Here’s whether you should study computer science if you’re bad at maths:
You do not have to be an exceptional mathematician to succeed in computer science.
Some applications of computer science are deeply steeped in incredibly challenging mathematical topics.
Others hardly use any math at all.
To get a degree, you usually need to get through introductory calculus at most.
So if you want to learn all about what it takes to study computer science, then this article is for you.
Let’s get into it!
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Should You Study Computer Science If You’re Bad at Math?
Yes. Also no.
That’s not the most satisfying answer, but the truth is that computer science (CS) is a massive field. There are so many different topics of research and study.
Some of them involve the hardest math problems yet discovered by humanity.
Others have virtually no math at all.
If you steer into computer science topics that don’t use a lot of math, you’ll be fine.
Or, you can use computer science as an aid in learning more math and overcome your challenges.
There are options out there.
We have a lot to cover today, so let me summarize a few things.
First, I’ll cover computer science topics that aren’t brutal with the math.
Then, I’ll point out the opposite end of the spectrum.
I’ll also talk about fundamental computer science requirements.
For instance, if you want a CS degree, you will have to take some math classes.
What Kinds of Computer Science Don’t Need a Lot of Math? (4 Topics)
I said first that you should study computer science even if you’re bad at math.
More accurately, you can study it.
Whether or not you should study computer science has to do with your interest in CS itself.
To prove to you that you don’t need to be a special math genius to break into CS, I’m going to show you a handful of topics that really don’t use much math at all.
#1 Software Development
So many aspects of software are not mathematical.
A lot of software development is all about algorithms and structuring the software.
And, at the helm of software engineering, you have computer scientists who are organizing all of the different people writing the code.
Naturally, there is math-heavy software, and writing those algorithms does require an understanding of the math involved.
But, there are countless examples of software that aren’t deeply rooted in math, and you can work on any of it as a computer scientist who doesn’t excel in math.
Again, there are aspects of networking that have some math, especially when you start to drift into security.
Also, anything related to hardware design will probably have more math.
But, when you steer clear of those exceptions, most of networking is very logical and not entirely mathematical.
This is true whether you work in networking theory or in networking applications.
#3 Human-Computer Interaction (HCI)
For those unfamiliar, HCI is the study and development of interactions between humans and computers.
So, looking into how people type things or how information is displayed by computers would fall under this category.
Again, aspects of hardware design will lean on math.
But, when you’re trying to innovate new ways to talk to a computer or receive information from your phone, it doesn’t have to be mathematical.
A great example is speech recognition software.
Clearly, there are components of that research that hinge entirely on language processing rather than math.
This might seem weird, but many aspects of working with databases don’t require much math on your part.
The database infrastructure is already there, and it does the math for you.
In other words, you don’t have to reinvent SQL to make or use a database.
Because of that, a lot of applications of databases actually de-emphasize the math that you need to know as a computer scientist or developer.
But, if you are trying to reinvent SQL, then you’ll be deep in the math in no time.
What Kinds of Computer Science Use a Lot of Math? (5 Topics)
That concludes the math-light topics (although if you look hard enough, you could find more to add to that list).
Now, let’s get into computer science topics that use a lot of math.
This can help you really see the disparity of topics at play in such a vast field of study and work.
#1 Statistical Modeling
Statistical modeling is so ingrained into modern society.
Businesses make decisions based on data and analytics.
Health care is built around it.
You really can’t escape it.
Any computer scientist working in this area is going to be writing algorithms that do modeling math, and it can get very complicated very quickly.
You’re going to struggle in this field until you master the fundamentals at play.
#2 Scientific Software Development
While there are aspects of software development that don’t use a lot of math, there are also entire realms of software that exist solely to do math.
If you’re making modeling or calculation tools for scientific use, you’re probably going to be neck-deep in the math that is executed by your software.
That said, even the most math-heavy scientific software ever still has user interface design, and you don’t have to be a math expert to work specifically in those avenues.
#3 Theoretical Computer Science
If you want to research computer science itself, get ready for some deep math.
Theoretical computer science gets into the theory of computation.
That’s math-major type of stuff where you think about why 2+2=4.
No, really. Think about that for a moment.
Why is that equation true?
From a theory of computation point of view, it’s actually quite complicated, and you’ll likely borrow from number theory to arrive at a conclusion.
If this tidbit of theoretical computer science excites you, you might have found your career path.
If it gives you the bad kinds of chills, then you can try to steer around it as you study and practice computer science.
Cryptography is at the heart of security.
It’s all about making things that even a computer can’t understand without a key.
Cryptography involves mathematically complicated codes in order to secure information.
It’s completely math-dependent.
At the crux of cryptography are advanced concepts of probability and statistics.
Until you overcome math hesitancy, this field will be fairly inaccessible.
Optimization is a fun topic in computer science, and we’re going to spend a minute here.
This is really the center point of math in computer science.
I would say that you can get fairly deep into optimization without getting into very advanced math.
