Epidemiology, Mathematical Foundations for Biostatistics, Principles of Statistical Inference, Regression Modelling for Biostatistics 1
8-12 hours total study time per week
Two major assignments worth 40% each (equivalent to 2 x 2000 words) and two short assignments worth 10% each.
James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statistical Learning with Applications in R. Springer, 2003. (freely available online: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf)
R and RStudio
The topics covered include: Linear Regression and K -Nearest Neighbors; Classification (logistic regression, linear discriminant analysis); Resampling Methods (Cross-Validation, Bootstrap); Model Selection and Regularization (subset selection, shrinkage methods, dimension reduction methods); Beyond Linearity (fractional polynomials, basis functions, splines, generalized additive models); Tree-Based Methods (decision trees, bagging, random forests, boosting).
Course notes, online mini-lecture videos, online tutorials, discussion board