STAT2203 – Probability Models and Data Analysis for Engineering46.4
This was perhaps my least favourite course in my degree so far.
I can imagine a world where this content was interesting, and where I was engaged during a lecture. And I'm sure the lecturer is a smart guy and whatever, but's he's just so god damn boring.
To make it worse, we didn't get the whole course workbook at the start - it was released in chapters over the semester, so any chance of printing it out and taking nots was lost.
There were also 6 assignments which are all small enough to feel like you don't need to do them, but tricky enough to stuff up your entire schedule. They often covered stuff that was covered in lectures/tutes and we really had no business knowing. If you take this course, I'd suggest you sign up to Maths StackExchange and get ready to spend every third weekend in tears, trying to understand what this question is even asking.
The other thing is that, if you get behind at all, it makes catching up/understanding anything that's happening in lectures almost impossible since the course content is building upon itself so heavily.
If you're thinking about doing this course, it's probably required, so commiserations. Otherwise, for your own sanity, don't touch this course with a 10ft pole.
STAT2203 – Probability Models and Data Analysis for Engineering48.3
As this is the only statistics course I've done, it's hard to draw comparisons. In my year the lectures were quite boring and difficult to understand. Hopefully this year is a little better as it was the first time the course had run. Tutors were quite willing to help, as was the lecturer. That said, most concepts required sitting down with a tutor, lecturer or others in the course to fully understand. (I stopped attending lectures about four weeks in) Lecture recordings were not available but will hopefully be implemented this year.
This course also assumes an understanding of some mathematical concepts not taught in the prerequisites. Be prepared to do some background reading before lectures.