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HelpICantThinkOfAName:
Subject Code/Name: ECON3106 - Politics and Economics

Contact Hours:  2 x 1.5 hour lecture per week. 1 x 1.5 hour tutorial per week.

Assumed Knowledge: ECON2101. I'd also say take 2112 before this as well.

Assessment:

6 x 3% problem sets.  3 of these involved deriving equilibrium conditions for some setup, the other 3 were responding to empirical papers. I liked these, they weren't super intensive, but you did have to understand the material to do well on them.

16 x 1% online quizzes. 2 of these were due for each week of lectures. Just short multiple-choice quizzes (4-5 questions each), nothing very intense. You can repeat as many times as necessary. Free marks.

4 x 5% discussions. These were in-tutorial discussions about research papers that we were given a week to read. This is the weirdest part of the course for me, it seemed very difficult to actually get the marks for this, 3-4 contributions per discussion were needed from my experience, but when you have 30 people in an online tutorial it gets very messy very quickly - you're forced to gun for the marks early on so that nobody steals your idea and so you can actually get a word in.

46% final exam. This wasn't very difficult overall. 2.5 hours to do 2 short math questions, 2 short responses, and 3 multiple-choice questions. The final exam covered (at most) 10% of the course, and didn't take anywhere near 2.5 hours to complete. There were strange word limit requirements though, with one saying that your mathematical reasoning had to be strictly below 20 words.

Lecture Recordings? Pre-recorded lectures, four per week. This is one of the few times where I have felt that the pre-recorded lectures were superior to having live lectures, mainly due to the lecturers.

Notes/Materials Available:  Full slides.

Lecturer: Gabriele Gratton, 5/5. His lectures are always engaging and he was super helpful throughout the term. If you have the opportunity to take a course under Gabriele, take it.

Federico Masera, 4/5. Not quite at Gabriele's level, but close. A big improvement (in my opinion) over 2206.

Year & Trimester of completion: 2021/T2

Difficulty: 2/5.

Overall Rating:  5/5.

Your Mark/Grade: 82 DN.

Comments: This is such a great course. I wasn't very convinced early on, but the topics flow nicely from one to another, individual preferences lead to social choices, then to lobbying and corruption, political advertising, and conflict. The course cleverly integrates empirical data throughout to reinforce what the theory is saying, and closes with a lecture devoted to exploring empirical data. It's well structured, well-paced, well taught, and well assessed. If you're looking for a third-year economics course that isn't super intense, this is your new best friend.

Opengangs:
Subject Code/Name: MATH1041 - Statistics for Life Sciences

Contact Hours:
- 2 x 2 hour live lecture.
- 1 x 1 hour tutorial.

Assumed Knowledge:
No assumed knowledge is required for the course.

Assessment:
- 9 x online tutorials on Mobius (10% altogether)
- 1 x midterm (15%)
- 1 x assignment (15%)
- Final exam (60%)

Lecture Recordings? Yes.

Notes/Materials Available: Lecture slides are sufficient.

Textbook:
The recommended textbook is Introduction to the Practices of Statistics.

Lecturer(s):
- Lecturers: Dr. Nicole Mealing and Dr. Laure Helme-Guizon

Year & Trimester of completion: 2021, Term 2

Difficulty: 1.5/5 (the average MATH1041 student, though, may feel the difficulty is about 3-3.5/5)

Overall Rating: 2/5

Your Mark/Grade: 85 HD.

Comments:
I did this course purely because it was going to be a WAM boost but I'm disheartened by a few things from this course. My biggest complaint is the assignment. I'll get to that in a bit.

The course serves to be one of two math-intensive courses for people doing a life science degree, the other being MATH1031 which is more akin to the content you find on MATH1131. MATH1041 is an introductory course on statistical practices for people who may not want to do any more mathematics courses beyond this course.

For the most part, the course was just revision from when I took MATH2901 and a lot of the content felt very familiar to me. You learn about the basic probability theory principles as well as the basic statistical inferences that permeate any statistics major (confidence interval, hypothesis testing, inferences about the mean). The midterm was also really nice, we were given enough practice to perform well in the test and it felt like a nice 15% to get. The tutorials felt very dry and I felt that the tutors didn't want to be there (or at least I got that energy from them), which made me not want to go to the tutorials.

