The reasoning framework for making life and career-defining decisions
If choosing the appropriate degree course or program to study Artificial intelligence(AI) at University was straightforward, this article would end here. But it doesn’t.
This means there is more to it than just deciding to study AI at a University, especially if the objective is to become an AI professional after a few years.
The image above depicts keywords and topics within AI course titles of MSc, PhD, Diplomas and BSc degrees at various universities across the United Kingdom. Each of the courses I went over to create this word cloud had even more keywords in their respective modules.
Becoming an AI professional through the academic route is not entirely straightforward.
Suppose you don’t give your university course selection a considerable amount of thought. In that case, you might find yourself in a data-centric course when your interests lies in the hardware-focused aspect of AI, which means you could have been best suited for a course that covered Robotics or Mechanics.
In this article, I reveal a practical framework that presents an adaptable approach to selecting an appropriate AI-related course at most higher institutions. This framework can be used for yourself, or feel free to share it with young AI enthusiasts.
How do you make a decision about which AI course to choose at University?
There is no specific answer to the question above. A practical approach to selecting AI courses at University is to leverage different methods of reasoning that consider your aspirations (what and why), capabilities (how) and environment (where).
The ‘what’ and ‘why’ take into consideration internal and external motivations that drive you to pursue a specific goal. Even more specifically, the ‘what’ process ensures that you are not just chasing an arbitrary goal; instead, you understand what it is you want to get out of a 30 to 50-year career within AI, with milestone goals also considered as well.
Be the first to comment