I studied Computer Science at the University of Wollongong, then Biomedical Data Science at Nanyang Technological University. On paper, the two sound closely connected.
Changing fields still means catching up on things you haven’t learned yet.
Being able to load and process a dataset is a start. I also want to know what its fields mean, how it was collected and which differences matter. Without that background, I can write the code quickly and still be asking the wrong question.
I find it easier to admit when I don’t understand something. Looking up a term before moving on takes less work than finishing a task with a vague idea of what it means. That sounds obvious, but it’s easy to forget when trying to get through things quickly.
AI makes it convenient to ask follow-up questions about an unfamiliar subject. I treat it as a useful starting point. If I’m going to base a decision on the answer, I still want to check the underlying material, especially when the answer sounds confident and I know very little about the topic.
That period of study made me more comfortable with being a beginner at something. My computing background was useful. There was also a lot of new material to learn, and no real benefit in pretending I already knew it.