Abhishek Sinha completed his undergraduate degree in computer science engineering at PES University in Bangalore, India. He also explored the impact of contamination in big data on model misspecifications as a research assistant for Arnab K. Laha at the Indian Institute of Management, Ahmedabad before enrolling in the M.S. in Data Science program at Columbia University.

The 2022 alumnus was an active member of the Data Science Institute (DSI) Student Council and the Columbia Debate Society. He served as a graduate student assistant for the Northeast Big Data Innovation Hub and was on winning teams during the DSI and Dataiku Fall 2020 hackathon and the inaugural MAP Project48 competition.

Sinha looks forward to joining Eightfold as a product analyst in data science and analytics and shared a bit about his background, interests, and Columbia experience before commencement.

How did you become interested in data science?

Back in my undergraduate program, I interned at Amadeus Labs at the end of my second year. I worked on a chatbot. It was my first exposure to any sort of work with data and piqued my interest in it. After this, I took up quite a few data-related courses that went on to reinforce this interest.

Why did you choose to come to Columbia for graduate school?

I chose Columbia because of its focus on cross-institute collaboration and applying data science in the real world to make an impact. The fact that it’s in New York and gave me access to Columbia’s exemplary alumni network was simply an added bonus.

What have you enjoyed most about living in New York City? 

To me, the best part of New York City is its density. There are a million things to do at every corner—the restaurants, the pubs, and the experiences. Live music has become a personal evening favorite and exploring the diverse range of music New York City has to offer. I love that this city truly never sleeps.

How did your undergraduate experience at PES University prepare you for Columbia’s M.S. in Data Science program?

My undergraduate experience gave me a robust foundation in programming. I would definitely recommend taking a few courses on programming or linear algebra to be better prepared.

Speaking of courses, what was your favorite course during the program?

Time Series Analysis by Haoran Li was a great course that gave me a thorough grounding in all time series concepts from both a statistical and practical programming perspective. 

Tell us about your capstone project.

Our capstone project was to quantify the impact of climate change on socially-vulnerable people in the U.S. under the guidance of Marco Tedesco. We created the Socio-Economic Physical Housing Eviction Risk dataset to account for the financial vulnerability associated with the housing market due to climate hazards across the U.S. using R. This dataset will be hosted at NASA’s Socioeconomic Data and Applications Center. We also modeled the relationship between evictions, climate, and vulnerability factors using machine learning for feature selection and prediction, making key considerations for explainability.

How did the pandemic impact your Columbia experience?

The first half of my program was primarily online. While virtual coursework was fine, it was extremely difficult to stay engaged and maintain a strict schedule. It also impacted my social experiences in that it took a lot of active effort to interact with anyone in the cohort. This ended up pushing me to participate in a boatload of activities that would allow me to meet people that I would not have met otherwise. 

Did you complete an internship during your studies?

I interned with NBCUniversal for a little under a year—full-time and, later, part-time—as a part of their Customer Journey team for Peacock Decision Sciences. Maintaining productivity and pulling my 20 hours was super challenging during my second semester when our course load was extremely heavy. I distinctly remember an instance when I was applying exactly what I was learning in my statistical inference course to the work I did at my internship. That kind of opportunity to apply what I was learning to my practice is exactly what I hoped to gain.

This interview has been edited and condensed for clarity.

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