As a liberal arts college, Grinnell teaches us many important things like interdisciplinary thinking, problem solving, and communication skills. But it also means that some technical skills that are sought out by companies are left out of curricula. As an aspiring data scientist, I had to teach myself software and concepts like Python, machine learning, SQL, Hadoop, and Spark in order to succeed in my internships and research experiences.
Then in my senior year at Grinnell, my friends and peers would tell me how interested they were in the skills I had taught myself. I was a mentor at the Data Analysis and Social Inquiry Lab (DASIL), so I decided to use my position as a platform to help others. I envisioned and created the learning materials for a “Python for Machine Learning” series consisting of 5 workshops. My workshops covered the following material:
- Python Programming
- Conceptual Machine Learning
- Applied Machine Learning in Python
- SQL
- Distributed Computing with Spark in Python
Well over 100 students, or around 8 percent of the Grinnell student body, signed up for my series. There was a buzz around campus: students were anticipating the first workshop with excitement, and professors were strongly encouraging their students to participate.
The actual workshops then consisted of in-class problems and coding activities interspersed between lectures. It was definitely intimidating at first to teach in front of a room of 100 students, easily Grinnell’s biggest ever course. But my students asked great follow-up questions and seemed really interested in the material, easing my nerves.
Overall, the experience improved my ability to explain difficult concepts in simpler terms and deepened my own knowledge of data science. I also strengthened the connections with my community that I had built as a statistics tutor and data science mentor during my time at Grinnell. In addition, it helped me realize my passion for teaching and mentoring, which I aim to continue pursuing at IBM. A professional goal of mine is to eventually advise less-experienced data professionals on the appropriate use of algorithms as well as give lectures on topics like data ethics and machine learning at my company.
Martin Pollack ’22, a Data Scientist for IBM, studied mathematics, statistics, and German at Grinnell.
