Potential pitfalls of machine learning include bias and unequal benefits. In healthcare, these stakes are especially serious.

When students enroll in the Advanced R Workshop, they will be taught by someone who stood in their shoes before them. Ravi Pandey, a 2016 Smith graduate of the Master of Science in Information Systems program, leads the class. He knows what it takes to succeed professionally, and he teaches graduate student weekend workshops on R, machine learning and Python.

What separates the top performers from everyone else? According to Googler and Maryland Smith graduate Arhaan Saksena, MS '15, it's a focus on the big picture. "Always ask yourself, what is the larger problem that you're trying to solve?" he says. "It's crucial to define the overarching goal first, and work smartly to resolve the issue." Otherwise, he says, you will end up spending time and resources on tasks that may not be aligned with the actual problem.

By Meredith Cobb Gabrielle Bianchi ’18, MS ’19, is a firm believer in seizing opportunities. Within three years at the University of Maryland’s Robert H. Smith School of Business, she finished a double major in accounting and information systems, got involved in Greek life and club activities, and earned citations in the College Park Scholars and QUEST honors programs.