Teaching

My Teaching Philosophy

My approach to teaching is rooted in the belief that the most effective learning happens at the intersection of theory and practice. In my courses, I strive to demystify complex topics in accounting and finance by grounding them in real-world data and hands-on analytical exercises. I design my curriculum to empower students not just with established knowledge, but with the critical data literacy and machine learning skills necessary to ask new questions and solve the challenges of a data-rich world. My goal is to foster an environment of intellectual curiosity where students develop the confidence to become analytical, evidence-based thinkers.

Graduate Courses

MSc in Data Science and Analytics Program

DSA 5045: Machine Learning for Finance

This flagship course provides a comprehensive introduction to machine learning models and their application to financial data. Students learn the entire data science pipeline, from data wrangling and feature engineering to model evaluation and interpretation, using Python libraries like Scikit-learn and TensorFlow to solve real-world problems in areas such as credit scoring, fraud detection, and algorithmic trading.

View Syllabus

*Exemplary student projects will be featured here at the end of the semester.

DSA 5047: Data Analytics for Finance

This course focuses on the tools and techniques for extracting actionable insights from financial and alternative datasets. Students work with SQL, Python (Pandas, Matplotlib), and data visualization tools like Tableau to analyze capital market data, build financial models, and effectively communicate their findings. The curriculum emphasizes a case-based approach to data-driven decision making.