I would first like to start this blog by saying how surprising it is to think that this will be my last blog as an EngSci p2 blogger, and also that I am nearly halfway through my degree. To wrap up all the blogs, I essentially asked actual part 1 questions I knew, wanting to choose Engsci next year, to provide me with questions I should answer in the form of a Q&A-type blog. So, here are the questions they asked:
Q1: What do you expect the Engsci cut-off to be for next year?
I personally think the EngSci cutoff has been kinda unpredictable and has been slowly dropping over the years. Back in 2019, it was around a 7.1 GPA, and for most of the late 2010s and early 2020s, it hovered near 6.0. For my cohort, however, the cutoff ended up being N/A, meaning that anyone who wanted to pursue EngSci could gain admission regardless of their GPA. This was likely due to a quant guy who pretty much advertised engsci as “ENGSCI is the quant trading spec. Do ENGSCI, be like me, make $200k a year, <implying then drugs and hookers>” in the industry talk for engsci, which led to like 20-30 people losing interest in my spec and now led to low gpa (part 1 gpa <5) people who failed to get into mechatronics/mechanical wanting to get into quant trading 💀 but also hate the spec cause Engsci isn’t as hands on as what Mechanical/Mechatronics is. Engineering science was also the most popular second choice, interestingly, so people were certainly considering it as a backup specialisation, especially if they did not get into Mechatronics/Mechanical. This year, the industry talk seemed good, and there certainly was a good ammount of propaganda being spread around by the goat Kevin Jia in your part 1 lectures, which led to a lot more people being interested in Engsci. So, I definitely do expect a cutoff, but not a high cutoff (maybe like a 2-4 GPA cutoff is what I expect).
The reason I expect a low cutoff is also due to the j*b market being poor, and many people thinking that employers won’t properly understand what Engsci is, so it’s a bit risky to have a degree they are not familiar with. Furthermore, Engsci does have a software vibe to it (even though we do a lot more than what an SWE does and don’t focus too much on software engineering in the degree). Everyone thinks that AI can code right now and is scared to enter a more software-based career, so they do not choose anything related to it, which leads to a lower cutoff for fields like engineering science.
So yeah, I definitely think there’ll be a cutoff, but a low one. My advice would be not to stress about what it’s going to be. Just focus on doing the best u can in Part 1 and getting a solid GPA. If u actually like EngSci and what it teaches, ur GPA won’t be the thing stopping you from getting in.
Q2: Favourite/underrated lecturers in ENGSCI?
Tbh, most of the lecturers I have had in Engsci have been nice and excellent at communicating all the content to us. In terms of my top 5 personal favourite lecturers, I would prob say it’s Kevin Jia (teaches ENGSCI 211, ENGSCI 263 typically), Peter Bier (teaches ENGSCI 233 and also taught u all ENGSCI 111), Justin Fernandez (teaches BIOMENG/ENGSCI 221 typically), Julia Musgrave (teaches ENGSCI 233) and Andrew Mason (teaches ENGSCI 255 typically). All of these lecturers were very clear in explaining their content and are highly praised by the cohort for how well they teach us the complicated concepts we need to know. In fact, my cohort praises at least 90% of the lecturers in a positive way. So, I believe none of the lecturers I have are underrated imo. Also, luckily, in part 2, I have never encountered any lecturers who were bad at what they teach or intentionally wrote hard assessments to fail half the cohort (although I have definitely encountered a few lecturers who were a bit unorganised, but they were all good at what they had to teach). Also, do keep in mind that the lecturers change every single year so you may not get the same lecturers as me but nonetheless, I can pretty much assure you that the ENGSCI department hires the best of the best people to teach all the content and all the lecturers you have are very fair in terms of giving you something that is of a fair level to whats expected of you.
Q3: How to get started with a personal project?
