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Building on a strong foundation in quantitative biology, fundamental statistical methods and basic R programming, students will learn an array of advanced biostatistical methods for data analysis. Topics covered include: data wrangling, methods for the analysis of designed experiments, regression analysis, including mixed effect models, and the analysis of multivariate data, including advanced supervised and unsupervised learning techniques. Requires students to apply their knowledge across a myriad of complex biological datasets.

Building on a strong foundation in quantitative biology, fundamental statistical methods and basic R programming, students will learn an array of advanced biostatistical methods for data analysis. Topics covered include: data wrangling, methods for the analysis of designed experiments, regression analysis, including mixed effect models, and the analysis of multivariate data, including advanced supervised and unsupervised learning techniques. Requires students to apply their knowledge across a myriad of complex biological datasets.

Building on a strong foundation in quantitative biology, fundamental statistical methods and basic R programming, students will learn an array of advanced biostatistical methods for data analysis. Topics covered include: data wrangling, methods for the analysis of designed experiments, regression analysis, including mixed effect models, and the analysis of multivariate data, including advanced supervised and unsupervised learning techniques. Requires students to apply their knowledge across a myriad of complex biological datasets.

Design and analysis of experiments for both field and bench scientists. Methods for the analysis of designed experiments, including analysis of variance with fixed, random and mixed effects; also, regression analysis and analysis of covariance. Methods for the analysis of multivariate datasets such as cluster analysis, principal components analysis, multidimensional scaling, and randomisation methods. There will be a practical component to this course involving the use of appropriate statistical software.

Design and analysis of experiments for both field and bench scientists. Methods for the analysis of designed experiments, including analysis of variance with fixed, random and mixed effects; also, regression analysis and analysis of covariance. Methods for the analysis of multivariate datasets such as cluster analysis, principal components analysis, multidimensional scaling, and randomisation methods. There will be a practical component to this course involving the use of appropriate statistical software.