Just after wrapping up 3 intense weeks at the UPenn Wharton Data Science Academy, I dove right into another data science experience. I spent a week at the Joint Statistics Meetings (JSM) in Toronto, held from August 6-10. This was my second time attending JSM; and armed with freshly learned data science knowledge from the past month, I was all set to explore the world of statistics once again.
On August 8, I presented my own project at a Speed Session -- a brief 5-minute presentation followed by a 45-minute poster showcase and Q&A. My project compared competing metrics used in the FIRST Robotics Competition (FRC), the high school robotics program I am passionate about. I was honestly surprised by the reaction I received as JSM is usually populated by seasoned data science professionals and academia. The fact that I was just a high school student seemed to surprise people, but in a good way. They were genuinely impressed by my project and the way I presented it. My oral presentation earned a big round of applause, and many people came to my poster presentation with compliments and engaging discussions. During my poster session, the SCORE Network, a national initiative dedicated to teaching data science through the lens of sports statistics, approached me and invited me to join their mission. Talk about exciting!
The excitement didn’t stop there. On August 9, I was invited to the executive meeting of the Section on Statistics and Data Science Education, under the American Statistical Association (ASA). My JSM presentations earned an honorable mention during the meeting, and I had the chance to chat with the section chair and a bunch of data science professors. It was surreal to be recognized by such a high level organization!
But let’s rewind a bit. Besides showcasing my project, I also went to other sessions and learned from data science experts. I sat in on talks covering various subjects like process meta-analysis, the future of data science, and entering the world of sports statistics. It was eye-opening and very interesting to get an inside look at how statisticians from different realms think and work.
One session that really stood out to me was held by the SCORE network. It was almost like fate; even before they approached me during my poster session, I had already marked my calendar for this sports stat related session at JSM this year. Because of my experiences with FRC, I’ve always been curious about sports statistics and how they were used, even if I didn’t watch professional sports. It was a 2-hour session featuring 4 speakers, and I found them all very intriguing.
First up, a speaker talked about an undergraduate program that introduces students to sport statistics and connects them with professional sports teams. While I am not old enough to participate in the program just yet, it was a treat to see one of the pipelines for sports statistician in action. Then the next talk was about applying sports stats to curling – a sport I hadn’t really seen from a data perspective. While this competitive ice cleaning is not very common in the US, it is extremely popular in Canada. The speaker had put a lot of effort to effectively analyze win probabilities based on the enigmatic dance of stones. Since there is very limited data from past competitions, she also collected her own data by developing an interesting image process software that can accurately read the locations of stones, rather than simply reply on human data collectors who often do not have a very good vantage point. The final duo of presentations, both centered around the SCORE network itself, offered a glimpse into the creative realm of contributors. The flexibility they enjoyed piqued my curiosity, nudging me to explore possible future involvement.
Looking back, this year’s JSM in Toronto spoke to me on a deeper level compared to my first stint in 2022. I grasped intricate presentations that once felt like a code I couldn’t crack, and I forged new connections that made me feel like a part of this thriving data science community. The conference was a canvas of statistical wisdom, painted with the brush of my own journey, and it marked a significant step forward in my data science adventure.