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NUS Statistics Module Review: ST5201 Basic Statistics Theory Statistical foundations of data science


– This module is essential a rehash of ST2131 and ST2132, with some parts of ST2132 omitted. The 1st half of the module is exactly ST2131 while the second half is about ST2132.

– Prof Choi isn’t a very enthusiastic lecturer. Her lectures feel more like rambling sessions, maybe because these concepts are too trivial and it’s not fun to teach easy concepts in Statistics. Her sense of sarcastic humour is quite amusing. Her lecture notes are really comprehensive and easy to read, which is a big plus.

– Homework assignments are manageable and you can google most of the answers online. Some parts of the assignments require you to do a little bit of programming to obtain some Monte Carlo estimates or bootstrap estimation. I just used Python for that instead of R/SAS.

– The midterm test was difficult for me because my probability fundamentals aren’t strong enough. I doubted my answer for 1 question, changed it and got the whole question wrong. Next, the 1st 20 marks worth of questions are T/F questions that have negative marking, so be careful of answering those. In addition, I get the feeling that Prof Choi is the kind of marker who looks at your final answer, then penalises your working. No partial credit is given for any wrong answers even if you did some correct steps, as is the case for my midterm script when I went to check it.

– The final exam was pretty manageable with 60 marks worth of T/F questions, no negative marking this time. The rest of the questions were quite manageable, with most of them being straightforward application of formulae or results from lecture notes.


– Recommended to postgraduate Math students who have done well for ST2132 to take this module as a substitution to 1 of the level 5000 Math modules.

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