Former Mechanical Engineer at BP
Fiji/Thailand/South Korea/Bay Area
Tell us a bit about yourself.
I rejected tech careers as a kid after visiting my dad’s office and seeing how he had to sit in front of a green and black screen all day, but I — of course — didn’t realize that in a very short time we’d all be staring at screens all day, no matter what we did for a living. I joined oil & gas (the “dark side,” as some engineering friends called it) for global adventures, but never felt at home in the culture and never made it away from the Gulf of Mexico. The physical scale (literal hugeness) of the technology was always awe-inspiring, however. A few years ago, we bought licenses to software that would predict equipment failures and learn to run the control board. I was intrigued, and after researching, discovered that it was just matrix math (linear algebra) at the root of these programs. Shortly afterward, my brother suggested I get into this thing called data science.
At what school and in what concentration did you receive your undergraduate degree?
I studied mechanical engineering and international affairs at Georgia Tech. Go Jackets!
Why did you choose the I School?
Several reasons: my family has migrated to the area, thus it’s where I want to work and live; UC Berkeley’s great reputation and extensive contributions to the computing and data science fields; its proximity to the tech capital of the world; the richness of the education — it’s not just math and algorithms (though I like those); and, most importantly, the interactivity amongst classmates and the faculty through its online format.
What is the I School's advantage?
The people are fantastic: the instructors really care and even the austere ones volunteer to give extra office hours around challenging assignments. Right now I’m in Natural Language Processing with Deep Learning, and it’s supposedly spring break, but the instructors have added three office hours to the week. I’ve gained so many insights about data science and industry just through conversations and shares on our school-wide collaboration tools. Plus, I’ve had several job interviews through referrals by fellow students, and I’ll be working overseas for the summer as a result. Also, if you don’t have a project portfolio to display your worth, you will have a nice one after just a few semesters. Lastly, the live sessions bring you and your classmates together and keep you motivated.
What has been your favorite class at the I School and why?
Last semester, when I took Applied Machine Learning concurrently with Fundamentals of Data Engineering, was awesome. The level of excitement I got from writing and then executing algorithms to learn and predict was highly unexpected. Then to combine that with writing scripts that built pipelines to take data from the web, to data lakes, to parquet tables, to data structures in Python for some machine learning, and finally to storing the results in a distributed file system… slick! Natural Language Processing with Deep Learning has been very rewarding as well. After a lot of focus, I feel like I have a low-level understanding of neural models in TensorFlow (low is good in this case).
What are your future plans?
After this summer, working in the “algorithms lab” of a company in Seoul and taking Experiments and Causality, I plan to be in the Bay Area taking Machine Learning at Scale and Capstone, and trying really hard to get in with an autonomous driving group or something of that sort. I am a mechanical guy after all, so it’s hard to completely shake the spatial reasoning and deterministic formulas (statistics and programming are quite abstract, in comparison). All of that is unless I leave my heart in Korea.
Do you have any advice for aspiring information professionals/data scientists?