The thing about my way of teaching stats is that my cool, novel examples get old really fast. Like, pop culture is on a thirty-day cycle. Anyway, YEARS ago, 2014 to be exact, I blogged about illustrating bimodal data distributions using baby name data from the Social Security Administration. Specifically, when an older name comes back into vogue, it has a bimodal distribution when you plot time on the X axis. I stand by this example. However, all of the data illustrations come from 2014. When our traditionally aged college students were 6 or 7. Which makes this example feel a little stale. However, here is some data, courtesy of Redditor fevaah , showing old names that are back in vogue through 2025 ! And Eleanor, Violet, Nora, and Hazel are here, illustrating bimodal distributions for a new generation. If you used my old baby-name data, this is a quick, easy refresh for your lectures (or a fun new addition if you didn't). If this idea was help...
This article, written by Matt Daniels for The Pudding, allows you to play around with a whole bunch of Spotify user data in order to generate visualizations of song popularity over time. You can generate custom visualizations using the very interactive sections on this website. For instance, there is a special visualization that allows you to finally quantify the Biggie/Tupac Rivalry.
So, data and pop culture are my two favorite things. I could play with these different interactive pieces all day long. But there are also some specific ways you could use this in class.
1) Generate unique descriptive data for different musicians and then ask you students to create visualizations using the software of your choosing. Below, I've queried Dixie Chicks play data. Students could enter their own favorite artist. Note: They data only runs through 2005.
Is this representative of all data? What does he mean by "normalize the data" as a way to correct the data? Where could we collect data as to have a more representative sampling? Would Sirus skew older? What about iTunes?
3) Using data mining/archival data to generate insights into research questions.
Here, the question explored in this article is, "What is the difference between a flash in the pan song versus a song for the ages?".
Here, data from 2013 hits has been tracked. And it founds that the post-hit plateau is a good indicator of music that will have longer staying power. Here, event though Daft Punk's Get Lucky peaked much higher than Onerepublic's Counting Stars, Counting Starts has a higher plateau. Also, note that with this interactive piece, students could select any number of songs to compare.
So, data and pop culture are my two favorite things. I could play with these different interactive pieces all day long. But there are also some specific ways you could use this in class.
1) Generate unique descriptive data for different musicians and then ask you students to create visualizations using the software of your choosing. Below, I've queried Dixie Chicks play data. Students could enter their own favorite artist. Note: They data only runs through 2005.
2) Sampling errors: Here is a description of the methodology used for this data:
3) Using data mining/archival data to generate insights into research questions.
Here, the question explored in this article is, "What is the difference between a flash in the pan song versus a song for the ages?".
Here, data from 2013 hits has been tracked. And it founds that the post-hit plateau is a good indicator of music that will have longer staying power. Here, event though Daft Punk's Get Lucky peaked much higher than Onerepublic's Counting Stars, Counting Starts has a higher plateau. Also, note that with this interactive piece, students could select any number of songs to compare.