Computer science

Computer science or computing science (abbreviated CS) is the study of the theoretical foundations of information and computation and of practical techniques for their implementation and application in computer systems. Computer scientists invent algorithmic processes that create, describe, and transform information and formulate suitable abstractions to design and model complex systems.[1][2]

Computer science has many sub-fields; some, such as computational complexity theory, study the fundamental properties of computational problems, while others, such as computer graphics, emphasize the computation of specific results. Still others focus on the challenges in implementing computations. For example, programming language theory studies approaches to describe computations, while computer programming applies specific programming languages to solve specific computational problems, and human-computer interaction focuses on the challenges in making computers and computations useful, usable, and universally accessible to humans.

The general public sometimes confuses computer scientists with other computer professionals having careers in information technology, or think that computer science relates to their own experience with computers, which typically involves activities such as gaming, web-browsing, and word-processing. However, the focus of computer science is more on understanding the properties of the programs used to implement software such as games and web-browsers, and using that understanding to create new programs or improve existing ones.[3]

large capital lambda Plot of a quicksort algorithm
Utah teapot representing computer graphics Microsoft Tastenmaus mouse representing human-computer interaction
Computer science deals with the theoretical foundations of information, computation, and with practical techniques for their implementation and application.



The earliest foundations of what would become computer science predate the invention of the modern digital computer. Machines for calculating fixed numerical tasks, such as the abacus, have existed since antiquity. Wilhelm Schickard designed the first mechanical calculator in 1623, but did not complete its construction.[4] Blaise Pascal designed and constructed the first working mechanical calculator, the Pascaline, in 1642. Charles Babbage designed a difference engine and then a general-purpose Analytical Engine in Victorian times,[5] for which Ada Lovelace wrote a manual. Because of this work she is regarded today as the world's first programmer.[6] Around 1900, punched card machines were introduced.

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.[7] 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 1950s and early 1960s.[8][9] The world's first computer science degree program, the Cambridge Diploma in Computer Science, began at the University of Cambridge Computer Laboratory in 1953. The first computer science degree program in the United States was formed at Purdue University in 1962.[10] Since practical computers became available, many applications of computing have become distinct areas of study in their own right.

Although many initially believed it was impossible that computers themselves could actually be a scientific field of study, in the late fifties it gradually became accepted among the greater academic population.[11] It is the now well-known IBM brand that formed part of the computer science revolution during this time. IBM (short for International Business Machines) released the IBM 704 and later the IBM 709 computers, which were widely used during the exploration period of such devices. "Still, working with the IBM [computer] was frustrating...if you had misplaced as much as one letter in one instruction, the program would crash, and you would have to start the whole process over again".[11] During the late 1950s, the computer science discipline was very much in its developmental stages, and such issues were commonplace.

Time has seen significant improvements in the usability and effectiveness of computer science technology. Modern society has seen a significant shift from computers being used solely by experts or professionals to a more widespread user base. Initially, computers were quite costly, and for their most-effective use, some degree of human aid was needed, in part by professional computer operators. However, as computers became widespread and far more affordable, less human assistance was needed, although residues of the original assistance still remained.

Major achievements

The German military used the Enigma machine (shown here) during World War II for communication they thought to be secret. The large-scale decryption of Enigma traffic at Bletchley Park was an important factor that contributed to Allied victory in WWII.[12]

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


A number of computer scientists have argued for the distinction of three separate paradigms in computer science. Peter Wegner argued that those paradigms are science, technology, and mathematics.[17] Peter Denning's working group argued that they are theory, abstraction (modeling), and design.[18] It has also been argued that they are the "rationalist paradigm" (which treats computer science as branch of mathematics, which is prevalent in theoretical computer science, and mainly employs deductive reasoning), the "technocratic paradigm" (which might be found in engineering approaches, most prominently in software engineering), and the "scientific paradigm" (which approaches computer-related artifacts from the empirical perspective of natural sciences, and identifiable in some branches of artificial intelligence.[19]

