My question requires a little background which I will give here:
I'm heading into the 2nd year of my Epidemiology PhD program. I work on vaccine trials/infection control using simple non-linear math models and love it. The majority of in-department modeling courses have been survey courses focused on translating epidemiological concepts into models, only briefly touching on the math used for implementation.
I've signed up for some introductory courses in the econometrics department (with my department's permission) which cover bayesian statistics, pdfs, importance sampling etc. I chose courses in the econometrics department as I thought they would be more applied and have more students at a similar math level to myself.
1) My main anxiety is that I've always been a solid B grade math student. I learn best through project-based learning and hands-on implementation. Formula based lecturing and proofs have been very difficult for me and I find that subsequently I don't retain much.
2) I've been able to learn more difficult mathematical concepts using computational software (I am a proficient R and MATLAB user) and doing self-teaching tutorials. Concepts that have previously been explained to me in math theory 4-5 times 'click' when I code them out and can experiment with sampling, seeing actual results. (for example, mcmc metropolis-hastings)
3) What would be your recommendations be for devising a study plan for these classes, and my future classes, which maximizes learning the relevant mathematical concepts given this 'learning style'? Were there any websites, types of courses, or textbooks/exercise books that taught difficult concepts with code but didn't overly rely on pre-built functions?
People in other departments either seem to grasp concepts effortlessly or stop trying to learn them. In addition no one else else in my cohort or the years above work on these type of questions or models so I have no one to ask for advice.
I am under no delusions about my mathematical abilities here and have no ambition to pursue math/statistical/machine learning research. My goal is to reach a level where I am able to fully comprehend the tools used in my particular field and be able to implement and critique them at a 100% competent level. Thanks!