(Here, I can only answer software/data science-related projects because that is what I have experienced so far, but I think some of this advice could apply to a mechanical or a more hardware-based project as well)
The best way to start a personal project is to stop overthinking and just look around you. Every great project starts with spotting a small problem that annoys you or someone else and deciding to fix it. It doesn’t have to be world-changing; it could be something like automating a boring task, analysing data from your hobby, or building a tool to make student life easier. For example with me, I decided to make a GPA cut-off predictor for fun and also cuz it’s so hard to actually statistically predict cut-offs of different specialisations at university. I applied my machine learning and scikit-learn libraries skills and used as much data as I could find to build my project. If you want to do a machine learning or data-science type project, make sure you have a big dataset to work with (I didn’t with my GPA predictor app and that caused a big issue in terms of how accurate my model was). But regardless, the most important thing is that the personal project should be about the problem you, yourself, are passionate about solving.
Once you find that problem, break it down into smaller steps and learn what you need along the way. Use those smaller steps as a guide to write different functions of your project and then integrate all the functions to work together in the end. You’ll pick up the technical stuff naturally when you have a clear goal in mind (imo it’s easier to learn how to program if you are building code to solve a problem you want to solve rather than doing coding for the sake of coding or leetcoding). The hardest part isn’t coding or designing, it’s choosing one idea and sticking with it. So, find something that sparks your curiosity, start small, and build something that actually makes life better for you or the people around you.
If you do not have any idea about any problem you want to solve or need a more laid out project (so u can focus on ur programming skills being better rather than the design process of things), I would recommend looking at past year’s or even your year’s matlab project but doing a similar version of them in python/any other language (so u can like find similarities between the languages of different features).
Q4: If your EngSci degree came with an annual subscription, what perks would it include?
To that, I would keep my response short and simple: a tight-knit group of close friends, stats assistance centre and quick ed-discussion replies.
Q5: What are the key differences between Engsci and Mechatronics?
EngSci and Mechatronics overlap in areas like maths, physics, and programming, but they focus on completely different ways of thinking. Mechatronics is about building things, eg, designing robots, wiring sensors, programming motors, and physically making systems work. EngSci is more about analysing those systems, modelling how they behave, and understanding why they succeed or fail under certain conditions. For example, a part of what Mechatronics is about is actually building a drone and setting up all its parts and control systems inside the drone. EngSci is about analysing how that drone performs under different weather conditions, or calculating the probability it’ll fail under strong winds. Another example could be looking at production lines. Mechatronics students actually design the mass production/manufacturing lines of a product, but EngSci students are meant to analyse the production line designed by the mechatronics students to see if it works to produce the goods in the most optimal way possible. EngSci sits at the intersection of data, modelling, and optimisation, while Mechatronics leans toward design, prototyping, and hardware. Both need problem-solving and coding, but EngSci is the “why” and Mechatronics is the “how”. You need both kinds of minds to make engineering ideas actually work.
Q6: What spec out of Mechatronics and engsci is more likely to land an internsh*p (assuming 2 students, one from each spec, apply for the same role)?
I honestly don’t think either spec has a major advantage because they target slightly different kinds of roles anyway. EngSci students usually go for internsh*ps that involve data, modelling, software, or optimisation – things like analytics, simulation, or even consulting. Mechatronics students tend to go for design-heavy, hands-on roles that involve hardware, robotics, or control systems. If both applied for the same j*b, it would really come down to the specific company and how well each person markets their skills. For example, if it’s a drone company, the Mechatronics student might work on the hardware and sensor side, while the EngSci student might model how the drone behaves in extreme weather and predict the risk of failure. In the end, it’s less about which spec you’re in and more about what you’ve done and your projects, portfolio, and initiative matter way more than your title. Regardless, I do not think one should pick a spec just cuz it has more internsh*p opportunities, but rather pick a spec based on what they find more interesting to study. I think the j*b market is cooked regardless of which spec you are doing right now.
Q7: Is it crucial to be good at coding in order to thrive in engsci?
