Timed trees: introduce the concept and how they can be estimated. Could introduce both ML and Bayesian methods. As a practical could just use treetime.
Typing schemes: introduce MLST, cgMLST, pros and cons.
Visualisation: additional and/or more advanced phylogeny visualisations using ggtree (but this adds an extra hard prerequisite)
Lineage assignment and clustering: a larger dataset (maybe starting from preprocessed FASTA files) where fastBAPS can be run. To cover the workflow of generating an initial tree, cluster with fastBAPS, generate reference-based alignments for each cluster, followed by gubbins and iqtree
treetime
.ggtree
(but this adds an extra hard prerequisite)