Course Overview

This course is the first course in quantitative business science for data analysts, detailing: (1) statistical programming, (2) stochastic simulation, (3) computationally intensive methods, (4) mixed and multilevel models, and (5) formal model comparison using information criteria. The practical goals of the course are to teach students how to specify, code, fit, and interpret model-based inference, and appreciate the powerful things ‘model thinking’ can do for analyzing dependent data when sampling is over time, space, or within clusters, which are common in digital operations (e.g., internet of things), platform business (e.g., Airbnb, Alibaba, Uber), and sharing economy (e.g., crowdfunding). The course is ‘heavy on code’ since having ‘computational thinking’ in the digital era entails a lot of scripting and programming.

Learning Achievement

The objectives of this course are: (1) to familiarize the R language, (2) to perform statistical computing via computer programming, (3) to develop statistical models via R and the Stan package, and (4) to develop the skills in organizing an effective data-driven strategy in a real business world.

Competence

Course prerequisites

*Restrict to 3rd-year and above.

Grading Philosophy

Course schedule

Course type

Online Course Requirement

Instructor

Shu-Jung Yang

Other information

Department of Business Administration,
Graduate Institute of Business Administration

Site for Inquiry