Is your child interested in technology, programming computers, or creating video games? Computer science is a field that many people find fascinating. Those who study it have a strong understanding of how software and hardware relate to computing. When one becomes a full-fledged computer scientist, they can create tools and frameworks that help solve major problems.
With a computer science degree, your child can work in several exciting fields. These fields include software engineering, game development, database administration, IT consultancy, information security, cybersecurity, and artificial intelligence. On top of that, the salary that computer scientists earn isn’t too shabby.
However, there is one aspect of computer science that causes many to pump the brakes when they think about venturing into this career path: math. Computer science and math are closely linked, and the goal is to encourage children who are resistant to math to see the connections between mathematics and something they are truly interested in learning.
Below, we will look at why computer science and math are inseparable, which will hopefully spur aspiring computer scientists to not look at the subject with dread, but to look at it as a tool for the future, in which they can become the next programming, cybersecurity, or artificial intelligence guru.
[Read: Benefits of Mathematics]
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Many computer science disciplines and applications require math
Once an aspiring computer scientist accepts that computer science and math go together, they might ask themselves, “How much math is required of me?” The answer to this question depends on the computer science program they are studying.
However, there are some mathematical disciplines that are found in a majority of computer science programs.
Calculus is simply a branch of mathematics that studies the rate at which change occurs. Even though the average computer scientist might not use calculus every day, it does show up when doing computer security, graphics, machine learning, game programming, and computer vision.
[Read: Calculus in Daily Life]
Discrete mathematics studies mathematical structures whose nature is discrete (distinct and separable). This branch of mathematics covers topics like logic, set theory, algorithms, combinatorics, trees, and graphs. In computer science, all discrete math topics are relevant to computing.
Some have even argued that discrete mathematics is the foundation of computer science. As such, it is something that every computer scientist needs to learn.
This mathematical discipline is all about dealing with vectors, matrices, and transformations. Linear algebra comes in handy when working with things like graphics, cryptography, image processing, cybersecurity, information retrieval, and machine learning in computer science.
In number theory, students study natural numbers and integers. In computer science, number theory is an important aspect in several fields and applications. These include computer programming, public-key cryptography, graphics, random number generation, and cybersecurity.
Graph theory is a branch of mathematics dedicated to the study of graphs. A graph is a mathematical structure made of vertices and edges and is used to show a relationship between two or more points and lines. In computer science, uses of graph theory include networking, image capturing, data mining, and clustering.
Binary is also how numbers are stored in computer memory, and it is in this form that computers do operations on them. For example, the number 2 is represented as 10, and the number 35 is 100011. Knowing how a computer sees these numbers and operates in binary format — as opposed to their decimal and hexadecimal counterparts — simplifies computer design.
Statistics encompass the tools and techniques involved in the collection and analysis of large quantities of numerical data. So how does it connect computer science and math? Statistics is essential in computer science fields like data science (as far as machine learning and Big Data are concerned), data compression, random number generation, speech recognition, network modeling, and image analysis.
Working with algorithms requires knowledge of math
Simply put, an algorithm in computer science is a series of instructions or processes designed to help a computer solve a problem logically. Since computer scientists use computers for real-world problem solving, they need to know how to work with algorithms. This is how, for instance, programmers learn to write efficient programs. An efficient program uses less memory and takes less time to execute while solving complex problems.
During undergraduate studies, students will encounter algorithms when learning a subject called data structures and algorithms. But many are surprised to find out that this is not the first time they encounter them; they have been using algorithms since beginning algebra.
Even something as simple as x = 3 + 7 is an algorithm in mathematics. By looking at this, a student knows that to solve for x, they need to add the numbers on the right. With this algorithm, students learn to apply this type of thinking to other unfamiliar scenarios to solve problems. And math is what prepares them for this kind of thinking.
Math allows programmers to build applications that interact with the real world
When computer science and math work together to build applications, those applications accurately interact with the real world. Math, after all, is the language of nature. It is the language we have invented and refined since ancient Roman times to correctly describe the patterns we observe in nature and do something useful with those descriptions.
This means we can actually “teach” computer systems to interact with their environment. Mathematics, in this scenario, serves as the bridge of communication between the computer system and its surroundings. Without math, computers wouldn’t know how to interact with the real world, meaning that some inventions would never work.
Taking math on the road
A good example of computer science and math teaming up to produce technology that accurately navigates its environment is self-driving cars. The software that drives these cars can do several complex things to make sure it is safe, including:
- Keeping a safe distance from other vehicles on the road
- Accelerating at the right speed in the fast or slow lane
- Turning at the correct angle
- Avoiding hitting pedestrians
All these actions need to be mathematically precise. The data that is allowing the smart car to do this is being fed to the software through its hardware — mainly its camera and sensors. The data is then transferred to the vehicle’s system as binary information so that it can understand what it is “looking” at or sensing.
Another example is how Google Maps can guide you to your desired destination. It uses complex equations to calculate the shortest route and the exact time you will get there based on your speed, location data, historical traffic patterns, and other pieces of data. The software behind Google Maps constantly updates in real-time using machine learning.
[Read: Program for Prime Number in Java]
Math prepares computer scientists for analytical thinking
Computer scientists, like everyone else, make mistakes. So, they need to be able to objectively look at their work, identify their mistakes, and fix them. This ensures that the solutions they implement don’t cause further problems.
What happens in software engineering is a good example. Suppose a user is using a piece of software, and it crashes during a critical operation. They will send a bug report to the programmers, who will debug the software and implement a patch to resolve the issue. Next time the user tries to do the same operation, the software will not crash.
Fixing mistakes this way is something that students have been doing since they started learning math. When someone gets an answer wrong in math, they go back to the equation and analyze each step. While doing this, they try to recall their thought process as well. Then, when the person finds the wrong step, they correct it to find the right answer. It is in this way that math imparts analytical skills in students way before they ever start fixing bugs.
There is a strong link between computer science and math. Those who wish to excel in this field need to be well-versed in some important math concepts. These include calculus, discrete math, linear algebra, number theory, and/or graph theory, to name a few. These come in handy in several computer science disciplines.
Furthermore, math helps computer scientists work with algorithms and build programs that interact with reality. It also improves the analytical skills of computer scientists, taking their problem-solving to the next level. These are only but a few reasons computer science and math go hand in hand.
At BYJU’S FutureSchool, we celebrate learning in all its forms and would love to help your child expand their mathematical mindset. We teach classes on math, coding, and music (with an expanded curriculum on the way). Regardless of your child’s age or knowledge, there’s a course that’s perfect for them. Check out our website to browse our catalog and sign up for a FREE trial.