Discrete Mathematics is a subject that has gained prominence in recent times. Unlike regular Maths, where we deal with real numbers that vary continuously, Discrete Mathematics deals with logic that does not vary continuously or is discrete or separate distinguishable entities. Some functionalities in Discrete Mathematics include algorithms, set theory, graphs, statements in logic, recursion and probability, and so on.
Its importance lies in the fact that most key mathematical competitions such as American Mathematics Competition (AMC) or American Invitational Mathematics Examination (AIME) are based on Discrete Mathematics. On the other hand, not many would know that it plays a significant role in Computer Science and is often called the Backbone of Computer Science. Those who major in Computer Science in universities and colleges rely on it heavily.
Use in computing
Discrete Mathematics is, in fact, the math of computing. Computers work on algorithms of bytes 0 and 1. Knowledge of Discrete Mathematics is a prerequisite for algorithms, probabilities, logic, graph theory, set theory, and so on. Graph theory is used in compilers, operating systems, and networks while Set theory is used in software engineering and databases. On the other hand, probability theory is used in machine learning, networking, and artificial intelligence. In effect, Discrete Mathematics makes all aspects of Maths easy.
The goal of Computer Science is problem-solving with speedy solutions and Discrete Mathematics helps us solve real-world problems and daily projects and is comparatively easy to understand. Some of the basic use cases include identifying spam messages, choosing a valid password, steps needed to sort a list using a given method, and recognising the efficiency of one algorithm from the other are all now possible. All the advanced programming languages or emerging technologies leverage basic programming by calibrating the application of bytes.
As there is increased use of automation now, any task that is repeated without manual intervention through machines or robots inherently relies on the application of Discrete Mathematics. The concept is used in creating automation and is popularly called Artificial Intelligence or Machine Learning these days.
In daily life
Discrete Mathematics plays a significant role in big data analytics. Some examples that use concepts such as Graph theory are Google Maps. Encryption and decryption in cryptography that are used for masking data as part of cybersecurity are derived from number theory. Basic functionalities like password encryption and blockchain technology for mining and cryptocurrency also use number theory.
In Big Data, Discrete Mathematics also serves as a powerful means of managing very high-dimensional data or drawing inferences from large noisy data. It helps turn massive data into meaningful data and algorithms that can be used to scale up computations of humongous sizes.
Thus, Discrete Mathematics provides an exciting means for problem-solving in a highly technology-reliant society and also opens up exciting careers for better sophisticated analytical and technical tools and improving problem-solving abilities.
It is time that Discrete Mathematics is included in school curricula starting from Class 6 onwards with a focus on graph theory, combinatorics, number theory, discrete geometry, and discrete probability. These modules will also help students who are not particularly strong in arithmetic or algebra to understand the concepts more easily.
The writer is the CEO and Co-founder of byteXL.
