This advanced statistics course covers advanced topics in statistical inference and modeling. Students will learn advanced statistical techniques and methodologies, including maximum likelihood estimation, Bayesian inference, and linear mixed models. The course also focuses on the application of these methods in the context of biological data, such as gene expression data, sequence data, and proteomics data. Students will gain practical experience through hands-on programming exercises and projects and will develop the skills needed to analyze and interpret complex biological data using advanced statistical methods.

CILO-1: Apply statistical theories to address public health issues. research using quantitative research methods CILO-2: Contrast statistical modelling (Maximum Likelihood Estimation, Bayesian models and Mixed Models), and apply them to data analysis in public health research. CILO-3: Apply nuanced statistical methods (time-series, survival, multivariate and high-dimensionality data analysis) in public health research. CILO-4: Apply the skills in bioinformatics data analysis of genetic, the data analysis process and the importance of these to public health sciences. CILO-5: Conduct data analysis and data visualization for communication of key findings using R language and other bioinformatics tools. CILO-6: Collaborate effectively to develop and present a research project by applying advanced statistical methods to a specific bioinformatics problem.