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NUS Statistics Module Review: ST4234 Bayesian Statistics

Lecture attendance is not compulsory. Each tutorial set is assigned to a group of students (which is assigned around the start of the module. Students get to choose who they want to team up with), and each group only need to be present for the week in which their tutorial is presented. Prof. Li mentioned explicitly that the grading only looks at completeness instead of correctness. Tutorial questions generally are standard questions which apply techniques already demonstrated in lecture, so as long as one pay attention in class, they should not spend too much time on tutorial sets. The tutorial problems also require extensive coding in R, and students are generally assumed to be somewhat proficient in R before enrolling in tis module. Due to the COVID-19 situation, attendance to tutorial was no longer compulsory, but Prof. Li strongly recommended each group to send at least one person to present their solutions.

The data analysis project was just a slightly extended version of a tutorial set, covering concepts across multiple chapters. It was issued near the end of the semester, and we were given about 2 weeks to complete it.

I call modules like this a “methods” module: Many techniques and concepts are introduced in order to solve many practical problems, but rarely are these concepts explained in depth or even proven. As such, one does not need an excellent understanding in order to do well in this module – as long as they practice the tutorial problems consistently and know what step to carry out in each scenario, they should score decently well. I found this somewhat unfortunate, as many concepts look interesting enough to be explored further. Regardless, the concepts are still not easy to understand at the surface level, so one should prepare to spend quite some time on this module (while I think it is possible to do well in this module without really knowing what is going on, it is much more difficult and painful and is probably not worth it).

Prof. Li is the best statistics lecturer I’ve encountered so far. He has a good intuitive grasp of the concepts and is able to deliver them in a reasonably fluent manner. He’s also a highly approachable and friendly professor who helps his students whenever they are in need, both offline and via email. However, his lecturing voice tends to be monotonous, so it’s easy to lose attention in the middle of his classes. Regardless, taking this module under him was a good experience.

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