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): Song, Peter Xuekun (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.
Concentration Competencies that BIOSTAT620 Allows Assessment On
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

BIOSTAT695: Analysis of Categorical Data

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 3 Credit Hour(s) for residential students;
  • Instructor(s): Song, Peter Xuekun (Residential);
  • Prerequisites: Biostat 602 and Biostat 660
  • Description: Regression models for the analysis of categorical data: logistic, probit and complementary log-log models for binomial random variables; log-linear models for cross-classifications of counts; regression models for Poisson rates; and multinomial response models for both nominal and ordinal responses. Model specification and interpretation are emphasized, and model criticism, model selection, and statistical inference are cast within the framework of likelihood based inference.
  • Syllabus for BIOSTAT695