Courses Taught by Nicholas Henderson

BIOSTAT607: Basic Computing for Data Analytics

  • Graduate level
  • Residential
  • Fall term(s) for residential students;
  • 1-3 Credit Hour(s) for residential students;
  • Instructor(s): Henderson, Nicholas (Residential);
  • Prerequisites: No other courses
  • Advisory Prerequisites: students with no prior programming experience at all are strongly encouraged to take BIOSTAT606 "Introduction to Biocomputing" offered before the Fall term starts.
  • Description: This course is designed as a 3-credit modular course focusing on basic programming skills, including Python (1 credit), R (1 credit), and C++ (1 credit). The course covers key features of each programming language, data structures, basic data processing skills, basic data visualization skills (for Python and R), and basic UNIX skills. Students are allowed to take one or more modules according to the need basis.
  • Learning Objectives: (a) To understand key features of R, python, and C++ programming languages in a modular way. (b) To understand basic data structures, basic data processing skills, basic data visualization skills (for Python and R modules), and basic UNIX skills

BIOSTAT685: Elements of Nonparametric Statistics

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 3 Credit Hour(s) for residential students;
  • Instructor(s): Henderson, Nicholas (Residential);
  • Prerequisites: Biostat 602 or STAT 511, and Biostat 650 or Perm. Instr
  • Description: First half covers theory and applications of rank and randomization tests: sampling and randomization models, randomization t-test, Wilcoxon rank sum and signed rank tests, Kruskal-Wallis test, asymptotic result under randomization, relative efficiency; second half covers theory and applications of nonparametric regression: smoothing methods, including kernel estimators, local linear regression, smoothing splines, and regression splines, methods for choosing the smoothing parameter, including unbiased risk estimation and cross-validation, introduction to additive models.
  • Syllabus for BIOSTAT685