Teaching

• The Hong Kong University of Science and Technology (Guangzhou)

  • UCMP 6010 - Cross-disciplinary Research Methods in Social Science (Graduate Level), Fall 2023 [Evaluation report: Available upon request]

    • UCMP 6010 is a modulized graduate-level course that focuses on using various approaches to perform quantitative analysis through real-world examples. Students will learn how to use different tools in an interdisciplinary project and how to acquire new skills on their own. The course offers different modules that are multidisciplinary/multifunctional and generally applicable to a wide class of problems. Module C is taught by Prof. Zhuoni Zhang for students without a social science background, focusing on 1) Probability and Statistical Inference, 2) Establishing Causality in Social Science Research, and 3) Applications of Quantitative Research Methods in Social Science Research.

  • UGOD 5040 - Urban Data Acquisition and Analysis (Graduate Level), Fall 2024 [Evaluation report: Available upon request]

    • This course is taught by Prof. Muzhi Zhou, which aims to introduce students to different types of data and their application in answering various research questions. This course will teach basic concepts and perspectives used in data collection, such as sampling survey designs and population data analysis. Since alternative data sources (e.g., social media data, simulated data from large language models (LLMs), and GIS information) have become increasingly available, the course will also cover other modes of data acquisition to explore cutting-edge methods for collecting and analyzing non-survey data, and how they can be used in combination with survey data. Upon successfully completing this course, students will have a good knowledge of data collection and usage and can apply it to their research. In this course, I taught the students how to perform sequence analysis for life course and time-use diary data using R.

A Basic Checklist for Observational Studies in Political Science (Yiqing Xu, @Stanford)

Applied Econometrics / Causal Inference / ML Notes (Apoorva Lal)

Applied Empirical Methods (Paul Goldsmith-Pinkham, @Yale)

Awesome Causal Inference (Matteo Courthoud)

Frontier Topics in Empirical Economics (Zibin Huang)

Statistical Computing in Python and R (Apoorva Lal)