Every EngSci student’s journey kicks off the same way: you turn up to the Part II field trip with a backpack, a hard hat, and only a faint idea that you’re about to be thrown into something intense. I covered that trip in detail in my last blog, but once it’s over, the real grind begins – endless internsh*p hunt, assignments, tests, and exams. To survive that chaos, you start unlocking the tools that make EngSci not just manageable, but powerful. There are plenty of tools we use throughout the degree, and listing every single one would make this post a novel. So instead, here’s a look at the ones that became my go-tos in the second year and showcase the wide range of things you encounter in your engsci degree.
(On a side note, I will not be naming the full papers I talked about here for most cases in this blog, so it may be worthwhile to check this page to know which course I am talking about.)
So, 🥁, tools I mastered and now I can’t survive my degree without are:
1) Chat GPT
This one comes with a caveat: don’t use it to cheat or to churn out assignment answers. Instead, treating ChatGPT like a 24/7 tutor who can quiz you, summarise concepts, or whip your messy notes and conceptual knowledge into crisp tables for cheat sheets is what’s your actual survival tool. Note that many courses in Engsci have either the 10% rule or 50% exam weight, so if you use ChatGPT the wrong way and cruise by, u will probably be failing your exams and, consequently, not doing well in the course. Also, ChatGPT is, shockingly, not that good at data analysis, so blindly copying and pasting its output can lead you to do poorly on the assignments in some cases. However, if properly used, it can be an excellent tool for sharpening your understanding, speeding up revision, and turning overwhelming lecture slides into bite-sized knowledge. Think of it less as a shortcut and more as your personal trainer that pushes you to articulate concepts clearly and keeps you honest about what you know. Used this way, ChatGPT doesn’t make it easier; it makes you better at playing the game.
2) A Good IDE ( eg, VS Code or PyCharm) and Jupyter Notebooks
By the time you reach ENGSCI 233, coding becomes the battlefield, and your IDE is either your weapon or your downfall. VS Code is the default for most EngSci students because it is fast, lightweight, and endlessly adaptable, making it perfect for switching between multiple languages during weeks of juggling assignments.
PyCharm, though slightly slower to launch, shines when projects get heavier. Its integrated unit testing tools make it slightly more useful than VS Code for ENGSCI 263 and ENGSCI 233, letting you run individual tests with a click, view results in an organised panel, and debug without leaving the test runner, which turns out to be a huge time-saver in both group and solo work. I like to think of VS Code as your nimble, everyday use multi-tool and PyCharm as the heavyweight armour to verify my work for high-stakes battles.
Then there is Jupyter Notebooks, the go-to for data-heavy courses like ENGSCI 205. Jupyter notebooks work somewhat similarly to the R notebooks you encounter in MM2, tho the code is, ofc typically written in Python. Jupyter notebooks let you write code, test it in chunks, and instantly visualise results alongside explanations, turning raw computation into clear data storytelling. Having both a solid IDE and Jupyter ready is essential; I have seen many people crash out in ENGSCI 205 labs simply because they did not have a good IDE and a Jupyter notebook properly installed. So, master how to optimally install and use these tools, and you will thrive when you come across courses where coding takes the centre stage.
3) Python Libraries
Your journey widens when you take electives that dive into machine learning or data analysis, or well, not just electives but even the core engsci papers in general, because that’s when Python stops being just syntax and becomes the universal language of engineers and data scientists alike. The Python libraries you encounter (e.g., Pandas, NumPy, Scikit-learn, and Matplotlib) are both coding conveniences and the backbone of modern analytics. Pandas lets you wrestle massive datasets into clean, structured tables you can actually work with, while NumPy makes the heavy lifting of linear algebra and matrix operations almost effortless, turning calculations that would be painful by hand or require heaps of iterations (an endless amount of for loops) to solve into instant results. Scikit-learn then takes things up a notch by giving you ready-to-use tools for regression, classification, and clustering, letting you dip your toes into machine learning long before most engineering students even touch the subject. In fact, ENGSCI students learn more about machine learning than ECSE (electrical, software or compsys) students. So, if you’re into Machine Learning, Engsci becomes a great spec to gain its foundation. And Matplotlib ties everything together visually, allowing you to plot, analyse, and present your results in a way that makes your work feel less like homework and more like a professional report. Together, these libraries make you realise that EngSci isn’t just about passing assignments, it’s about building the same skills and workflows that real-world engineers, data scientists, and researchers depend on daily.
4) Microsoft EXCEL
Most people think of Excel as nothing more than a dull spreadsheet tool for budgets and admin work, but in EngSci, it gets completely rebranded into something far more powerful. You take it to the next level by using tools such as Solver to tackle complex optimisation problems: plug in inequalities, constraints, and objectives, and suddenly Excel becomes an engine for finding the most efficient solution using the simplex method, whether for minimising costs or maximising resources. The fun doesn’t stop there – EngSci also pushes you into exploring PERT charts, where you play with uncertainty in project timelines, map out dependencies, and identify the critical path that makes or breaks a project plan. Watching a spreadsheet reveal the bottlenecks of an entire system is oddly thrilling. At the same time, you complement Excel with Python (although you don’t get to do that in second year Engsci), which gives you the power to go beyond Solver’s limitations by automating workflows, modelling larger datasets, and experimenting with optimisation methods at scale. The result is that EngSci students don’t use Excel the way everyone else does: we weaponise it, and in our careers, we can combine it with Python and analytics to solve problems that feel more like engineering research than “just spreadsheets.” EngSci turns tools others overlook into instruments of real engineering power. In fact, some of the cool stuff I did with Excel in ENGSCI 255 is shown below.
