Math for Computer Science

Computer Science. One of the most lucrative and sought-after career fields with a high development rate than most and a myriad of opportunities stretching around widely different disciplines and roles. Given the pervasiveness of computer technology in our everyday lives, we can already establish that it has a wide scope in terms of future employability but it also encompasses a need for a strong background in more than just coding.

Perhaps my high school teacher was right when she explained how math finds a way to sneak into the prerequisites of most career options. Such is also the case with computer science but some specializations need a higher math proficiency than others. Before we proceed to talk about how math plays an important role in computer science, let us find out what Computer Science really is.

Computer science, as the name suggests, is the science or study behind computers that revolves around computation and it’s various applications. The principles of Engineering, Mathematics and Logic are applied to both practical and theoretical disciplines of the subject.

Now that we have that figured out, let’s have a look into the different kinds of mathematics that are most widely required and expected for a computer science role.

1. Binary Math: Hackers are often shown furiously typing away at their laptops while their screens flash patterns of 0 and 1 but the binary math needed for most real-world solutions is hardly at the same level. Binary math often just includes basic operations in the base-2 numeral system and a rough idea of their implementation in modern-day computation proves to be sufficient.

2. Algebra: Algebra is the study of generalizations of arithmetic operations and assimilates advanced functions to learn more about practical algorithms for matrix computations which then synthesize solutions to real-world problems. Linear algebraic concepts serve as the primary mathematical tools for applications such as cryptography, Machine Learning, Image Processing, Quantum Computation, and so on.

3. Statistics: Statistics encompass the study of manipulation and representation of data which includes presenting, analyzing, and interpreting the aforementioned data. It proves to be essential to decipher and interpret algorithms and is a vital tool for applications ranging from speech recognition to data mining and from artificial intelligence to traffic modeling.

4. Calculus: Calculus is the study of continuous change. Partial derivatives are utilized in continuous optimization algorithms to traverse gradients. The various mathematical analysis’ constitute the proof-based foundations of thinking about various kinds of calculi and it is also pivotal in the study of intelligent algorithms.

5. Discrete Math: Discrete Math is the study of how mathematical structures that can assume only distinct, separated values combine with one another. Graph theory and set theory act as foundational tools for most disciplines of computer science. It is commonly used in applications ranging from Machine Learning to Cryptography.

That brings us to the end of mathematical tools essential for computer science, more on the topic some other day.

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- Vinita Rajan