Joining the QMSS program this Winter 2023 is Yun Lee, a Graduate Student Instructor for the QMSS 301 course. Yun is currently pursuing a Master's in Applied Data Science through the School of Information. Her current research delves into Natural Language Processing and involves analyzing text data from social media platforms such as Twitter and Reddit. Her goal is to explore human biases and investigate if these biases will be reflected in trained AI models.
Yun started her undergraduate degree at National Taiwan University, where she spent two years studying economics. She completed her last two years at Waseda University’s School of Political Science and Economics. Her undergraduate research was within the field of behavioral economics with a focus on social equality. It consisted of looking for a relationship between internet access and political behavior, diving into questions such as: does internet access affect an individual’s likelihood of voting?
Following her undergraduate degree, Yun took on a role as a market research analyst for IQVIA, a pharmaceutical company. During her time there, Yun conducted numerous interviews that made her aware of the abundance of data available to be collected. This realization motivated her to dive deeper into programming, so she taught herself R. After expanding her computer programming skill set, Yun made a career change and began a role as a data analyst at Garmin. Over her two years there, she gained proficiency in Python and developed a deeper understanding of data science and machine learning. Inspired by the range of implementations of data science, Yun decided to pursue another degree.
Yun chose to continue her education at the University of Michigan because she was drawn to the plentiful resources and flexibility that were offered. She believes that what best prepared her to be a GSI for the QMSS program was her previous field experience along with the supportive QMSS faculty.
Yun thinks that QMSS 301 is an important course since it combines elements of both data science and social science, creating numerous opportunities for analysis. She emphasizes the importance of exploring QMSS applications with students so that they can eventually become inspired to implement these methods in new, creative ways. Yun also believes that the lab component of this course is beneficial for developing practical experiences such as comfort with using Python. Yun specifically thinks that one project in 301, in which students execute exploratory data analysis and evaluate the performance of their own model, is especially important and fundamental to QMSS ideas.
As a suggestion for undergraduate students, Yun states that crucial hard skills to develop are the ability to extract and clean data using SQL, and the ability to properly depict research results. As for soft skills, Yun strongly encourages students to learn to ask questions in class!