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Computer science, or computing science, is the study of the theoretical foundations of information and computation and their implementation and application in computer systems.[1][2][3] Computer science has many sub-fields; some emphasize the computation of specific results (such as computer graphics), while others (such as computational complexity theory) relate to properties of computational problems. Still others focus on the challenges in implementing computations. For example, programming language theory studies approaches to describing computations, while computer programming applies specific programming languages to solve specific computational problems.


Main article: History of computer science

The history of computer science predates the invention of the modern digital computer. Prior to the 1920s, the term computer referred to a human clerk who performed calculations. Early researchers in what came to be called computer science, such as Kurt Gödel, Alonzo Church, and Alan Turing, were interested in the question of computability: what things can be computed by a human clerk who simply follows a list of instructions with paper and pencil, for as long as necessary, and without ingenuity or insight? Part of the motivation for this work was the desire to develop computing machines that could automate the often tedious and error-prone work of a human computer.

During the 1940s, as newer and more powerful computing machines were developed, the term computer came to refer to the machines rather than their human predecessors. As it became clear that computers could be used for more than just mathematical calculations, the field of computer science broadened to study computation in general. Computer science began to be established as a distinct academic discipline in the 1960s, with the creation of the first computer science departments and degree programs.[4]

Major achievements

Despite its relatively short history as a formal academic discipline, computer science has made a number of fundamental contributions to science and society. These include:

  • A formal definition of computation and computability, and proof that there are computationally unsolvable and intractable problems[5].
  • The concept of a programming language, a tool for the precise expression of methodological information at various levels of abstraction[6]
  • Revolutionary technologies such as general-purpose computers, the Internet, digital signatures, electronic commerce, and search engines;
  • The enabling of new types of scientific research, such as computational physics and computational chemistry[7].

Relationship with other fields

Main article: Diversity of computer science
Wikiquote has a collection of quotations related to:

Despite its name, computer science rarely involves the study of computers themselves. In fact, the renowned computer scientist Edsger Dijkstra is often quoted as saying, "Computer science is no more about computers than astronomy is about telescopes." The design and deployment of computers and computer systems is generally considered the province of disciplines other than computer science. For example, the study of computer hardware is usually considered part of computer engineering, while the study of commercial computer systems and their deployment is often called information technology or information systems. Computer science is sometimes criticized as being insufficiently scientific, a view espoused in the statement "Science is to computer science as hydrodynamics is to plumbing" credited to Stan Kelly-Bootle[8] and others. However, there has been much cross-fertilization of ideas between the various computer-related disciplines. Computer science research has also often crossed into other disciplines, such as artificial intelligence, cognitive science, physics (see quantum computing), and linguistics.

Computer science is considered by some to have a much closer relationship with mathematics than many scientific disciplines[9]. Early computer science was strongly influenced by the work of mathematicians such as Kurt Gödel and Alan Turing, and there continues to be a useful interchange of ideas between the two fields in areas such as mathematical logic, category theory, domain theory, and algebra.

The relationship between computer science and software engineering is a contentious issue, which is further muddied by disputes over what the term "software engineering" means, and how computer science is defined. Some people believe that software engineering is a subset of computer science. Others, taking a cue from the relationship between other engineering and science disciplines, believe that the principle focus of computer science is studying the properties of computation in general, while the principle focus of software engineering is the design of specific computations to achieve practical goals, making them different disciplines. This view is promulgated by (among others) David Parnas[10]. Still others maintain that software cannot be engineered at all.

Fields of computer science

Mathematical foundations

Algorithms for protecting private data, including encryption.
Graph theory
Foundations for data structures and searching algorithms.
Mathematical logic
Boolean logic and other ways of modeling logical queries.
Type Theory
Formal analysis of the types of data, and the use of these types to understand properties of programs -- especially program safety.

Theory of computation

Main article: Theory of computation
Automata theory
Different logical structures for solving problems.
Computability theory
What is calculable with the current models of computers. Proofs developed by Alan Turing and others provide insight into the possibilities of what may be computed and what may not.
Computational complexity theory
Fundamental bounds (especially time and storage space) on classes of computations.

Algorithms and data structures

Analysis of algorithms
Time and space complexity of algorithms.
Formal logical processes used for computation, and the efficiency of these processes.
Data structures
The organization of and rules for the manipulation of data.
Genetic algorithms
A genetic algorithm is a search technique to find approximate solutions to optimization and search problems.

