With both major projects now completed, I took some time this week to reflect on everything I’ve learned over the past few months. From analyzing racial and age disparities in police shootings to uncovering patterns of political violence across the U.S., the experience has been intense and eye-opening. Project 1 helped me understand how statistical tools like t-tests and Cohen’s d can quantify bias and show meaningful differences between demographic groups. The visualizations and cumulative plots revealed age gaps that go beyond numbers—they highlight systemic issues.
In Project 2, I switched gears toward unsupervised learning and spatial clustering. Using K-means on real-world event data showed how regional and temporal patterns emerge from political unrest. The clusters not only told us where violence was happening, but what kind of unrest was occurring—whether protests, battles, or violence against civilians. I also learned the importance of careful data cleaning, feature selection, and visualization in driving analysis that makes sense both statistically and socially.
Across both projects, the biggest takeaway was how data science can contribute to social understanding and policy change. These aren’t just numbers—they’re lives, locations, and events that matter. I’m grateful to have had the opportunity to dig deep into such meaningful topics. Looking forward, I hope to keep combining data science with real-world issues to build more awareness and create impact through insight.