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.
Course Goals: The goal of this modular course is to prepare students in Health Data Science program with adequate programming skills. It serves as a prerequisite of several other courses in the Health Data Science Concentration program.
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
BIOSTAT815: Advanced Topics in Computational Statistics
Prerequisites: BIOSTAT601, BIOSTAT602 and BIOSTAT615 or equiv and proficiency in C++
Description: Modern numerical analysis for statisticians. Combination of theory and practical computational examples illustrating the current trends in numerical analysis relevant to probability and statistics. Topics choose from numerical linear algebra, optimization theory, quadrature methods, splines, and Markov chains. Emphasis on newer techniques such as quasi-random methods of integration, the EM algorithm and its variants, and hidden Markov chains. Applications as time permits to areas such as genetic and medical imaging.