Course Director: Wodan Ling, PhD
This course provides an introduction to the fundamentals of Python programming and the basics of data analysis and scientific computing techniques with Python. The course will teach basic programming components, including data structures, control flows, functions, and classes; data processing via the libraries – numpy and pandas; data visualization via the libraries -- matplotlib and seaborn; basic statistical analysis via the library – scipy; Monte Carlo method (including random number generation, simulation, and numerical integration) and numerical optimization and how they are applied in biostatistical and data science practices. This course will emphasize hands-on programming as well as the theory and methodology of computing techniques. Every week there will be a 1.5-hour lecture followed by a 1.5-hour in-class programming exercise (with some question-answering problems). The exercise is intensive and not expected to be finished within the class but serves as the weekly assignment that is required to be submitted before the following lecture. There will be a mid-term exam and a final group project (evaluated by report and presentation).