I went a similar route. What did it for me was conceptual analysis. You know how to program already. Maybe you did object oriented analysis? Once I understood,

I went a similar route. What did it for me was conceptual analysis. You know how to program already. Maybe you did object oriented analysis? Once I understood, that creating a sample space can be understood as creating a class of the samples you expect, and then putting the attributes on an axis of a space, it clicked. Depending on the space, you then can visualize the outcome probabilities as weight of a point or area in the space. A random variable is then a mapping of that space to R etc. Or that differentiation is basically a way to get the slope of a function’s tangents at any point (it is differentiation of a function!) and its converse, integration, helps you find the area under a function graph. The important point is here that many interesting points can be mapped to doing this (for example calculating the probability from A to B, if you decided to represent it as area). I had worked with UML, OPM (object process methodology) and BFO (basic fundamental ontology) before. Asking “what is it (attributes, parts) and what can I do with it helps me a lot. The most important trait however was coping with frustration, sometimes it took me months to understand a concept.