U of T Statistical Sciences: What program are you graduating from, and what was your undergraduate program?
Sara Golestaneh: I recently graduated from the Master of Applied Computing program in the Data Science Concentration.
Were you always interested in data science?
I became interested in computer science back when I was in high school and developed a Snakes and Ladders game as part of a computer coding club. After that, I gradually became interested in more complicated topics such as how can we create a brain that is smarter than a human brain through computer programming. That shifted my interest towards machine learning and AI, which is why I did my undergrad in computer science with a focus in AI.
For my undergraduate studies, I completed a bachelor’s in computer science (Focus in AI) at U of T with a minor in mathematics.
Near the end of my undergrad, I took part in the Professional Experience Year Co-op Program (PEY). I worked with a company that gave me the chance to implement a simple content-based product recommendation engine using TF-IDF. That was the first time I experienced the joy of applying my statistical knowledge to a real-world problem. It was the main trigger for me to apply to the Master of Science in Applied Computing program.
What do you like about the MScAC program?
The MScAC program prepared me for working in the industry while conducting applied research. There were also many interesting courses offered through the program. I especially enjoyed Data Science Methods, Collaboration and Communication. It was an applied course where we did many interesting projects and learned more about what data scientists do in the real world.
How has the MScAC prepared you for the future?
While traditional master programs in computer science are often completely research-based, the MScAC program gave me the chance to do research alongside working on a real-world problem at the same time. It also helped me gain insight into how state-of-the-art techniques in machine learning can be applied in industries.
What are your academic interests, and what makes you passionate about your field?
I’m interested in machine learning, more specifically, Natural Language Processing (NLP). I completed my applied research project for the MScAC in that area as well. There are many challenges in NLP that yet need to be addressed and any discovery or advancement in NLP will have a great impact. That’s what makes this field so interesting to me.
What advice do you have for students that are interested in enrolling in the MScAC?
The MScAC program is a gateway from the academic world to industry. I really encourage students, who are interested in getting their master's degree in a field that combines both research and industry, to apply. My field of study in the MScAC program was data science. For those interested in data science, you need to have a very strong background in statistics. You also need to be able to communicate well and to clearly convey your knowledge to non-technical people.
To become a great data scientist, you also need to stay on top of your field and read the most recent papers to get inspiration for your own projects.
What are you most looking forward to after graduating?
I am currently working as a full-time data scientist at SOTI. That’s the company I did my research internship at. However, I am thinking of applying for a PhD in natural language processing. I feel like there are still a lot of things to learn and discover in this field.
What are your career interests? Also, do you have a "dream job" in the future?
I am planning to continue my career either in the field of NLP as a researcher or as a data scientist with a focus in NLP. My dream job would be to have my own start-up in this field one day.
Outside of work, what do you enjoy doing?
I enjoy playing simple made-up songs on my ukulele or the piano. And composing songs using apps like BandLab. I am also an anime fan. You can listen to my songs on SoundCloud.
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