On December 21, 2021, a joint workshop on the applications of machine learning in finance, titled “Applied Machine Learning Pilot Projects”, was held at the School of Political Science and Economics (SPSE), Waseda University. Prof. Karen (Yumin) Zhang from the Monash Business School of Monash University joined Prof. Giang Nguyen to share the real-life application of statistical/machine learning methods and techniques on business case studies and research projects.
The joint workshop saw the participation of around 20 motivated undergraduate students from both the English- and Japanese-based Degree Program at the SPSE, the majority of whom held high interests in pursuing a career in the financial industry or furthering their studies in economics and finance to the graduate level in the future. The students have acquired forefront knowledge in data science and obtained hands-on experience to clean and process complex datasets, build models, and explain statistical estimation results with applications in R through the contents, exercises, and assignments of the weekly course Financial Data Analytics taught by Prof. Nguyen. The workshop added to a deeper understanding of applied machine learning and offered fresh perspectives to students on algorithm development and unsupervised learning.
After joining this workshop, students were excited to share their opinions: “As someone hoping to pursue a career in the financial industry, I benefitted from this guest lecture because I learned how data analytics is used in this industry,” said Sungjae Lim, a fourth-year student at the SPSE. “I gained a deeper understanding of applied statistical learning and how it can be utilized for different purposes in both industry practice and academic research.” “It was very interesting to know about how the guest speaker utilized machine learning to conduct analyses in her projects. Her interpretation of machine learning helped us to develop further understanding of this tool,” said Yuelin Wang and Xinchen Yu, two fourth-year students at the SPSE.
“Although some undergraduate students in the course do not intend to advance to master’s programs, they believe that skills in computer programming and abilities to represent and interpret information through the language of statistics and mathematics is becoming increasingly important for internships, job-hunting, and career development in today’s competitive job markets,” said Peter (Siyuan) Chai, the Teaching Assistant of the Financial Data Analytics course as he highlighted the opinions of participants.
Prof. Giang Nguyen and Prof. Karen believe that mutual learning and international cooperation in data science are meaningful ways for young students and researchers to understand empirical approaches in social sciences and put them into practice.
