Post by Sean Harvey

A student who will be taking my Data Science course this fall recently asked me a question: “Are we only going to use Python?” It made me stop and reflect. Python has become the dominant language in many data science and machine learning applications, so it will certainly be a major part of our course. But data science is much bigger than any single programming language. Depending on the problem you’re solving, you might find yourself using Python, R, SQL, or visualization tools. In many organizations, professionals spend as much time accessing, cleaning, and understanding data as they do building predictive models. One of the interesting parts of teaching a course for the first time is that the curriculum serves as a roadmap, not a limitation. There is always room to adapt based on student interests, emerging technologies, and the time available during the semester. The conversation also reminded me of something important: the best way to teach is to keep learning yourself. Between graduate classes, I’ve decided to spend some time brushing up on SQL. It’s a skill I’ve used before, but like any skill, it gets stronger with practice. As I continue to expand into analytics and data science, it’s a reminder that learning doesn’t stop when you finish a degree, earn a certification, or start teaching a subject.