As others have mentioned, how much and what kind of math you need depends heavily on what you do. And while I wholeheartedly encourage you to do what you enjoy, be it with or without maths, I would like to offer another perspective: A loveletter to maths.

Math in general gets a lot easier and more fun the longer you do it and the more interest you can build. Often the people that teach math are extremely good at it, and maybe because of that they suck at explaining it. There is a lot to doing it right.

First of all, I think you to build excitement. Math strives to describe the world! Math is the foundation of science, math is history, and many of the concepts and techniques arose out of necessity… Or sometimes spite! There are many funny stories or interesting people behind the formulars and concepts you encounter. Learning why the hell some math was even invented and how the guy or gal got the idea is 1000x more interesting than just getting an example for the application of it. It helps you remember stuff.

Then there are a dozen ways to explain every single concept and then some. You will find some much more intuitive than others and the sum of them will sharpen your understanding of them. Looking for different explanations for the same thing can be a great help. Did you know many things in maths where discovered multiple times? That happens a lot, because even brilliant mathematicians don’t properly understand each other, or even themselves.

Another thing you should do is to always develop your vocabulary for every domain/concept you encounter. People will throw around made-up words and symbols like no tomorrow. Often, there are simple concepts behind them, hence they are casually abstracted away. You need to understand the concept and then translate it into your own words and then draw a connection back to the made up stuff. Maths is a lot like programming. 1 + 1 is just a function, returning a result. So are integrals, formulas in vector algebra, and every single damn other thing in maths. Just follow the chain!

And finally, there are also some amazing insights hidden in maths. Gödel’s incompleteness theorems might send a chill down your spine once you grasp their implications. Computability and information theory will shape your view on the world and yourself.

I went from getting Ds to Bs to advanced theoretical CS courses and you can do it too. You don’t have to, but you can.

Don’t need a degree, but computer programming is fundamentally logic and algorithms. You need to have internalise reasoning logically. In some ways critical thinking is closer to programming than trig is.

Math, despite being a great skill to have, is not mandatory for a large volume of programming roles. It may hurt you in some interviews but interviews are a fucking crap shoot / shit metric either way. Computers do most of the math, so you don’t have to!

Source: I’m dyslexic, suffered from dyscalculia and migraines until I was allowed to use a calculator, and barely passed high school math. No degree. No bootcamp. 8 years as a dev.

I’ve also excelled in multiple roles where colleagues were math or CS PHD’s, and become the senior or go-to on more projects than not. The key part is to know your strengths. I’m never gonna accept a role developing accounting software, or anything that would require me to code complex math on a regular basis. You’d be surprised how far you can get with Google.

IMO mathematical/logical/abstract thinking is critical for programming well, but IMO that’s different from “math degree” math.

Software as a means to an end can be used in almost every domain, so proficiency within that applicable domain is often either useful or necessary. That is to say, “math degree” math is likely needed for 3d rendering (certain games), scientific computation (incl machine learning), etc, but maybe not, otherwise. It depends on what software you’re trying to build.

To be more specific, general programming is definitely and specifically different from trig and calc. However, because math is also broad, “mathy” concepts like type theory, relational algebra, set theory are considered important for programming, even if only informally or indirectly so.

You’ll encounter math eventually. It could be as simple as implementing linear interpolation for a custom type, or understanding why a type is not suited for a particular application (e.g. never use floating points to represent money). If you delve into low-level networking, you’ll need a good understanding of binary/decimal/hexadecimal conversions and operations. If you go into game development or graphics, you won’t survive without a deep understanding of vectors, matrices, and quaternions. Any kind of data science is just math translated to a machine-readable language.

In my opinion, knowledge of the basic concepts is more important than being good at actually performing mathematics with pen and paper. For example, if you need to apply a transformation to a vector, nobody expects you to whip up a program that does the thing. Instead, you should immediately know:

• what a transformation is (translation, rotation, scaling, projection, etc),
• that each transformation has a corresponding transformation matrix,
• that you’ll have to deal with inhomogeneous and homogeneous coordinates, and
• that you’ll have to combine the transformation matrices and the original vector.

