Tiffany Zhu has always enjoyed solving puzzles; solving the Rubik’s Cube was one of her favorite childhood pastimes. An ability to problem solve and think about challenges in different ways led her to a career in software engineering and data science. She worked for EAB, an education technology and research company, before enrolling in the M.S. in Data Science program at Columbia University. Today, the 2021 alumna is a cognitive developer for IBM.
What helped to pique your interest in data science?
When I was an undergraduate [at Carnegie Mellon University], I took an introduction to machine learning (ML) course. I thought it was the coolest class because, going in, I had no idea what ML meant, and I found out it perfectly combined my interests in statistics and computer science. I almost didn’t take it because I thought it would be too challenging for me, but if you find a topic fascinating, then it doesn’t matter because you’ll be driven to succeed no matter what.
How did your undergraduate experience at Carnegie Mellon prepare you for the M.S. in Data Science program?
I took statistics and computer sciences courses. I think taking at least one of each topic would definitely help you succeed at DSI. Not knowing any statistics or anything about programming would make it a bit overwhelming.
Why did you choose to come to Columbia?
I chose Columbia because it is one of the pioneers of data science master’s programs. Also, with big data raising security and ethical concerns all the time, I wanted to make sure I learned the best practices. I appreciated how much Columbia values and champions the ethical considerations of using big data as much as I do. And of course, Columbia allows you the unique experience of studying and living in New York City.
What did you like most about living in New York City?
Although I grew up pretty close to NYC, I never lived in the city. Studying at Columbia and being able to live and study in the city was a unique experience. Columbia is far enough from the busiest part of the city to give you that environment to concentrate on studies, yet close enough that it’s just a short subway ride away to the heart of NYC. I enjoyed being able to easily get on the subway and visit midtown or downtown any day when I didn’t have classes and was free.
What was your favorite course?
One of my favorite courses was Reinforcement Learning taught by Chong Li. It was a challenging class but Professor Li is an amazing teacher and explains everything so well and thoughtfully. The topic is also really fascinating, and we learned how the topics were being used in real projects.
How did your internship experience as a cognitive developer at IBM change or challenge you?
It was a unique experience since it was completely virtual. I learned how to work with a team of people who you’ve never met with in person, and how to put my data science skills to use in real-world situations. My team contributed to the open source community, so it was a fun and new experience to understand more about that community.
What was your capstone project?
My topic was Energy Efficient Machine Learning at the Edge with GE. We essentially explored how much we could compress / quantize training data and still get similar results in machine learning models with uncompressed data. The idea was that if you could still get the same result with compressed data, then this could decrease the amount of storage and energy it takes to run models and thus benefit the environment.
How did the coronavirus pandemic impact your experience during the M.S. in Data Science program?
I moved out of NYC to New Jersey to be with family. Although everything became virtual, I still appreciated how understanding professors were and how much they cared about the students. Some professors even went above and beyond to make sure they had ample amount of time slots for students to sign up for one-on-ones to discuss the course or anything in general.
What else did you gain during your experience at Columbia?
I gained many new lifelong friendships and acquaintances from all different backgrounds. I learned to be more confident with my skills as a data scientist, how to work with a team more efficiently, how to ask for help when things are overwhelming, and how to give help when I’m more experienced in an area.
— Sharnice Ottley