Name of the field

Despite its name, a significant amount of computer science does not involve the study of computers themselves. Because of this, several alternative names have been proposed. Certain departments of major universities prefer the term computing science, to emphasize precisely that difference. Danish scientist Peter Naur suggested the term datalogy, to reflect the fact that the scientific discipline revolves around data and data treatment, while not necessarily involving computers. The first scientific institution to use the term was the Department of Datalogy at the University of Copenhagen, founded in 1969, with Peter Naur being the first professor in datalogy. The term is used mainly in the Scandinavian countries. Also, in the early days of computing, a number of terms for the practitioners of the field of computing were suggested in the Communications of the ACMturingineer, turologist, flow-charts-man, applied meta-mathematician, and applied epistemologist.[20] Three months later in the same journal, comptologist was suggested, followed next year by hypologist.[21] The term computics has also been suggested.[22] In continental Europe, terms derived from "information" and "automatic" are often used, e.g. informatique (French), Informatik (German), Informatica (Spain, Italy) or informatika (Slavic languages) are also used.[citation needed]

Renowned computer scientist Edsger Dijkstra once stated: "Computer science is no more about computers than astronomy is about telescopes."[23] 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. However, there has been much cross-fertilization of ideas between the various computer-related disciplines. Computer science research also often intersects other disciplines, such as philosophy, cognitive science, linguistics, mathematics, physics, statistics, and logic.

Computer science is considered by some to have a much closer relationship with mathematics than many scientific disciplines, with some observers saying that computing is a mathematical science.[8] 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. David Parnas, taking a cue from the relationship between other engineering and science disciplines, has claimed that the principal focus of computer science is studying the properties of computation in general, while the principal focus of software engineering is the design of specific computations to achieve practical goals, making the two separate but complementary disciplines.[24]

The academic, political, and funding aspects of computer science tend to depend on whether a department formed with a mathematical emphasis or with an engineering emphasis. Computer science departments with a mathematics emphasis and with a numerical orientation consider alignment with computational science. Both types of departments tend to make efforts to bridge the field educationally if not across all research.

Areas of computer science

As a discipline, computer science spans a range of topics from theoretical studies of algorithms and the limits of computation to the practical issues of implementing computing systems in hardware and software.[25][26] CSAB, formerly called Computing Sciences Accreditation Board – which is made up of representatives of the Association for Computing Machinery (ACM), and the IEEE Computer Society (IEEE-CS)[27] – identifies four areas that it considers crucial to the discipline of computer science: theory of computation, algorithms and data structures, programming methodology and languages, and computer elements and architecture. In addition to these four areas, CSAB also identifies fields such as software engineering, artificial intelligence, computer networking and communication, database systems, parallel computation, distributed computation, computer-human interaction, computer graphics, operating systems, and numerical and symbolic computation as being important areas of computer science.[25]

Theoretical computer science

The broader field of theoretical computer science encompasses both the classical theory of computation and a wide range of other topics that focus on the more abstract, logical, and mathematical aspects of computing.

Theory of computation

According to Peter J. Denning, the fundamental question underlying computer science is, "What can be (efficiently) automated?"[8] The study of the theory of computation is focused on answering fundamental questions about what can be computed and what amount of resources are required to perform those computations. In an effort to answer the first question, computability theory examines which computational problems are solvable on various theoretical models of computation. The second question is addressed by computational complexity theory, which studies the time and space costs associated with different approaches to solving a multitude of computational problems.

The famous "P=NP?" problem, one of the Millennium Prize Problems,[28] is an open problem in the theory of computation.

DFAexample.svg Wang tiles.png P = NP ? GNITIRW-TERCES Blochsphere.svg
Automata theory Computability theory Computational complexity theory Cryptography Quantum computing theory

Information and coding theory

Information theory is related to the quantification of information.This was developed by Claude E. Shannon to find fundamental limits on signal processing operations such as compressing data and on reliably storing and communicating data. Coding theory is the study of the properties of codes and their fitness for a specific application. Codes are used for data compression, cryptography, error-correction and more recently also for network coding. Codes are studied for the purpose of designing efficient and reliable data transmission methods.

