My first data science job was in a small real-estate startup in Singapore. While I was completing graduate school in Stanford, like countless others in Silicon Valley, I became enamoured with tech. I had befriended a number of people in the tech community in the Bay, and one of them connected me to Darius Cheung, founder of 99.co.
Another common way I hear of people getting started in data science is through bootcamps. These have the advantage of specialized, practical training, and they're often connected with larger companies.
Q: Looking at your journey as a Data Scientist, what knowledge, skills, experience have contributed the most to your progression?
In terms of formal training, graduate-level statistics lessons and Andrew Ng’s machine learning course at Stanford provided a good theoretical foundation.
Programming-wise, I used Matlab and Python a fair bit for analysis, and was extremely fortunate to have a Python mentor, Ariel Rokem. I also participated in hackathons regularly and picked up a few different languages throughout the stack, which proved handy later on when I had to build my own APIs, or prototype certain tools and visualizations.
At the end of the day, experience on the job was and continues to be the most valuable experience. Nothing like baptism by fire!
