How One UChicago Student Found His Passion for Teaching

For Galib Khan (MSADS ‘22), the path to advanced education was initially one he never intended to follow. A graduate of Rutgers University with a background in data science, he began his career at Northern Trust in Chicago, where his role focused on analyzing vast datasets using tools like PySpark and parallel programming. Despite his initial resistance to more schooling, Khan eventually decided to pursue the MS in Applied Data Science program at the University of Chicago, a decision that transformed his career trajectory. 

Early influences and reluctant beginnings 

Khan’s journey began with a nudge from Francisco Azeredo, an MS in Applied Data Science professor at UChicago whom he met early in his career. Azeredo saw potential in Khan and encouraged him to consider the MS program. “I was adamant not to go back to school—after graduating I thought I was done with it,” he recalls, laughing. However, Khan soon recognized that advancing in the evolving field of data science required more than just his bachelor’s degree. “In this field, a bachelor’s degree is not cutting it,” he explains. “That’s why I ultimately decided to enroll in the program at UChicago.” 

Beyond simply encouraging Khan to enroll, Azeredo became an important colleague during Khan’s time in the program. Azeredo later offered Khan the opportunity to be his teaching assistant, deepening Khan’s experience in data modeling. “Even now, Azeredo and I still talk here and there about modeling and complex computations,” Khan says. “We try to help each other out whenever we can.” Azeredo’s support extended beyond academic advice, evolving into a long-term professional relationship that has contributed to Khan’s growth. 

Balancing theory and application in MS-ADS program

Reflecting on the program’s structure, Khan found that UChicago’s curriculum provided a well-rounded education in both theoretical and applied data science. Although he describes the curriculum as “standardized”—covering classic topics like linear and logistic regression—he credits the program’s network as an invaluable asset. The connections he formed during the program have been key, especially in a field as versatile as data science. “We recently had an alumni meetup, and it’s incredible the kinds of conversations that happen there—like someone asking if anyone knows of any opportunities. That’s the power of being connected to people in your field.” 

Khan singles out Linear Algebra, taught by instructor Danny Ng, as particularly impactful and his favorite course in the program. “Danny does a phenomenal job of breaking the math down and exposing how models like linear regression actually work using Linear Algebra,” Khan says. “The way he illustrates it and walks through it—it’s really beautiful. It’s a good, solid course that helps you understand some of the classic statistical models. Not only that, but linear algebra is like the engine of this whole field when you think about it.” 

Becoming a TA: sharing knowledge and building community 

Khan’s love for teaching and helping others led him to become a teaching assistant for UChicago’s Statistical Models for Data Science and Reinforcement Learning courses. “I’ve always been the guy people go to for help with programming,” he says. Being a TA allowed him to channel that passion while supporting others on their academic journey. “I really do enjoy teaching and helping out other people. Also meeting people from different backgrounds—whether they’re from the Federal Reserve, the banking, or the insurance industry. It’s a great networking opportunity.” 

Finding his path and inspiring others 

Khan’s journey from a reluctant student to a TA and an aspiring adjunct professor exemplifies the power of growth through connection. Looking back, he sees his decision to pursue the MS in Applied Data Science as an impactful choice he made for his career. With a passion for teaching and a positive perspective on networking, Khan’s story serves as a guide for future students in the program—illustrating that sometimes, the most rewarding paths are the ones we least expect.