Statistics with Sasa

Today's been a big day in the stock market . Lots of people have lost a lot of money, and a lot of people are understandably really upset . Here's a quick breakdown of what's happened so far A subreddit called /r/wallstreetbets  (visit at your own peril), which has exploded in popularity recently and has over 5 million subscribers (and counting) got really excited about three stocks: GME (Gamestop), AMC (the movie theater place), and BB (Blackberry). Gamestop was the main stock.  Yes, I know all three companies are doing terribly in the real world. I won't go into why they got excited about the stocks here.  They convinced a lot of other people to buy the stocks and they did well. Really well. Take a look at their Yahoo Finance pages and look at their 1 month price charts (then ignore the past two days). GME , BB , AMC Everyone got in on it, and I mean it. When a lot of people buy a single stock, the price rises. It turns out, this was hurting a lot of Hedge Funds and I

 As you might have noticed, I post rather erratically and infrequently. There's a lot of reasons why, but the main reason why is that writing is hard. Behind the scenes, I often spend 40+ hours on some of my blogposts between idea generation, data cleaning, learning about the topic, analysis, and writing. Even worse, frequently I spend a lot of time on an idea and find that my results aren't worth writing about (uninteresting results, messy data etc.). I've currently got a "Blog" folder on my computer with dozens of folders with ideas with input data and code analyzing said data, but only around 10 published blogposts to show for it.  This is a hobby that I do for fun, but I would still like to see the blog grow (and it kind of has), and I recognize that's probably not going to happen without some consistency in posts. Some of the more successful bloggers I know  blog far more frequently (I'm talking weekly), but that isn't exactly a pace I can sustain

Have you ever thought about bringing cold, hard statistics to one of life's greatest artistic joys? Well fear not, because in our increasingly data-driven world, our analyst friends are hard at work attempting to statistisize (numerize?) everything you can think of, so we can analyze and therefore optimize it. One of the art realms that is increasingly falling under the purview of data science is music. We all benefit from it in the form of curated daily Spotify playlists and Pandora stations that allow us to find new artists and songs.  I was recently able to get my hands on a Spotify dataset  that contains data on over 160k tracks dating from 1921 through December 2020. Aside from containing some basic features like track name, duration, and release date, it also contains some advanced metrics as calculated by Spotify like "track positivity" (is it a sad, depressed song, or a happy, positive song?), "danceability", "energy", "speechiness" (