions, and do not overthink reaching out for help. Everyone is figuring things out at the same time, and you learn a lot faster when you actually lean on the people around you. What you get out of the program really depends on how much you put into it.
3. Protect your sleep and actually take your breaks. There will always be more work waiting, but you cannot run this program on empty. Rest is not a reward; it is part of staying consistent.

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