I have the lottery project for programming practice, but that would require much more statistics knowledge than I have to come up with the algorithm and to interpret the results.
Another I have might not require as much stats knowledge. Possibly some, because it’s looking for correlations, but not nearly as heavy on the numerical analysis. A bigfoot search.
Bigfoot is, of course, a legend of a large hairy primate running around North America. The big problem with that- there’s really no way a large primate could maintain a breeding population large enough to account for the legend, but small enough to escape scientific notice for so long.
But- while it’s rare, humans do occasionally get large and hairy enough to be mistaken for a bigfoot(and even normal sizes might be so identified under some viewing conditions). Humans also occasionally just disappear never to be seen again. It seems likely to me that if any Bigfoot sightings are actually of a large primate, Homo Sapiens is by far the most likely source.
So, my thought is to comb through missing persons data, and bigfoot sightings, and see if there are any good correlations between the sightings and the last known location of the missing person. Focusing mostly on any missing persons with gigantism and/or hypertrichosis. I won’t reject non-mutants from the analysis entirely, if someone
Is the Bigfoot legend based on humans with various mutations that left or were ejected from society? A handful of mutant humans could escape capture or positive ID in the wild indefinitely, and they don’t require a large breeding population that has somehow escaped notice.
Again, like the lottery idea, I’m not expecting much. But it would be useful practice in writing code to do data analysis, and has the added advantage of requiring less additional knowledge to implement it, so I can focus mostly on the programming itself.