Students come to Cornell’s Master of Professional Studies (MPS) program in Data Science and Applied Statistics via diverse and sometimes unlikely avenues. James Kong, MPS ’19, arrived to the program – and eventually landed in the data science industry – by way of geology.
Fresh out of undergraduate studies at University of California, Berkeley, James spent a few years working in the field of geographic information systems (GIS). The role introduced him to data in a practical, problem‑solving context, and, through a mix of online and in‑person coursework, he found himself drawn more and more to statistics. That curiosity became a turning point. Kong decided to pivot careers and pursue graduate studies in stats.
“I went to Cornell,” he said, “and the rest is history.”
Today, James is a senior product analyst for Just Eat Takeaway.com, a global online food delivery marketplace. He describes his role as a mix of product analyst and data scientist, developing A/B test experiments, statistical analysis, and machine learning models to provide recommendations for senior leadership.
“Food delivery is a very competitive space. Everything moves fast, so we must be agile,” he said. “Today, I might be running A/B tests and analyzing new features, and then tomorrow I’ll be working on machine learning problems. I like that most about my role.”
Of his MPS experience, James said he found the program well structured, offering the flexibility to choose a statistical methods curriculum or more of an artificial intelligence track that’s heavy on statistics and data science. James leaned more toward the statistical methods side but took courses in Python and SQL – two programming languages he uses every day in his current role.
He credited the MPS Project for offering critical real-world experience. In the semester-long course, MPS student teams work with client entities big and small, from Fortune 500 companies to non-profits, to develop implementable solutions for real problems. For his project, James and his teammates partnered with a company to predict housing prices using deep learning models.
