Teaching

IDSC 4444: Exploratory and Predictive Analytics (Spring 2023 / Fall 2023)

• This course provides an in-depth exploration of data mining and machine learning methods. Students will learn various techniques, including exploratory methods such as association rules and cluster analysis, predictive methods such as k-nearest neighbors (k-NN) and decision trees, and text mining methods for extracting valuable insights from text data. The course integrates theoretical lectures with hands-on lab sessions, where students will implement these methods using the R programming language.

• Student Rate of Teaching: 5.27/6 (78% of participation rate)

What students said

“YoungJin is the kindest individual and instructor I have ever had. Extremely open to feedback from students so he can improve but also receptive to hearing feedback from others. He is very timely in his responses and understands that this content is difficult for some students and makes accommodations as he sees fit.”

“The instructor cared a lot about our success in the class. He made sure to be available outside of class time to help, which was very nice.”

“YoungJin always brought a high level of energy to class and clearly was invested in his students’ learning. He was readily available outside of class if students had questions.”

“YoungJin was very understanding of the students. I appreciate how he listened and took our feedback throughout the year.”

“He was very helpful whenever I had questions, and I liked how the labs and homework were structured.”

“YoungJin was extremely helpful in and outside the class. By far, this has been the class at Carlson I’ve taken where I’ve gotten the most help directly from the professor outside of the classroom, and YoungJin was extremely helpful and responded quickly. This helped me learn a lot better, and the one-on-one help for certain assignments was incredibly valuable.”