Don’t think this question has been asked before; I’ve just come across a question on how much math one has to know for a career in GIS. As a geography student I’m wondering how much computer science I’d need to know if I’m planning on being a GIS analyst, particularly as nearly any answer I read on here quickly delves into how to solve something through programming and it’s beyond me. Furthermore, I’ve noticed both on here and offline that most get into the GIS line of work through computer science rather than geography. I’m nearing the end of my undergrad and will be doing an MSc in GIS and remote sensing but I have also been told by a career advisor to look into a conversion course in computer science before I do the Masters to give me an edge.
However, I am apprehensive of a few things, the main reasons for my qualms being:
Aptitude. I’m wondering if I, as just a French/geography student, am cut out for computer science. More specifically, how much math is involved? While I am not averse to it (and have been using light statistics and quantitative methods increasingly in geography for my final year) it’s probably not my strongest suite. What other skills are crucial? Does computer science require more abstract of concrete thinking, as I think I might slightly lean towards the former?
The actual point? Though it’s a more applied Masters, I’ll be doing a little programming for the course. Would doing an entire year of computer science be an addition to me, not only in preparation for the Masters but also long-term, keeping in mind that it’s not only more fees but also will take a lot of effort? It’s known to be one of the most intense post-grads in the university. It is, after all, teaching the bulk of a three-year Bachelor’s in one year. I’m wondering if it will pay off. If it would be beneficial, I'm willing to spend an extra year in education.
To give an idea of what the course will entail, here’s the list of modules: • Introduction to computer systems (both semester 1 and 2) • Algorithms and data structures (both semester 1 and 2) • Databases • Structured programming • Discrete structures • Web information processing • Software testing • Multimedia technology • Software engineering and software processing • Operating systems