How I’m managing a CS Masters while working full time (Part 1)

This post (and subsequent ones) is my attempt at reflecting on my experiences after a couple of semesters studying for the NUS Masters in Computing, Computer Science Specialisation. Personally, I need to write about an experience before I feel like I’ve digested it properly, so I’m writing things out so my brain can make sense of things.

On a broader level, I’m also writing this for the benefit of other people who may be thinking of the same thing. Before I started this course, I was surprised how little I could find on the web about other people’s experiences — why they had decided to go for further qualifications, how they were managing their time, whether or not they found their courses useful, whether they were enjoying themselves.

I would have found this information really helpful about a year ago, so I decided to also write for people who would be in my prior position. Nothing here is prescriptive, but perhaps it will give you something to consider.

My background

  • I come from the sciences, but not from the “hard sciences” like Chemistry and Physics. I’m a Biology graduate in Zoology and Plant sciences. This meant that for three years I studied topics like how worms have sex, and how most of our genome is actually viral DNA from ancient infections. It was all very fun and intriguing. But compared to a subject like Physics, Computer Science or Engineering, it wasn’t very mathematical or quantitative.

My motivations for pursuing further education

  • After about three years after that event, I started to want a more solid foundation in the fundamentals of computer science. At work, I was working on mostly machine learning projects. However, I could see that most of the value on these projects were created not by applying sophisticated algorithms, but by automating data pipelines and models to run securely and reliably. In other words, the value came from the projects software and data engineering components. The problems I was facing — how to get a machine learning system to run safely and reliably, whether to expose a model’s results as an API or write the results back to a database — were problems with roots in engineering and CS rather than statistics and machine learning.

Eventually the pain of not being able to extend and apply myself to these issues I was facing became quite severe, and so I put in an application to NUS and crossed my fingers.

I work with data in the little red dot