It is necessary to master discrete mathematics to understand courses such as data structures, algorithms, digital design, AI/ML, data science, coding theory and cryptography. Discrete Math constitudes a major part of Computer Science theory.

Discrete Math’s concept of Network Flows has been used to conserve endangered species. Spatio-temporal optimisation concept has been used to implement geospatial apps, e.g. maps, 3D reconstruction, processing of satellite images. Linear algebra has been used to respond to pandemics and manage logistics. Linear algebra has been used to balance chemical equations. Discrete probability theory has been used in modelling uncertainty in ML/data analytics models. Hidden Markov models or probabilistic models are used in speech processing and multi-media data processing. Graph theory is widely used.

Discrete Math courses cover Logic, proof techniques, resolution, induction, set theory, combinatorics, permutations, combinations, sum rule, product rule, pigeon-hole principle, Ramsey numbers, cardinality theory, countable and uncountable sets, Cantor’s diagonalization, uncomputable functions and graph theory.

Even binary math is discrete arithmetic.

A B.Tech degree course in computer science has Discrete Math as one foundation subject.