What I mean is if you have the resources (time, energy, etc.) to do so, consider trying to get the data where the script returned ‘0’ values because the source you used didn’t have that bit of data. But make it clear that you’ve done independent research where you find the figures yourself, so that the user realises it’s not from the same dataset. And failing that, e.g. if there just isn’t enough info out there to put a figure, state that you looked into it but there isn’t enough data. (This lets the user distinguish between ‘maybe the data just wasn’t in the dataset’ versus ’this info doesn’t even exist so I shouldn’t bother looking for it.)
I think the big problem with trying to determine “related jobs” is that, more often than not, in the actual job market, the relationship between similar jobs is in name only.
Sure it would again be more resource-intensive, but I was thinking you could figure out yourself which careers are actually related, or ask people in those fields what they actually think are the core parts of their job and which others jobs they’d relate it to.
What I mean is if you have the resources (time, energy, etc.) to do so, consider trying to get the data where the script returned ‘0’ values because the source you used didn’t have that bit of data. But make it clear that you’ve done independent research where you find the figures yourself, so that the user realises it’s not from the same dataset. And failing that, e.g. if there just isn’t enough info out there to put a figure, state that you looked into it but there isn’t enough data. (This lets the user distinguish between ‘maybe the data just wasn’t in the dataset’ versus ’this info doesn’t even exist so I shouldn’t bother looking for it.)
Sure it would again be more resource-intensive, but I was thinking you could figure out yourself which careers are actually related, or ask people in those fields what they actually think are the core parts of their job and which others jobs they’d relate it to.