Danny Ng’s Path to Becoming a TA in UChicago’s Applied Data Science Program

For Danny Ng, the journey into data science and teaching was less about following a direct path and more about seizing opportunities as they arose. A PhD graduate in statistics from the University of Chicago, Ng’s connection to the MS in Applied Data Science (formerly MSCA) program is a bit different from most of the program’s teaching assistants. While many TAs come from the program itself, Ng’s connection stemmed from his time in the Department of Statistics, where he had already established himself as a teacher and researcher.

From PhD to teaching assistant

Ng’s journey into teaching for the MS in Applied Data Science program began towards the end of his PhD at UChicago. “I had teaching experience already—I was a TA during my undergrad and throughout my PhD years. As part of the PhD in statistics, we had to teach one class,” he explains. “When I was finishing my PhD, people in the MSCA program asked me if I’d like to be a TA. I think they liked the way I explained things, and that’s how I got started.”

At the time, the program had just started, and Ng saw an opportunity to help shape the future of the data science field by teaching in an emerging program. He was one of the first TAs in the MSCA program, which would later become the MS in Applied Data Science program.

Ng’s perspective on teaching data science

Ng’s role as a TA for the program has evolved over the years. He’s taught a wide range of courses, including machine learning and advanced machine learning. For Ng, the greatest reward comes from the diverse student body he interacts with. “What’s different about a professional program like this is the level of motivation,” he explains. “Students come in knowing exactly what they want, and they’re eager to learn because they’re paying for it. It’s not like undergrads who sit in class just to fulfill a requirement—they’re here to maximize their learning.”

The students in the program come from all walks of life—some straight out of undergrad, others from industry looking to switch careers or deepen their expertise. This diversity in background and experience is one of the most enriching aspects of the program, and it’s something Ng sees improving with each passing year. “The students are getting stronger. A few years ago, we had a lot of boot camp graduates, but now students come in with better programming skills and a solid foundation in machine learning,” he notes. This higher level of preparedness makes the program more dynamic and rewarding for everyone involved.

Teaching practical applications of data science

When it comes to the skills and concepts Ng hopes students take away from his courses, he emphasizes the importance of asking, “When should I use this technique, and when should I not?” While many traditional academic programs focus on teaching students how to use various tools and techniques, Ng encourages his students to think critically about how to apply them in the real world. “In professional programs like ours, we focus on application. We ask students to consider when a technique is applicable and when it might not be,” he explains. “It’s not just about knowing how to use a model; it’s about understanding when it’s the right choice.”

This real-world focus, combined with the program’s strong academic foundation, helps students bridge the gap between theory and practice, preparing them to apply their knowledge in a variety of industries.

Evolving with the field

Being a TA doesn’t just help Ng stay connected to the world of data science—it also keeps him sharp. “Teaching helps me stay updated on the latest changes in the field, whether it’s new machine learning techniques, changes in programming languages, or even the newest developments in cloud computing,” he says. Teaching forces him to stay current with evolving technologies, something he sees as an essential part of his own professional growth.

Additionally, Ng finds that teaching helps improve his own communication skills. “It’s one thing to know a topic, but it’s another thing to explain it clearly to someone else,” he says. “Being able to address students’ questions and communicate complex ideas effectively has been invaluable, especially when working with stakeholders at all levels in my job.”

Advice for prospective students

As someone who has worked with a range of students in the MS in Applied Data Science program, Ng has some advice for prospective students. “The most successful students tend to be highly technical,” he notes. “You need a strong foundation in mathematics, programming, and critical thinking. If you’re an undergrad considering this program, focus on building those foundational skills. The program moves quickly, and you’ll need that background to succeed.”

Ng also emphasizes the importance of continuous learning and adapting, “The field of data science is constantly changing. Staying up to date is crucial not just for the students, but also for those of us teaching the next generation of data scientists.”

A lasting impact

Ng’s story highlights the power of teaching and the personal growth that comes with it. Though he never completed the MS in Applied Data Science program himself, his deep connection to UChicago’s data science community as a teacher has allowed him to make a lasting impact on students and the field of data science. His perspective on the program, and the evolving nature of its students, shows just how much it has grown since its early days.