U of T Statistical Sciences: What motivated you to study data science?
Jinda Huang: I initially planned on joining the actuarial science program in my first year, but the data science specialist had been introduced as a new program and it seemed very interesting, so I decided to pursue it.
What do you like about the data science program?
I really like the class size of my courses. In some of my classes, there were eight people, whereas in a statistics class there can be two hundred. It allowed me to talk to professors more and as a result, my communication and problem-solving skills have greatly improved.
I also enjoy programming and the data science program let me practice my programming skills. This is also why I enrolled in the computer science program, and I think in the future I’m going to pursue a career that involves programming.
How did your computer science courses help you with your data science courses and vice versa?
I took a similar amount of computer science courses and statistics courses, and computer science definitely helped since programming forms the basis of data science. The basis for data science is like statistics because you need to know how to collect, clean, and analyze data, but you also need to know how to solve problems using programming methods.
In my opinion, data science requires you to be familiar with your programming languages, such as Python, R, SQL. Learning these languages will improve your logic and help you solve problems more efficiently.
What is your dream job?
My dream job is to be a full-stack engineer, which would include back-end development and front-end data handling. I actually just finished an internship as a full-stack engineer at Ecopia AI where I got to use my data science skills.
What was your internship experience like and what did you learn?
During my Professional Experience Year (PEY) I interned at Ecopia AI in Toronto. It's a start-up that works on high-precision maps. My role as a full-stack engineer was to build a website to help manage the work of the company's employees. This was a very important project because there are large amounts of data that come in every day.
I needed to work on the front-end of the website, using ReactJS to design and develop front-end components. Meanwhile, I also needed to work on the back end, which required me to build and maintain a big database, handling front-end requests. In addition, I had to build a bridge between the front-end and back-end, retrieving and storing data using MySQL on the database.
Through this internship, I improved my programming skills in MySQL, JavaScript and Python. My problem-solving ability and communication ability have been significantly improved as well. And I also learned how to handle large scale data in a short time.
What is your favourite place on campus?
Robarts Library is my favourite spot on the St. George campus. I spend half of my time there. I like to study on the higher floors, also known as the Robarts stacks. From there I can enjoy the beautiful scenery of the campus.
I also like front campus and back campus because I love playing soccer.
Do you have any advice for students interested in a degree in computer science or data science at U of T?
You need to be interested in what you want to study, whether it is data science or computer science. Having an interest is very important because it will motivate you to study hard and go far in your career.
Besides interest, I think you need to be confident if you want to be in the data science program. You do a lot of presentations in this program: you need to present reports and communicate well with your classmates and professors to succeed. So, if you prefer to study by yourself and work alone, I don’t recommend this program.
My final piece of advice is to just enjoy studying and the process of learning. The data science program has a heavier workload than many other undergrad programs.
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