Then came the assignment. The assignment was convoluted and long, I felt like I was answering the same question 3 or so times. I was confused a lot of the times because the questions were worded awkwardly and left a lot of room for interpretation. When anyone tried to clarify a question, the answers were always the same: You're being assessed on it. When addressing a missing unit in the assignment, the lecturers didn't bother making a public announcement. Instead they simply left a comment in the thread that was so difficult to find because it was so hidden among the other questions that arose from the assignment. As a result, I had to redo one of the graphs because I realised oh there WAS a unit attached. I felt that, if we got marked down for it, there would be some major complaints. The marking was a bit controversial as well. A lot of assumptions that were not made clear from the assignment specifications were vital to how you were supposed to answer. This made the assignment even more frustrating to do. There were parts of the sample answer that I was not happy with and it felt like the markers were rushing to get marking done. Losing full marks for small errors should never be permitted.

HelpICantThinkOfAName:
Subject Code/Name: MATH3311/MATH5335 - Mathematical Computing for Finance/Computational Methods for Finance

Contact Hours:  2 x 1.5 hour lecture per week. 1 x 1.5 hour tutorial per week.

Assumed Knowledge:  (MATH2121 or MATH2221 or MATH2111) and (MATH2501 or MATH2601) and (MATH2801 or MATH2901 or MATH2871). Be very comfortable with all of these. I'd go as far as to say take all of the core second-year math subjects before taking this (yes, even complex analysis).

Also, take 2301. I found it helpful to have some decent background knowledge in Matlab, although there's no need to already know Matlab. There are free and accessible resources to help you learn, and it's quite simple to pick up if you're already decently versed in another programming language, or if you're learning from scratch!

Assessment:

4x5% Assignments. These weren't too difficult for the most part, mainly being able to translate mathematical logic into Matlab efficiently. There are a few times where you'll want to bang your head into a desk, but as long as you know a couple of tricks (such as not always needing to store an entire matrix, but just one vector that you can play around with for some recursive fun), you shouldn't have too much trouble with it.

20% Matlab Test. This was a bit scary in the leadup, but as long as you just grind out the lab work and past papers you'll smash this.

60% Final exam. Very little preparatory material for this, only past papers. There's no 30-page problem set for this course (unlike every other math course I've taken). The only stuff you're given are weekly self-study questions taken from past papers, so what'll happen is that you finish up the term and realise that there are no new problems for you to study. This made me feel very underprepared going into the final, and I had gotten near full marks on the assignments and problem sets. The final for MATH3311 is shared for MATH5335, so there may be some scaling up for 3311 students.

EDIT: I just finished the exam. What a shocking paper. There were large parts of that paper that we never once covered in lectures, never touched on in labs, and never appeared in past papers, and it certainly didn't come from the textbook or any of the prerequisite courses. I can only pray that we're scaled like hell, because I was only able to answer 25% of that exam with some confidence.

Lecture Recordings? Pre-recorded lectures, accessible from the start of the course. They go in-depth with all the topics, and even into some non-assessable topics if you're super keen.

Notes/Materials Available:  Full slides. As I said above, there are no problem sets for you throughout this course, so you're basically relying on remembering everything you learnt in second year. Some extra resources (even just some refresher questions) would've been helpful.

Lecturers:

Professor Josef Dick, 3.5/5.  He was alright, explained things well, managed to keep my attention.

Dr Leung Chan, 3/5. A bit difficult to understand at times, but alright for labs.

Year & Trimester of completion: 2021/T2

Difficulty: 4/5.

Overall Rating:  2/5.

Your Mark/Grade: 67 CR (seems like the final exam was heavily scaled)

Comments: For a course called Mathematical Computing for Finance, I expected a lot more of the finance side to come into play. It wasn't until week 7 that we first talked about Black Scholes, and that was only in the context of non-linear equations. The course isn't super focused on finance, nor solving problems related to finance.

The first half of the course is your typical "here's Matlab, here's what you can do, here's why it's a terrible idea to use Matlab". The second half covers numerical integration, random numbers, simulations, and PDE's, with a few equations used in finance dotted throughout. I would've much preferred that the course have a focus on financial applications from the start, or at least have problems that help you to see the connection between what you're learning and what is used by financial analysts. Because of this I never felt that the course went below a superficial skim of financial computing. I still learned a lot about Matlab from this course, and if it weren't for catfishing us by calling it computing for finance I'd easily give it a 3.5 or a 4. It just feels like what I learned wasn't quite what I signed up for, which is a shame.