But even saying that optimization is built on the fundamentals of calculus, and that makes this a great place to talk about what constitutes hard math.
For the uninitiated, calculus is an intimidating word.
That’s supposed to be advanced math that only the brilliant people dare to try.
In reality, introductory calculus is very accessible.
A Calc I college course is going to cover the basics of optimization through a concept known as differentiation.
I know I’m throwing big words at you, but differentiation is actually the easy part of calculus.
That’s because you can calculate it by following a formula.
This is really the crucial point.
If you can follow an algorithm, which is essential for all work in computer science, then you should be able to handle differentiation.
That means you can handle the basics of optimization.
It also means that calculus might not be as scary as it seems (at least at the introductory level).
With all of that said, I still have this on the math-heavy list.
That’s because you won’t succeed in optimization if you can’t get through introductory calculus.
It’s just too fundamental to how optimization works.
On top of that, optimization problems can get very complicated.
So, if you’re trying to make a career out of optimization, you’ll need to be proficient with more than just the most basic aspects of calculus, and that does involve challenging math.
What Types of Math Come up in Computer Science? (5 Common Maths)
The math you’ll need as a computer scientist depends on what you do.
That’s a simple enough idea, but what types of math will you really be expected to know?
How much math study is involved in computer science?
This list covers the most common math topics, and it delves into some of the more extreme math that certain computer scientists run into.
I’m really talking about arithmetic here.
It’s the most basic form of math.
And, even though the computer does the computation for you, some fundamental understanding of how math works is very helpful across the board in computer science.
But, you don’t need to be a math whizz to achieve that understanding.
I’ve already touched on calculus.
I don’t need to reiterate what I’ve already covered, but something missing from the discussion so far is requisite math.
For most computer science degrees, you need to get through at least Calc I to graduate.
That’s the introductory calculus I was discussing earlier.
Beyond that, calculus concepts do come up a lot in many aspects of computer science.
In addition to optimization, graphing, visual structure, and calculus-based analytics are all part of computer science research and development.
It’s core, and you need to know enough calculus for your area of computer science (which ranges dramatically depending on your specific research or work).
But, if you can get through Calc I (which again, is quite accessible), then the door is open to the majority of computer science fields.
#3 Analytical Geometry
Analytical geometry is usually taught alongside calculus in college math classes.
The two really do go hand in hand, but in computer science applications, it’s worth mentioning them separately.
Analytical geometry is vital for any CS application that is going to use graphs.
It also comes up a lot in graphic design and development.
Let’s put it this way.
If your job is to tell a computer how to display visuals, analytical geometry is involved.
The level of analytical geometry you need to master varies, but getting through the introductory concepts completely changes how you even think about this stuff.
We’ve already touched on this.
Statistics are everywhere, and someone has to make all of the stuff that calculates and presents them.
That’s a big part of computer science.
But, there’s more going on here.
Statistics is interlinked with probability, and that’s the true core concept that most computer scientists need to understand.
Computers run on probability, and if you don’t understand the probability that is at the basic operating level of your computer systems, things are going to be harder for you as a computer scientist.
#5 Everything Else
The math concepts listed above are pretty general in computer science.
As I’ve mentioned, specialty work can sidestep even those math ideas, but they show up a lot.
If you aren’t avoiding math, then there’s a good chance your research will run into any number of other mathematical concepts.
It really depends on what you’re doing, but computer scientists tackle all branches of math.
Number theory, modeling, linear algebra, complex analysis, and so many other avenues of math are liable to show up when you really get deep into computer science.
I can’t even hope to list them all.
What Math Problems Are the Hardest to Overcome? (2 Math Concepts)
As you hopefully now agree, math is all over the place in computer science.
Sometimes it’s easy. Sometimes it’s literally impossible.
It can be anywhere in between.
As someone looking to get started in computer science, what math do you absolutely have to know?
Even though I mentioned calculus before, it’s not quite the most essential of all mathematical concepts.
Instead, the logic of math matters the most (I’ll elaborate).
And, the probability is pretty tricky to avoid if you want a long career in CS.
#1 Essentials of Logic
Computer science is not math, but it did evolve from math.
At its core, math is a strict application of logic, and so is computer science.
If you’re struggling with the essence of logic behind a math problem, computer science is going to be very difficult for you.
If your problems with math have to do with specific applications, keeping track of formulas, or deciphering word problems, there’s plenty of room for you in computer science.
When thinking about logic, there’s an easy test.
If you can understand computer science well enough to write logical code on your own, then you understand the essentials of logic (even in math) well enough to succeed.
I mentioned this before, but we should spend a little more time on it.
Not all fields of computer science rely heavily on probability, but it shows up in a lot of places where you might not expect it (like game design).
Random number generators and related randomization concepts are essential for so many aspects of computer science.
If you ever need the computer to pick anything at random at any point in an algorithm, you’re playing with probability.
If the very basics of probability give you a hard time, your options in computer science are severely limited, and you’ll struggle to complete your degree.