5) The Stats Assistance Centre
Most specialisations have part 2 assistance centres where students can get direct help with second-year papers, but EngSci doesn’t offer that luxury. Instead, you’re pointed toward the Stats Assistance Centre for specific stats-based papers, which works to your advantage. The centre becomes a lifeline for courses like STATS 210 and ENGSCI 255, where regression analysis, linear programming, probability distributions, and hypothesis testing can easily turn into traps if you try to battle through alone. Here, experienced stats GTA’s break down ideas that initially feel abstract, showing you how to apply them to real EngSci problems like optimisation under uncertainty or interpreting the data behind your models. So, the con of not having a part 2 assistance centre is somewhat balanced by having a stats assistance centre.
6) Study Guides
When exams roam around, panic is usually the default mode, but Engsci students know where to find their secret weapon: the study guides for papers like BIOMENG 221 and ENGSCI 205. These aren’t just notes; they are carefully structured checklists that map out every learning objective, making it clear exactly what the assessments can and will test. Instead of drowning in lecture slides and trying to second-guess what’s essential, the guides cut through the noise and give you a clear plan of attack. It feels less like chaotic last-minute cramming and more like walking into a campaign with intel on the enemy’s weak points – you know where the traps are and where the easy marks are hiding. For BIOMENG 221, the guides are lifesavers for wrapping your head around stress, strain, and failure theories, while for ENGSCI 205, they make the endless swirl of the data science process more digestible. Having these guides doesn’t just help you pass; it makes you feel like you’re playing the game in “insider mode,” with knowledge that streamlines your revision and makes your exam prep brighter, sharper, and infinitely more efficient than the average engineer.
7) Ed Discussion
One of the most underrated tools in the Ed Discussion. It’s the place where confusion meets clarity; honestly, it often feels like magic how quickly lecturers respond. Most of them are ridiculously fast, even past midnight, and they’re dropping replies that cut through the noise and directly address what’s tripping you up. What makes Ed special is that it’s not just about pointing out mistakes; the lecturers genuinely want you to pass, and you can feel that in the way they carefully break down problems, highlight common pitfalls, and sometimes even add extra context so you understand the why as much as the how. And then there’s Liam Fisher, who has basically become a legend in his own right. His responses don’t just answer the question; they read like mini-lectures, complete with step-by-step logic, annotated equations, and explanations that make challenging concepts finally click. I honestly call him the William Lee of Ed Discussion because his clarity, patience, and dedication mirror the gold standard that William Lee set for ELECTENG 101 in the first year. Whenever a challenging problem shows up, chances are Liam has already written a reply that is so thorough that it feels like a personalised tutorial. In fact, Liam is just one of the lecturers, and Engsci also has a bunch of other lecturers who reply as fast as he. In fact, I have attached the way Liam responds to your Ed questions. For EngSci students, Ed isn’t just a forum; it’s the frontline where lecturers prove they’re on your side, turning panic into understanding in record time.
8) Monster Americano
At some point in EngSci, you’ll find yourself staring down an all-nighter, and that’s when Monster Americano becomes less of a drink and more of a lifeline. Whether it’s an ENGSCI 263 group project that somehow expands into a week’s worth of work crammed into one night, or just a pile of assignments that all decide to be due on the same Friday, Monster Americano is the glue that keeps you conscious. One sip and suddenly you’ve got enough energy to argue with your group about which assumption to include in the model, debug code that definitely worked yesterday, and still have room left to format the report at 2am. In fact, Monster Americano doesn’t just power individuals, it fuels teams. Passing around a can in the early hours somehow builds friendships because you’re not just surviving the chaos you experience, you’re thriving in it together. In EngSci, coffee is for amateurs, but Monster Americano is for students who know the night isn’t ending until the simulation runs and the report is submitted.
When you line up all these tools, it becomes clear why EngSci is in a league of its own. It’s a degree that refuses to box you into one narrow path; instead, it hands you a toolkit that works everywhere (From optimising supply chains to predicting market trends, modelling geothermal systems, or building AI tools that can outthink yesterday’s best). You don’t just learn how to pass exams, you learn how to think across disciplines, speak the languages of engineers, data scientists, and consultants all at once, and adapt faster than the problem can change shape. That’s why EngSci attracts the people who want options and people who want to walk out with the skills to work in almost any technical or analytical field on the planet. It’s not just about specialising, it’s about being the specialist that every team wishes they had. In a world where the hardest problems don’t fit neatly into one box, EngSci builds the people who can work across all of them, and that’s why all the EngSci-ers are built differently 🙂