This seems very useful. Personally, I would also be interested in:
Rate of improvement: What level of skill or advancement would be considered poor, mediocre, and exceptional after X months/hours? This would be especially valuable for careers involving soft skills, in which it is often hard to know how you should be measuring your performance or what a good rate of improvement/advancement looks like. (For AI research, it could be something like, “after X hours of learning this concept, it would be considered poor/fine/great to score somewhere in the Y percentile of this machine learning competition”.)
Related careers: If you mostly enjoy a career except for one or two specific components, what are other similar careers that may be a good fit?
Thanks for your comment. For your first point, I definitely agree in an ideal world that benchmarks for improvement would be useful but I would be hesitant for a few reasons.
Firstly, you face quite a risk of putting people off a certain career when really you don’t have the certainty to give that advice (especially when I am not a specialist in the field), and that could be really damaging and maybe not that useful. Secondly, these things are generally really context specific for how good X amount of progress is in Y amount of time. Eg. for your example, it could depend on pre-existing technical background, the amount of guidance and support you received while learning etc. - and I think this would be hard to quantify in a useful way.
Your second point is a really good one I think and something I would like to include—I suppose if I reach the point of creating a more comprehensive collection then it should be easier to refer between them.
This seems very useful. Personally, I would also be interested in:
Rate of improvement: What level of skill or advancement would be considered poor, mediocre, and exceptional after X months/hours? This would be especially valuable for careers involving soft skills, in which it is often hard to know how you should be measuring your performance or what a good rate of improvement/advancement looks like. (For AI research, it could be something like, “after X hours of learning this concept, it would be considered poor/fine/great to score somewhere in the Y percentile of this machine learning competition”.)
Related careers: If you mostly enjoy a career except for one or two specific components, what are other similar careers that may be a good fit?
Thanks for your comment. For your first point, I definitely agree in an ideal world that benchmarks for improvement would be useful but I would be hesitant for a few reasons.
Firstly, you face quite a risk of putting people off a certain career when really you don’t have the certainty to give that advice (especially when I am not a specialist in the field), and that could be really damaging and maybe not that useful. Secondly, these things are generally really context specific for how good X amount of progress is in Y amount of time. Eg. for your example, it could depend on pre-existing technical background, the amount of guidance and support you received while learning etc. - and I think this would be hard to quantify in a useful way.
Your second point is a really good one I think and something I would like to include—I suppose if I reach the point of creating a more comprehensive collection then it should be easier to refer between them.