It all began in a computational statistics graduate class at Carnegie Mellon back in 2006. That was the year I was introduced to R—a language that seemed to hold the key to unlocking a deeper understanding of data and statistical modeling.
At the time, it wasn’t just about learning syntax; it was about immersing myself in a new way of thinking that merged computation and statistics in ways I hadn’t experienced before. R became a bridge between theory and practice, allowing me to test ideas quickly, visualize results, and explore datasets with curiosity and rigor.