Programming languages and compilers

Ways of translating computer programs, usually from higher level languages to lower level ones. Based heavily on mathematical logic.
Programming languages
Formal language paradigms for expressing algorithms, and the properties of these languages (EG: what problems they are suited to solve).


Data mining
Study of algorithms for searching and processing information in documents and databases; closely related to information retrieval.

Concurrent, parallel, and distributed systems

The theory and practice of simultaneous computation; data safety in any multitasking or multithreaded environment.
Distributed computing
Computing using multiple computing devices over a network to accomplish a common objective or task.
Algorithms and protocols for reliably communicating data across different shared or dedicated media, often including error correction.
Parallel computing
Computing using multiple concurrent threads of execution.

Computer architecture

Computer architecture
The design, organization, optimization and verification of a computer system, mostly about CPUs and Memory subsystem (and the bus connecting them).
Operating systems
Systems for managing computer programs and providing the basis of a useable system.

Software engineering

Computer programming
The act of writing algorithms in a programming language.
Formal methods
Mathematical approaches for describing and reasoning about software designs.
Software engineering
The principles and practice of designing, developing, and testing programs, as well as proper engineering practices.


Artificial intelligence

Artificial intelligence
The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Automated reasoning
Solving engines, such as used in Prolog, which produce steps to a result given a query on a fact and rule database.
Algorithms for controlling the behavior of robots.
Computer vision
Algorithms for identifying three dimensional objects from a two dimensional picture.
Machine Learning
Automated creation of a set of rules and axioms based on input.

Computer graphics

Computer graphics
Algorithms both for generating visual images synthetically, and for integrating or altering visual and spatial information sampled from the real world.
Image processing
Determining information from an image through computation.
Human computer interaction
The study and design of computer interfaces that people use.

Scientific computing

The use of computer science to maintain, analyse, store biological data and to assist in solving biological problems such as protein folding.

Computer science education

Some universities teach computer science as a theoretical study of computation and algorithmic reasoning. These programs often feature the theory of computation, analysis of algorithms, formal methods, concurrency theory, databases, computer graphics and systems analysis, among others. They typically also teach computer programming, but treat it as a vessel for the support of other fields of computer science rather than a central focus of high-level study.

Other colleges and universities, as well as secondary schools and vocational programs that teach [[computer literacy[] science, emphasize the practice of advanced computer programming rather than the theory of algorithms and computation in their computer science curricula. Such curricula tend to focus on those skills that are important to workers entering the software industry. The practical aspects of computer programming are often referred to as software engineering. However, there is a lot of disagreement over what the term "software engineering" actually means, and whether it is the same thing as programming.

See also

  • Computing
  • Informatics
  • List of basic computer science topics
  • List of computer science conferences
  • List of open problems in computer science
  • List of publications in computer science
  • List of prominent pioneers in computer science
  • List of software engineering topics


  1. "Computer science is the study of information" Department of Computer and Information Science, Guttenberg Information Technologies
  2. "Computer science is the study of computation." Computer Science Department, College of Saint Benedict, Saint John's University
  3. "Computer Science is the study of all aspects of computer systems, from the theoretical foundations to the very practical aspects of managing large software projects." Massey University
  4. Denning, P.J. (2000). Computer science:the discipline. Encyclopedia of Computer Science.
  5. Constable, R.L. (March 2000). "Computer Science : Achievements and Challenges circa 2000".
  6. Abelson, H. (1996). Structure and Interpretation of Computer Programs, 2nd Ed., MIT Press. ISBN 0-262-01153-0. "The computer revolution is a revolution in the way we think and in the way we express what we think. The essence of this change is the emergence of what might best be called procedural epistemology — the study of the structure of knowledge from an imperative point of view, as opposed to the more declarative point of view taken by classical mathematical subjects."
  7. Constable, R.L. (1997). "Nature of the Information Sciences".
  8. Computer Language, Oct 1990
  9. Denning, P.J. (2000). Computer science:the discipline. Encyclopedia of Computer Science.
  10. Parnas, David L. (1998). Software Engineering Programmes are not Computer Science Programmes. Annals of Software Engineering 6: 19–37., p. 19: "Rather than treat software engineering as a subfield of computer science, I treat it as an element of the set, {Civil Engineering, Mechanical Engineering, Chemical Engineering, Electrical Engineering,....}."

External links

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