Master's Program
Digital Experimentation Methods: A/B Testing
MS on Business Analytics 7025, HKU Business School
Course Description
The newly emerging capability to rapidly deploy and iterate online controlled experiments to assist decision-making in organizations is one of the most significant innovations in today’s technology industry. As more and more social interactions, decisions, opinions, and transactions are mediated by online platforms, digital experiments are becoming increasingly crucial for firms to understand their user behaviors and make product decisions. This course will cover the most cutting-edge digital experimentation methods used in the daily operations at large technology firms. We will also share the key lessons and pitfalls encountered in practice. Topics include the statistics behind experiments, experimental design, methods of analyzing experiments, A/B testing platforms and culture in organizations, recent developments in digital experimentation, and observational causal studies. Students will also learn how to conduct and analyze online experiments using programming languages, such as python, assignments, and a course project.
Please find all the course materials @ https://github.com/shanmit/Course---Digital-Experimentation-Methods-A-B-Testing
The primary source of information for this course is my practical experience and research in the field of AB testing. Additionally, I reference the book titled "Trustworthy Online Controlled Experiments (A Practical Guide to A/B Testing)" by Ron Kohavi, Diane Tang, and Ya Xu, which is published by Cambridge University Press. Feel free to use these slides to enhance your understanding of AB testing. However, if you intend to incorporate these slides into your own courses, please email me to obtain my permission and ensure full acknowledgment is given.
Ph.D. Course
Applied Social Network Theory & Networked Experiments
Foster School of Business, UW, Seattle
Course Description
This course will examine the foundations of and recent research in Applied Network Theory and Networked Experiments from economic, management, sociological, and statistical perspectives. Randomized experiments are important tools for testing theories, evaluating or finding strategies and policies in general, and are especially useful in social network settings. It is also increasingly possible to conduct randomized field experiments in many empirical contexts. This course is aimed at doctoral students conducting original research in applied network theory and applying randomized experiments in social-network settings, which is relevant to students from diverse fields, such as management, economics, sociology, statistics, and computer science/machine learning. The course will follow a research seminar format. We will examine and evaluate network research and networked experiments with deep critical thinking.
Undergraduate
IS460, System Analysis and Design on digital product design and agile product management
Foster School of Business, UW, Seattle
IS445, Database Management on ERT and SQL
Foster School of Business, UW, Seattle