The aim of this module is to training professional statisticians for bio- and health informatics. Students are able to conduct correct statistical analysis for different types of data structures. Moreover, students are able to use correct statistical language to formulate and solve the newly encountered problem in practice.

This module includes the following five required courses, which account for 12 credits. 

1.  Computing in epidemiology and biostatistics(2 credits): Introducing the rationale of statistical computing. Students are able to write and implement the code.

2.  Statistical Inference in Data Science(3 credits): To learn the principles of statistics. Students are able to use correct statistical language to formulate and solve the newly encountered problem.

3.  Statistical analysis for repeated measurements(2credits): The advanced course of generalized linear model. To learn how to conduct regression analysis for repeated measurements data.

4.  Introduction and application of computational biology methods(3 credits): Introducing the aim of computing methods in modern researches of epidemiology and biostatistics, including the rationale of developing algorithms and their biological meanings.

5.  Analysis of Big Data in Health (2credits): Introducing the foundation and statistical methods for large bio- and health informatics. The course is based on real database and research results in the literature.

Students who complete this module will be equipped with the basic knowledge regarding the implementation of statistical analysis for various data structures. In the future, students are able to be the statistician or consultant in any data analytics related field.