I had a hard time finding any meaningful data (about music at least) that i wanted to work with for this preliminary excel exercise. I eventually found a database of 70,000 songs for DJ’s to use so potential clients can easily browse the music they could be entertained with. There was a subset of the top 1500 party songs with that set, so i decided to use it being it was more digestible for the charting exercise.
I made a few simple charts using this database of songs, it isn’t exactly data in the traditional sense so there was not a lot to do with averaging or dealing with numbers. I was able to organize it in ways that showed me how many songs in each genre were in this top 1500 list and how many songs by each band were in the list. This could give me an idea of what era’s of music created the most timeless party music, the overall top genre was surprisingly 80’s rock.
While searching for data I came across alot of complex API’s that I could tell would be useful if i knew how to make use of them. I found an interesting music data site EchoNest as well as Music Machery. Music Machinery has a long list of music API’s including spotify, last fm and whosampled. It made me think i could somehow use the whosampled.com API to do a project about the most sampled music and the songs with the most sample strings ( samples of songs that contain samples of other songs – this could go on for a many levels of sampling music).