I think in part 2, you can probably get by without actually being too good at coding (cuz u can do more mech-based electives that do not require any coding, but even the data-science-based electives do not require too much coding). I think in engsci, the coding we do is very different to the typical SWE coding and is more about engineering computation, where you are expected to write code to solve an ODE rather than being expected to write code to actually make an app. So, if you are good at the MM type papers, u should have no problem with the coding we do in ENGSCI. Also, you are expected to write code to make good-looking graphs in Python and R in EngSci. However, the coding to actually make the graphs is typically offloaded to ChatGPT (in the past, when ChatGPT wasn’t around, people needed to write all the code manually to actually manipulate and plot their datasets), but right now, to make complex graphs, everyone just asks ChatGPT for the code, and it is acceptable to do that in at least ENGSCI 263 because in engsci, you are meant to spend analysing the graph output rather than actually spending ages writing code for graphs. In part 2, you are also expected to know how to code up the eulers method in python (as well as improved eulers and RK-45 method {which is the same as ode45 function u learnt in matlab} from scratch) and doing all of that does require a good knowledge of all the coding u learnt in ENGGEN 131 but its not too bad to code because its just a more complex for loop (you just need predefine the number of steps/iterations and step size and then repeatedly update the eulers method formula at the end of the day).
Q8: In part 2, what’s the balance between ‘memorising content’ vs ‘problem-solving’, on a scale of pre-med to electeng-test-1?
I would say Engsci, on average, is close to the middle of that scale and has a good balance between ‘problem-solving’ and ‘memorising content’ as a whole, but each individual courses share its own scale. In courses such as ENGSCI 205 (machine learning), it is surprisingly more memorising content rather than understanding content because the lecturers expect you to recall definitions without cheatsheets. In other courses, and in fact most courses, lecturers allow cheatsheets, but they do give problem-solving questions in there, but they do not give like ELECTENG 101 level of unfamiliar problem-solving (and they still have marks for recalling some of the content and formulas, but that content is not typically expected to be of a word-to-word definition). For the 2025 p1 cohort, having seen both of your ELECTENG 101 tests, I would say all of the part 2 engsci tests of this year have been of a much easier level than that (even our ENGSCI 263 test that got massive scaling by 11% {the test out of 30 originally was made to be out of 27}). Regardless, you do get to do a bit of problem solving, particularly around analysing what the numbers you calculated mean in terms of the context of the problem you are solving in many engsci tests.
However, since each individual shares their own level, I would attach what I (in my subjective level) feel where that boundary is on the scale below for each of the courses I did.
Q9: What would you say your least favourite part of EngSci has been so far?
I would say the fact that we do not actually get past tests/exam answers that much, and also the fact that the lectures can get a bit content-heavy because many courses just have 2 lectures a week (I think in part 4, most had 3 of them and some had 4 but many of courses only had 2 lectures and some had 3). So, if u miss a lecture, it’s a lot of content to catch up on.
Q10: What are the similarities and differences between the Chemmat121 course and the EngSci materials analysis courses (eg. BIOMENG/ENGSCI 221)?
I would say something like BIOMENG 221 has a very similar assessment weighing structure to something like CHEMMAT 121. Both of these courses have a Canvas quiz as a test (although in the second year, this quiz is held invigilated in a computer lab, but the test is still open book). Both these courses right now have a big 50% exam as well.
However, the material analysis courses in your second year are actually a lot less memorisation-heavy and a lot more about applying what you know about material properties to different applications. A lot of BIOMENG 221 is also just recalling formulas and applying them to the right situations. For example, below is a past exam question of the course that I think u can somewhat understand.
We had to model a composite material as a ‘thermal’ electrical circuit (u will learn all the content behind it in the second year), but you can prob see how something like this would be a lot less memorisation-heavy than Chemmat 121.
So, pretty much the material course in the second year is a lot more about how we can model things to solve problems rather than actually memorising or rote-learning the content about how each material behaves. We also, in fact, extend on enggen 121 statics in this course and further apply what you learnt about statically determinate beams to actually analyse more complex structures (and in those analyses, we also look at shear and normal stresses, so a bit more of first week chemmat in there).
The end….
So, that’s all the questions I got from part 1 that I knew. So, to everyone who’s read even one of my blogs or ever messaged me asking about EngSci, tysm. I hope something here helped u make sense of what this degree really is and that the propaganda I laid out here actually spread to you. And if you have any more questions about Engsci or my blogs, feel free to put them in the comments. That’s all from me, signing off as your part 2 EngSci blogger one last time 🔥.