Tuesday April 13th

My first Data Hackathon!

Finding new friends

  • I remembered in LA that I used to hang out in data circles, when I wasn’t hanging out with Haskell or Hacker people, so I started out hanging out with people on the West Coast again.
  • A lot the reason for this was also because I had done this Computational Social Science workshop, and had gotten back into R again, the first programming language I ever tried (my dad studied Economics back when it was a very rigorous Quantitative field, and is a huge Open Source advocate, so we grew up with programmes like R and Octave on a Desktop partitioned with RedHat and Windows. He’s also super into databases and does really crazy stuff with Excel; he’s one of those people who is also always tinkering with stuff, especially if “he found a new open source thing” that looks interesting. These days, he is into photography stuff and editing, so we talk a bit about open source programmes for that, and he even made a greenscreen backdrop out of pvc and was asking me about lighting stuff the other day and has these weird robot cameras that work from his phone that he and my mom will randomly mention when we are chatting sometimes).
  • Anyways, back to R. Soon, it was a little bit of a compulsion. I sometimes had up to 5 (yes, that’s five) R workshops a week, was doing a book club, and just kept going. The community is pretty great. So these days, if I’m not hanging out in some cryptography or blockchain group, I’m usually in an R Meetup (hooray!).
  • Plus, it’s functional, so it scratches that itch. I even opened an issue when I was doing my presentation for the last book club I attended, when I found some broken links in a Graph library. I’m still much better at Python, though (ha). Don’t ask me to do your coding challenges in R, please :)
  • Tonight, I also led a session on Text Mining, which was pretty anxiety-ridden, but it’s such a supportive group, I can’t help but feel grateful for this group, especially during COVID.
  • One of my biggest fears is that after we are back on campus, it will be difficult to meet up regularly with everyone, because most of my friends aren’t local. Oh well.

My first Data Hackathon

  • I signed up for my first data Hackathon. I’m not going to lie; it was advertised as being more educational than competitive, but I had such a terrible first experience years ago with a hackathon in which teams were being made for some Node.js or C sharp project (I can’t remember the details, but it was something about making an app), that I felt incredibly nervous. In the first hackathon I tried to take part in (which was local in Southern California around 2017), I had arrived and asked where the restroom might be (because I had taken the bus to get there, and two train rides), and a group just looked at me and didn’t say anything. Needless to say, I ended up very alone, and after I did the instructional pre-learning part of the hackathon (where they introduce the technology, tools, etc), I left and never returned.
  • But this one was different. It was definitely a very kind group, and we had a very solid and informative session where people could learn about how to analyze data.
  • The mentors were fantastic. Every one of them gave such incredible advice, starting with one of the most amazing Applied Statistics professors I have ever met, who told us that we only had 5 minutes, so we should focus on a good story and in telling a good story. She also mentioned that we may need to supplement the dataset that was given to us. So our group ended up having to use an api to obtain more data.
  • I took an Advanced Regression class with her a few weeks ago, and learned so much! She’s awesome and has this deep, incredible knowledge, and makes me think that she is exactly the kind of person who would inspire me to become a professor. She’s amazing!
  • Another thing is that our group was based on the West Coast, but I am not. I think the first night, I went to sleep at 2am, but I got up to meet at 11am on Sunday.


  • We focused on looking at lead actor / actress gender and ratings using our dataset. Our inspiration was drawn from the Sony hack leaks, in which it was revealed years ago via leaked emails that Jennifer Lawrence was paid less than her costars in the movie “American Hustle” (7 percent vs 9 percent). But what did the data say? Did it show that users would rate more positively if the leads were male vs female?

I had to leave early

  • I usually talk with my parents on Sundays, so I had to leave a bit early; just after our presentation, but I definitely voted. So once again, I had no idea about the results, but figured since I didn’t get pinged and just people from my group added me on LinkedIn, we didn’t win anything.
  • I was wrong! We got the first place prize for Best Visualization (there were about 3 types of these kinds of awards given out), which was awesome!
  • And for our troubles, each member of the winning team got a $50 Amazon card! So it ended up being so much more than I could have ever hoped for!

Starting over

  • I can’t help but think about how different my experiences were in this hackathon versus my first experience, where I didn’t end up taking part.
  • Or in ones like BayHac or the LambdaConf one, where people were patient with me, and paired with me. I’m super grateful for that. I learned a lot, made new friends, and had a lot of fun. To my team, my mentors, and to the group and everyone who keeps supporting me and encouraging me to keep going; thank you so much for everything.

Here are some photos

And that’s it.

Written on April 13, 2021