How is math used in computer science?

Are you curious how mathematics relates to work in the computer science field? The truth is that competence in math is fundamental for a career in the computer science industry.

Explore the applications and types of math used in computer science, and find out if you can pursue a career in computer science without a strong background in math.

Applications of math in computer science

In computer science, math is a key component. Math comprehension can help make sure that system optimization and coding tasks are carried out successfully.

To execute these tasks efficiently, you typically need a solid understanding in specific areas like discrete mathematics, algebra, and statistics. This is especially true for tasks related to:

• Data analysis and structures
• Artificial intelligence (AI) and machine learning
• Cryptography
• Algorithm design
• Networking
• Quantum computing

The importance of math in computer science is really driven home by ABET (formerly known as the Accreditation Board for Engineering and Technology), a major accreditation board in the field. They specifically require that students can solve computer science problems using math, science, and/or tech skills.

As of 2024, this is ABET's first student outcome needed for accreditation, which shows that math skills are the foundation of a computer science education.

Types of math used in computer science

Learn more about the types of mathematics used every day in computer science:

1. Statistics

Statistics is a branch of math that collects, analyzes, and interprets data. Statisticians use data analysis techniques to understand data and relationships between data points and data sets.

• Used in: Data analysis, AI, machine learning, algorithm design, data mining

2. Probability

Probability is a field of math used to measure uncertainty and analyze the likelihood of events. It helps us understand and predict uncertain situations, enabling informed decisions and risk assessment.

• Used in: Data analysis, data structures, AI, machine learning, algorithm design, cryptography, networking

3. Linear algebra

Linear algebra — the study of vectors, matrices, lines, and spaces — is essential in computer science. It provides the foundation for modeling digital objects and transforming data into visual information.

• Used in: Data analysis, AI, machine learning, cryptography, data science, quantum computing

4. Discrete mathematics

Discrete mathematics deals with distinct units, unlike continuous mathematics. It includes counting theory, probability theory, graph theory, and combinatorics, which are all essential for computer science.

• Used in: Data structures, algorithm design, cryptography, networking, software engineering

5. Calculus

Calculus studies rates of change and areas around curves. Differential calculus uses derivatives to measure a function's rate of change, while integral calculus uses integrals to measure areas under curves. Derivatives and integrals are opposites and together form the field of calculus.

• Used in: AI, machine learning, robotics, scientific computing