Courses Taught by Peter Xuekun Song
BIOSTAT620: Introduction to Health Data Science
- Graduate level
- Residential
- Winter term(s) for residential students;
- 3 credit hour(s) for residential students;
- Instructor(s): Peter Xuekun Song (Residential);
- Prerequisites: BIOSTAT 607, BIOSTAT 601, BIOSTAT 650
- Advisory Prerequisites: No other courses
- Description: This course offers a systematic introduction to the scope and contents of health data arising from public health and the biomedical sciences. It focuses on rules and techniques for handling health data. Through both regular lectures and guest lectures, this course covers a broad range of health data.
- Learning Objectives: (a) To understand the foundation and rules for handling big health data. (b) To develop a practical knowledge and understanding of important statistical issues and relevant data analytics for health big data analysis. (c) To learn and master basic software and programming skills for data cleaning and data processing.
- Syllabus for BIOSTAT620
| Department | Program | Degree | Competency | Specific course(s) that allow assessment | BIOSTAT | Health Data Science | MS | Understand the roles and principles when a biostatistician conducts the analysis of biomedical or public health data | BIOSTAT620 | BIOSTAT | Health Data Science | MS | Distinguish among the different measurement scales and data quality, as well as their implications for selection of statistical methods and algorithms to be used based on these distinctions | BIOSTAT620 |
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BIOSTAT895: Analysis of Multivariate Categorical Data
- Graduate level
- Residential
- Fall term(s) for residential students;
- 3 credit hour(s) for residential students;
- Instructor(s): Peter Xuekun Song (Residential);
- Not offered 2023-2024
- Prerequisites: Biostat 651 and Biostat 695 or Perm. Instr.
- Description: Probability models for two-way tables; multi-factor, multi-response framework; product multinomial distribution theory; Taylor series estimates of variance, weighted least squares and Wald statistics; constraint equations; models for characterizing interactions; step-wise variable selection; factorial designs with multinomial responses; repeated measurement experiments; log-linear models; paired-choice and bioassay experiments; life-table models.