That's a wrap on my math degree though! What a journey! From failing extension math in year 11 and being told that a degree involving lots of maths wouldn't be a good idea, to ticking off all the requirements for a mathematics degree! Excited to finish up my economics courses next term, and then (hopefully) start my economics honours next year!

Opengangs:
Time to put my remaining two courses up.
Subject Code/Name: MATH2400 - Finite Mathematics

Contact Hours:
- 2 x 2 hour live lecture.
- 1 x 1 hour live lecture.
- 1 x 1 hour tutorial.

Assumed Knowledge:
Pre-requisites are either MATH1081 or MATH1231/1241/1251 or DPST1014. Highly recommend doing this course after or with MATH1081.

Assessment:
- 2 x class tests (20% each)
- Final exam (60%)

Lecture Recordings? Yes.

Notes/Materials Available: Lecture slides are sufficient.

Textbook:
No textbooks for the course.

Lecturer(s):
- Lecturer: Prof. Igor Shparlinski.

Year & Trimester of completion: 2021, Term 2

Difficulty: 2/5 (although having done MATH3711 and MATH3411 beforehand played a major role in this difficulty score; it would probably be around 3.5-4/5 for someone coming into the course with only MATH1081 or MATH1231)

Overall Rating: 4.5/5

Your Mark/Grade: 97 HD.

Comments:
I didn't have to do this course but it ended up being one of my favourite courses this term. The course has given me a new profound appreciation towards MATH3711. The course goes through a lot of the abstract algebra concepts you would find in a course like MATH3711 without getting bogged down in the details of proofs. (Don't worry, there are proofs in the course but the focus is more on the applications of the concepts). Think of it as an introductory course to algebra and not the algebra you find in high school!

The course is designed to be taken alongside MATH2859 (hence, the 3UOC instead of the normal 6UOC). Don't get confused between 3UOC and the workload however! The workload is about the same as a normal 6UOC course and you should treat it as such.

The first half of the course is a revision of the number theory components of MATH1081. You revisit concepts such as the Euclidean Algorithm and divisibility. You then cover other algebraic structures such as groups, (commutative) rings and fields which become an integral part of the second half of the course (coding and information theory). So if you enjoyed modular arithmetic in MATH1081, this is a great follow up course for you to do! On the other hand, if you enjoyed MATH3411 and want to do a bit more on coding theory, then this course is also a great course for you!

Opengangs:
Subject Code/Name: MATH3611 - Higher Analysis / MATH5705 - Modern Analysis (postgraduate equivalent)

Contact Hours:
- 2 x 2 hour live lecture.
- 1 x 1 hour tutorial.

Assumed Knowledge:
Pre-requisites are 12 UOC of Level 2 Mathematics with an average mark of at least 70, including MATH2111 or MATH2011 (CR) or MATH2510 (CR), or permission from the Head of Department.

Assessment:
- 3 x assignments (10% for the first two, 20% for the main assignment)
- Final exam (60%)

Lecture Recordings? Yes.

Notes/Materials Available: Lecture slides are sufficient.

Textbook:
The recommended textbook is Introductory Real Analysis by Kolmogorov.

Lecturer(s):
- Lecturer: A/Prof. Pinhas Grossman

Year & Trimester of completion: 2021, Term 2

Difficulty: 3.5/5

Overall Rating: 4/5

Your Mark/Grade: 78 DN.

Comments:
Definitely the most challenging course out of the four courses I did this term, and it's not surprising why. This is one of three core courses for anyone planning to go into Pure Mathematics and it serves to be the more "calculus" heavy course out of the three. Essentially, this is a rigorous calculus class and you need to have a certain mathematical maturity to do well in the course. The lecturer doesn't cover many proofs but rather develops the intuition for what the proof should look like and it's your job to fill in the details, and it's a pretty nice system to have.

The course begins with a conceptual understanding of what it means for you to say "cardinality" of sets (in particular, infinite sets), covering topics such as countability and uncountability before diving into the first real topic of analysis -- metric spaces. You'll develop an understanding for abstracting away from the concrete (instead of talking about distance functions, we can talk about metrics of a space). The course ends with a fairly dense topic on compactness of topological spaces, a topological property that generalises the notion of boundedness and closed-ness.

In all, I found this to be a really interesting course and the lecturer does an excellent job at explaining these topics in a way that seems fluid and cohesive. If you're interested in pure mathematics and want to dive into some more calculus, then this is definitely a course for you.

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