That abstract knowledge will give you a starting point. Then you can look up the particulars – the corresponding transformation matrices, the method to convert between inhomogeneous and homogeneous coordinates, and the process of matrix multiplication. I know because I failed calculus.

Practical programming itself does not require this kind of math. The stuff you’re trying to make a program do might; but even then I don’t think you’ll have difficulty in that context. The stuff you’re learning now will have had time to “settle”, and you’ll be working towards a concrete goal, which makes it easier in my experience.

Another thing is that just because you’re struggling right now doesn’t mean you’ll be struggling forever. Math didn’t really click for me until I took calculus. I had a math professor who it didn’t click for until their junior year of college as a math major.

So don’t sweat it. But it’s always a good idea to have another career idea or two in your back pocket just in case. There are lots of reasons you might not want to be a programmer as a career. You might hate it. You might love it enough that you want to be able to do it freely instead of at the behest of others for money.

These kinds of anxieties are normal for someone your age (assuming you’re not nontraditional student). But one day you’ll look behind you in all these worries will seem unjustified. Everything will almost certainly turn out fine.

Depends on the career path. Some need only the very basics - for example in frontend development, you’ll mostly use % and basic +/-.

tbh. Most of the useful programming related knowledge you’ll learn at yoyr first job, not at uni.

The curriculum sometimes will force you to learn something unrelated to your career and it has multiple purposes:

People learn the fastest in the topic where they already know a lot. And the slowest where they know very little.

Learning stuff outaide of your comfort zone literally works out your brain. You learn to learn. And your thinking becomes more flexible.

You should not become somebody who is only good at one narrow singular task and a complete idiot at anything else.

You never know if it becomes useful later in life. So I suggest still trying to do your best at any topic. And studying more for the exams where you are not as proficient.

As to which career path to go for:

Don’t be afraid to change midway, but make sure that you enjoy it. If you enjoy compsci, keep at it. (Or if you have student loan, put some more thought into the cost of switching).

I tried to go to University for CS but never quite got the hang of the math part. Instead I got a Certification in Computer Science from an apprenticeship (idk if that’s the right Translation, in German we call it “Fachinformatiker für Anwendungsentwicklung”) within 1.5 years and with extreme ease, because it was way less math-heavy and more focused on actual programming.

I stayed with the company that I did the apprenticeship with and got promoted from Junior to Regular within a year. I work exactly in the field and position I wanted to work in when I was going for the CS degree. In fact, I have the exact same responsibilities and the same pay as my colleagues with CS degrees. It might not be like that in every company, but it did work out for me.

Just for fun, I actually went back to Uni this semester to try and actually finish one or two math modules, but dropped out within 2 weeks because I was hopelessly incapable of even understanding the basic concepts lol

Being comfortable with algebra is kinda essential, however you probably won’t make much use of calculus unless you go into certain parts of the industry such as game development.

Practice makes perfect though, you may suck at maths today, but there’s nothing stopping you from getting better at it if you work at it

you can program without math, but it will be hard to pass a rigorous interview without math.

You should strive to learn symbolic math at least, and make sure you can do all the leet code problems and explanations using whatever math you are comfortable with.

If you enjoy the work you’ll do well. There are a lot of different roles and specialties in CS with some of them being highly math focused and others quite divorced.

You often need to be pretty good at math. But not because you’re “doing math” to write the code.

In real world software systems, you need to handle monitoring and alerting. To properly do this, you need to understand stats, rolling averages, percentiles, probability distributions, and significance testing. At least at a basic level. Enough to know how to recognize these problems and where to look when you run into them.

For being a better coder, you need to understand mathematical logic, proofs, algebra/symbolic logic, etc in order to reason your way through tricky edge cases.

To do AI/ML, you need to know a shitton of calculus and diff eqs, plus numerical algorithms concepts like numerical stability. This is kinda a niche (but rapidly growing) engineering field.

The same thing about AI also applies to any other domain where the thing being computed is fundamentally a math or logic solution. This is somewhat common in backend engineering.

I’m not “doing math” with pen and paper at work, but I do use all of these mathematical skills all. the. time.

I am an SRE on a ML serving platform.

The field is incredibly broad. Choose a field or employer or project that’s not doing that an you’re fine.