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Describing Data 

STEAM Teacher Casey Moden challenged his “Data Analysis with Python” students to tell a story. Instead of prose and chapters, their stories were made of graphs and data. 

Using Kaggle, a platform developed for data scientists
to interact and compete in solving real-life problems, each student found data on a topic they were interested in, ranging from coding languages to video games. From there, they looked for potential correlations and relationships within the sets. These patterns were then displayed using Seaborn, a platform that specializes in data visualization. The project combined critical thinking, data science, public speaking, and presentation. 
 
Nathan ‘25 studied Formula 1 Racing data, specifically Italian-Australian racer Daniel Riccardo. Looking at over 70 years of racing data (like wins, average race time, and performance ranking), he was able to discover a decline in performance in racing teams that Riccardo joined. Teams on an upward trend would start to drop off shortly after bringing in Riccardo. Coincidence? Maybe. Interesting? Definitely. 
 
Yael ‘23 chose to look at data around popular sitcom “Parks and Recreation.” Using code, she created a formula that counted each time a character spoke per episode, using that total number of lines per character as part of her key research. Though she didn’t find correlation between episode popularity and the number of lines a specific character had, she noted that Leslie Knope (played by Amy Poehler) consistently had the most lines of any character.  
 
Tools like Seaborn and Kaggle are professional applications often used in the real world. The heart of the project was the tools used and the different ways students went about questioning their sets of data. Every presentation told a story of the topic through the lens of data, but also of the hard work of each individual.