Ameya Karnad received his bachelor’s degree in information science and engineering from B.V. Bhoomaraddi College of Engineering and Technology in India. He worked as a software engineer for Hewlett Packard Enterprise before pursuing his M.S. in data science through the Data Science Institute (DSI) at Columbia University. Today, the 2020 alumnus is a software engineer at Oracle in the Philadelphia area. He is also passionate about teaching and is adjunct associate faculty for a data science consulting course in Columbia’s Applied Analytics Program this summer.
What piqued your interest in data science?
It was actually a combination of a movie and a course I took in my undergrad. A dialogue from the movie Captain America: The Winter Soldier first piqued my curiosity for data science. Around the same time, I took a course on data mining which gave a brief introduction to machine learning. The course made me realize that this is what I wanted to do.
Why did you choose to come to Columbia for graduate school?
I am personally interested in the applications of data science and machine learning algorithms in different domains. DSI’s program provides a lot of scope for engaging with different departments across the University. For example, I took a course on data science and public policy from Tamar Mitts of the School of International and Public Affairs during my second term which helped me understand how policymakers use data science algorithms to tackle the problems of the 21st century. The course teamed up two students with a data science background and two students with a public policy background and looked at how we could solve public policy using data science. We worked on four different projects throughout the semester.
Which other courses did you enjoy?
My favorite course at Columbia was Algorithms for Data Science taught by Eleni Drinea. This course set the foundation for all other courses that I took in subsequent semesters. My current work at Oracle involves me working on the concepts I learned in that course.
Tell us about your internship opportunities during your DSI studies.
During the summer and fall of 2019, I worked as a research associate at EdLab, which was a part of the Gottesman Libraries of Teachers College. I got this opportunity thanks to the DSI Summer Scholars program. At EdLab, I had the opportunity to work on lots of exciting problems like the recommendation of scholarly articles, social network analysis of research publications, etc. This helped me cultivate a research mindset and out-of-the-box thinking while trying to solve problems.
What was your capstone project?
My capstone project, Editorial Classification, was in collaboration with Bloomberg. I was teamed up with Aastha Joshi, Sarang Gupta, Nirali Shah, and Ujjawal Peshin. We were mentored on the DSI side by Smaranda Muresan and by Daniel Preotiuc-Pietro and Kai-Zhan Leeon the Bloomberg side. The project was to build a classification system to distinguish regular news articles, which are based on ground reports, from editorial and opinion articles. We used various machine learning, text, and deep learning algorithms to perform this task.
What else did you gain from your DSI experience?
I think the courses at DSI have not only increased my technical skills in data science and machine learning, but also helped me become a better problem solver. It has also made me realize the importance of the reporting and presentation of results. Also, the curriculum focused a lot on the ethics of data science, which is quite important in the field today.
Do you have any pointers for prospective students?
During my undergrad, I took various courses on algorithms, data mining, databases, business intelligence, etc. But the one course that I would definitely recommend to prospective students would be data structures. I think that understanding different data structures and working with them before coming to Columbia helped me tackle my courses. Also, knowledge and experience in working with a couple of programming languages, preferably Python, would help as well.
— Sharnice Ottley