I was just trying to enjoy my day out but the administration kept dropping my grades on me so I'm hitting back. I took 6 courses this semester, here are my reviews in ascending order (bad to great).

IS1108 Digital Ethics and Data Privacy

Grade: Unsatisfactory

This course does not even deserve the dignity of a letter grade. No one takes it seriously, and everyone intends to S/U it anyways. It's a course where the professor uses AI to generate lectures for students to use AI to generate assignments for TAs to use AI to grade.

I don't blame the School of Computing for the existence of this course. It's literally just how I imagine NUS Admin hedges against the possibility of an NUS graduate creating weapons of mass destruction and bringing bad press. It's just so they can say "But look! We taught them ethics at college!"

What a shame, because a real ethics course where we study actual ethics (i.e. moral philosophy) and how it informs the actual laws and policies surrounding technology would have been so incredibly cool. Oh well. At least I had a really fun group :D

MA1521 Calculus for Computing

Grade: F

When I attended the first lecture in the first week, the theatre was already half-empty, and the students were louder than the professor reading off his slides. At some point, someone packed up and walked out mid-lecture and once he broke the tension, everyone just started streaming out.

I think it's phenomenal how the professors managed to make one of THE most interesting areas of math feel so lifeless. I only attended my tutorial once, but the TA was excellent. He showered us with great resources on the Telegram chat and some pretty mind-blowing explanations. Weirdly enough, I had fun preparing for the final exam. I'm going to miss not having any more math courses after one last statistics course next semester.

Overall, this course was a void sandwich - I only really noted its existence on either end of the semester.

GEA1000N Reasoning with Data

Grade: B-

People keep asking who this course is actually for. The answer is me. This course is for people like me.

Before University, I had practically no experience with statistics. This course was really humbling. My statistics intuition is atrocious, and I've been woefully ignorant of concepts like populations, samples, confidence intervals and hypothesis testing my whole life.

Honestly, I'm grateful to have been able to take this course with Prof Loo Yoke Leng. She is such a gem. She cares so much about making sure we understand what we're learning, and I feel like I've asked her the same questions a thousand times but she's always been so patient.

I think the worst parts of this course are the parts that Prof didn't have any control over. Things like the comically irrelevant tutorial sheets, and poorly designed group project. I wish they gave NUSC greater agency with GEA, because I really feel like Prof Loo would do a great job.

CS2030S Programming Methodology II

Grade: A-

CS2030S is a standard Java Object-Oriented Course that slowly morphs into course about programming languages and functional programming. We start off in a place that's new, but seemingly end up exactly where CS1101S ended off.

It's genius, because while Java is indeed a great language to understand type systems and engineering paradigms, it is also horribly frustrating to work with. And over the course, we slowly get some insight into why it was designed the way it was.

I also really like how they get students to pick up Linux, Vim, and even a bit of Git on the side. The lab exercises were stupidly tough sometimes, but I actually really appreciate them. Mostly because I've been learning Rust, and we happened to be building many of the abstractions that I was taking for granted.

What knocks it down for from top-tier for me are the boring lectures, and the mind-numbing labs (had a particularly sleepy TA). I started skipping lectures a few weeks in, and spent the compulsory labs learning Rust instead. This years assessments were also easily significantly harder than all the past years and I imagine it's only going to get harder in the future.

Okay but the recitations were 11/10. Prof Ooi Wei Tsang is probably my favourite professor of this semester. He made concepts digestible so concisely, even when I wasn't keeping up with my lectures. I'm really hoping he wins a teaching award.

CS2040S Data Structures and Algorithms

Grade: A

This course is just plain fun. The lecturers, TAs and Tutors all visibly love what they teach, and that enthusiasm really rubs off.

The lectures are as entertaining as they are informative. Prof Eldon is absolutely manic, and sounds so terrifying on 2x speed that I started going for lectures live. He's even better in person.

My only gripe (and probably my biggest with the course overall) is the conscious effort to avoid ANY mathematical rigor. I get that discrete structures was a tough course and that many students struggle with it, but it was a prerequisite. I don’t think it’s unreasonable to expect students to brush up on that foundation rather than have this course simplify content to accommodate the gaps. The few times they delve into real proofs for the algorithms were probably the highlights of the semester - especially the lecture on AVL Trees.

I can't really pin down the pedagogical purpose of the recitations, but Eldric basically used them to essentially used them to model the kinds of thought processes we should have when solving problems. Eldric is really one of a kind, realistic yet approachable, bringing down real-world problems to our level and connecting them with our concepts. I imagine different tutors have entirely different approaches, and my only regret is not attending recitations by other profs like Ben Leong.

I had the best TA. He's so fast at identifying counter examples when we screw up. He's incredibly knowledgeable, took fascinating tangents while being mindful of the time, always connected our theory with practical implementation details. I think he was just the icing on the cake. The problem sets vary in difficulty but they're all just great fun. I do wish they didn't use Java for this course, because grappling with this obnoxiously verbose language often ends up obfuscating the core logic of algorithm design, but I guess this was something I would have to eventually pick up anyways.

Overall, Peak Lecturer, Peak Tutor and Peak TA. Whatever peak finding algorithm they used to find the people to run this course was definitely correct. I'm going to miss this course dearly.

NGN2001E Global Narratives

Grade: A+

Every week for NGN, we consume and discuss a new text - often from different parts of the world, and in different genres. Every week Prof Hannah would begin by asking, "Anyone here a fan of [insert genre]?" and every week I found myself raising my hand. I couldn't help it, I just love a good story, and I love it regardless of genre.

I've scarcely gone a week without tearing up preparing for this course. Sometimes because the reading is so moving. Sometimes because it's so achingly beautiful that I felt an existential inferiority, that I could dedicate the rest of my life to a story and not tell it half as good. Sometimes it was both.

Beside the readings and discussions, Prof Hannah gave out some of the best handouts ever. Tips for writing better, asides about tangentially related topics, and introduction to more abstract literary concepts like gender theory or cultural relativity. I adored these asides. I could tell prof was holding back because this is an introductory course, but GOSH I wish I could take a proper literature course under her.

It is telling that for two semester in a row, my favourite course has been a humanities-coded course. I love being stylish in my assignments. I love that my risks get rewarded. I love how intellectually stimulating it is to consume, reflect and discuss something new every week. I love sitting with these stories and how much bigger the world feels every time. I felt genuinely excited waking up at 7am to attend this 8am class, and I'm so, so terribly afraid I won't be able to scratch this itch once I graduate and start working.