Just a fun onboarding pack. Continuously updated (hopefully).

Overall Review

It’s great, but keep your expectations in check. Their purpose is to educate you, teach you to think, and show you how the world works. Their purpose is NOT to get you a job.

Academics

Whether you like it or not, and better start liking it, academics will be an important chunk of your life, and you need to take them seriously. Even better if you enjoy it, but not everyone does that, and it's fine too.

Parallel Pathways

The data science degree is a mix of math and computing, and that’s two parallel pathways you’re travelling together on:

Mathematics: Calculus + Linear Algebra → Probability (for Statistics) → Statistics → Linear Statistical Models → Modern Applied Statistics

Computing: Foundations of Computing → Foundations of Algorithms → Elements of Data Processing → Machine Learning → Applied Data Science

Both pathways are fundamentally important, and you may like one over the other, but you cannot ignore either.

Non-Optional Subjects

As a data scientist, you must know a few subjects that are technically “optional” (due to bureaucratic reasons) but are actually required to do well in your career:

• Data Analysis: Quick intro to the world of statistics, you can just do statistics, but you will have an easier time if you have done this before
• Database Systems: Information Modelling, SQL, Query Optimisation, Database Architectures. This is critical for any data science job.
• Digital Tools & Methods: Too bold of a move to include an arts breadth in here? It’s EODP, but well taught, and your analysis subject is just the arts.
• Object-Oriented Software Development: Daksh, now you’re just making me do a computing degree. Probably, but gone are the days when you could just chuck a notebook and the ML engineer will figure out the rest of it. Or you might wanna be an ML Engineer, good choice. Take this subject so you know (at least the basics of) how to make your analysis see the light of day.

The Compounding Debt

The worst hellhole I’ve seen students stuck in, including myself. Can’t say about others, but the data science degree. Everything in the third year builds on what you learnt in the second year, and the second year builds on what you learnt in the first year. So if you accumulated debt (half-understood concepts, challenging ideas, missed lectures), you might pass the subject, but it’ll get worse later as you’re destined to struggle with later ideas. A rule of thumb could be that for anything below H2A, you need to revisit the subject over the break and gain an H1-level understanding. Applies to math, applies to computing, applies to life.

How to perform well?

Stay ahead of the curve. Learn things before they’re taught, so university is solidifying concepts, not introducing you to them. Not for everyone, but for those who wanna do exceptionally well.