Fall 2019 Co-lectured with Prof. Qin Gao

This course helps graduate students to learn how to analyze data in human factors research, including basic statistical knowledge and approaches by R/Python codes about:

• Exploratory analysis (descriptive statistics and plotting)
• Comparison (both parametric and non-parametric)
• Correlation and regression
• Factor analysis
• Text mining and visualization

I teach the last part. Regarding text mining, we introduce

• Preprocessing of texts (tokenization and filtering)
• Representation (tagging by lexicons, tf-idf, and embeddings)
• Training models (some commonly used methods of machine learning)

The lectures are designed practical and easy to learn by students in management science, probably with little backgrounds in computer science or relevant majors. We introduce what purpose each method/tool can be used for and explain how it works graphically and straightforwardly.

I also design and organize a workshop for novices to learn Python and practice the knowledge and skills. It starts with how to set up the environment and basics about Python. Then it goes through all the methods introduced in the lectures based on a real data set of reviews of laptops from jd.com (an online shopping platform in China).

We introduce basic knowledge of visualization based on the book Visualization Analysis and Design, including

• Abstraction of data and tasks
• Encoding
• Reduction of complexity (manipulation, facets, and reduction)
• Resources and tools (for both static and interactive plots)