Theoretical Computer Science: A 2024 Career Guide

Written by Coursera Staff • Updated on

Explore the components of theoretical computer science and its applications in the real world. Discover how to become a theoretical computer scientist and the various job roles you can pursue in this field.

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When you study theoretical computer science, you delve into a highly interesting combination of computer science and mathematics. It comprises two major sects: complexity theory and algorithms. The former focuses on classifying computational tasks and strives to bring forward the evidence and requirements of algorithms in certain cases. The latter is associated with action on complexity theory by designing and analysing the computational procedures. 

Theoretical computer science is an inseparable link between computer science and mathematics, and both subjects have profited equally from the connection. Here, you deal with memory, parallelism, randomness, and time. 

It is an active field, full of research and fruitful consequences with the potential for application. It requires in-depth knowledge of algorithms, their analysis, and mathematical proofs. Interest and dedication decide the rate of reaching the advanced levels. 

Components of theoretical computer science

The field contains numerous and varied components. Some concepts you might study in a theoretical computer science course include:

  • Algebra

  • Computational number theory

  • Computational biology

  • Distributed and quantum computation

  • Cryptography

  • Machine learning

  • Algorithms

  • Data structures

  • Coding theory

  • Combinatorics

  • Learning theory

  • Pseudorandomness

  • Computational complexity

  • Quantum information

Applications of theoretical computer science 

Theoretical computer science plays a crucial role in technological development, with its scope being in medicine, economics, physics, mathematics, and statistics. 

Application in the medical field

  • In the medical field, neuroscience utilises theoretical computer science to represent the brain and nervous system as networks of individual components performing computation. 

  • Systems biology uses theoretical computer science as the human body system is complex. With networks full of proteins, cells, metabolites, hormones, and enzymes impacting each other in a complex criss-cross fashion, they require a suitable platform to depict everything for easy understanding and workability. 

  • Computational models play a role in systems biology, simulation of bodily environments, interactive pathways, molecular docking, and structure prediction studies. 

Applications in other fields

  • Economics: It also finds application in economics, where it is natural to recognise that self-interested, interconnected beings carry out a distributed computation. 

  • Physics: Theoretical computer science is applicable in quantum mechanics, gravity, and other fields of physics. 

  • Software Systems: Many software systems, programming languages, and virtual architectures have their development foundations in theoretical computer science, including C, APE, Prolog, CCS, and ProofPower. 

Practical applications of theoretical computer science

  • Consistent hashing

  • Polar and rateless expander codes used in 5G

  • Dynamic and quick algorithms applied by various app providers 

  • Principles of Google's PageRank algorithm 

  • Internet security through innovations in encryption

  • Contribution to cryptocurrencies and blockchain technology

Jobs roles in theoretical computer science

Theoretical computer science is a practical aspect that provides numerous job options and directly utilises research and knowledge. Examples include:

Research positions

Available in numerous roles, candidates can work as research fellows, student assistants, PhD scholars, and post-docs, where different funding agencies and fellowships provide funding during the research. The skills to become a theoretical computer scientist or researcher are curiosity and the ability to dive deep to answer questions opening with how, why, and what. It requires the ability to experiment, write articles, publish papers and a love of learning. 


You may be eligible for different professor positions depending on your qualifications, skills, and knowledge. The position suits individuals passionate about teaching and research; a PhD is generally required. 

Corporate world

Candidates who want to go for industrial job roles in theoretical computer science can qualify for positions such as data scientist, cryptanalytic computer scientist, theoretical quantum information scientist, and similar. Some common requirements are knowledge of programming languages, research skills, and hands-on expertise in algorithms, statistical, mathematical, probabilistic models, evaluation techniques, and high-performance computing. 

How do you become a theoretical computer scientist?

You typically need a bachelor's degree in computer science or a related field to become a theoretical computer scientist. A computer science and maths background from school provides a good basis for learning. It is common to need a master's doctoral degree to pursue advanced roles in this field.

The theoretical computer science aspirants must be familiar with discrete mathematics and logic, as further studies involve advanced concepts. The topics of concern are algebraic properties and structures, combinatorics, proof techniques, linear algebra, probability and statistics, and calculus. 

You will also find several basic concepts in the programme syllabus. Numerous government and private Indian colleges provide computer science courses during graduation that involve theoretical computer science as the subject. However, specialisation or post-graduation in theoretical computer science is available at different IITs. A general MTech in Computer Science also involves theoretical aspects. 

Next steps

Theoretical computer science is an engaging and interesting field for people passionate about numbers and computers. As a starting point, Coursera lists multiple courses and certifications, such as the Google Data Analytics Professional Certificate and the IBM Data Science Professional Certificate.

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