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.
  • Syllabus for BIOSTAT620
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

BIOSTAT830: Advanced Topics in Biostatistics

  • Graduate level
  • Residential
  • Fall, Winter term(s) for residential students;
  • 1-4 credit hour(s) for residential students;
  • Instructor(s): Song, Peter Xuekun (Residential);
  • Prerequisites: course/instructor dependent
  • Description: Advanced training in biostatistical methods primarily for doctoral students. Format will include lectures, readings, presentations and discussions in an area of special interest to students and faculty, such as stopping rules and interim analysis in clinical trials, conditional and unconditional inference and ancillarity, or nonparametric regression.
  • Syllabus for BIOSTAT830

BIOSTAT895: Analysis of Multivariate Categorical Data

  • Graduate level
  • Residential
  • Fall term(s) for residential students;
  • 3 credit hour(s) for residential students;
  • Instructor(s): Song, Peter Xuekun (Residential);
  • Not offered 2021-2022
  • 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.