This course will introduce students to biostatistics and data science. This course is intended for both students new to the area and those with prior training.

Statistical and data analysis methods covered will start with descriptive statistics and basic univariate tests and continue to more advanced regression models and other topics. The sessions will include lectures and hands-on tutorials that include real-time exercises. It is key that students are able to identify which methods to apply to what kind of data set, the assumptions of the model and how to interpret the output. Special emphasis in the course will be placed on critical thinking around analytical methods to be used.

Problem sets will be focused on the application of statistical modelling to the biological and health sciences. This may include laboratory or clinical data sets. Your defence of your analysis, as well as critiquing the work of others, will require you to draw upon some of your knowledge of biology and the health sciences.

A key component of the course will involve programming in R in order to conduct statistical analysis. Students will have both individual and team assignments to provide practice coding in R, one of the main languages used today in performing statistical analysis. Comfort with R will be helpful in learning other languages in the future in a statistical context. Off-the-shelf software, while more convenient, may not be available in the work environment you find yourself in and certain tests you may need, may not be available in any such software. Thus, learning to code is the best path forward for future practitioners of data science.