Algorithms and data structures

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Analysis of algorithms Algorithms Data structures Computational geometry

Programming language theory

Programming language theory is a branch of computer science that deals with the design, implementation, analysis, characterization, and classification of programming languages and their individual features. It falls within the discipline of computer science, both depending on and affecting mathematics, software engineering and linguistics. It is a well-recognized branch of computer science, and an active research area, with results published in numerous journals dedicated to PLT, as well as in general computer science and engineering publications.

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Type theory Compiler design Programming languages

Formal methods

Formal methods are a particular kind of mathematically-based techniques for the specification, development and verification of software and hardware systems. The use of formal methods for software and hardware design is motivated by the expectation that, as in other engineering disciplines, performing appropriate mathematical analysis can contribute to the reliability and robustness of a design. However, the high cost of using formal methods means that they are usually only used in the development of high-integrity systems, where safety or security is of utmost importance. Formal methods are best described as the application of a fairly broad variety of theoretical computer science fundamentals, in particular logic calculi, formal languages, automata theory, and program semantics, but also type systems and algebraic data types to problems in software and hardware specification and verification.

Concurrent, parallel and distributed systems

Concurrency is a property of systems in which several computations are executing simultaneously, and potentially interacting with each other. A number of mathematical models have been developed for general concurrent computation including Petri nets, process calculi and the Parallel Random Access Machine model. A distributed system extends the idea of concurrency onto multiple computers connected through a network. Computers within the same distributed system have their own private memory, and information is often exchanged amongst themselves to achieve a common goal.

Databases and information retrieval

A database is intended to organize, store, and retrieve large amounts of data easily. Digital databases are managed using database management systems to store, create, maintain, and search data, through database models and query languages.

Applied computer science

Artificial intelligence

This branch of computer science aims to synthesise goal-orientated processes such as problem-solving, decision-making, environmental adaptation, learning and communication which are found in humans and animals. From its origins in cybernetics and in the Dartmouth Conference (1956), artificial intelligence (AI) research has been necessarily cross-disciplinary, drawing on areas of expertise such as applied mathematics, symbolic logic, semiotics, electrical engineering, philosophy of mind, neurophysiology, and social intelligence. AI is associated in the popular mind with robotic development, but the main field of practical application has been as an embedded component in areas of software development which require computational understanding and modeling such as finance and economics, data mining and the physical sciences. The starting-point in the late 1940s was Alan Turing's question "Can computers think?", and the question remains effectively unanswered although the "Turing Test" is still used to assess computer output on the scale of human intelligence. But the automation of evaluative and predictive tasks has been increasingly successful as a substitute for human monitoring and intervention in domains of computer application involving complex real-world data.

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Machine Learning Computer vision Image Processing Pattern Recognition
User-FastFission-brain.gif Data.png Sky.png Earth.png
Cognitive Science Data Mining Evolutionary Computation Information Retrieval
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Knowledge Representation Natural Language Processing Robotics

Computer architecture and engineering

Computer architecture, or digital computer organization, is the conceptual design and fundamental operational structure of a computer system. It focuses largely on the way by which the central processing unit performs internally and accesses addresses in memory. The field often involves disciplines of computer engineering and electrical engineering, selecting and interconnection hardware components to create computers that meet functional, performance, and cost goals.

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Digital logic Microarchitecture Multiprocessing
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Operating systems Computer networks Databases Computer security
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Ubiquitous computing Systems architecture Compiler design Programming languages

Computer graphics and visualization

Computer graphics is the study of digital visual contents, and involves syntheses and manipulations of image data. The study is connected to many other fields in computer science, including computer vision, image processing, and computational geometry, and are heavily applied in the fields of special effects and video games.

Computer security and cryptography

Computer security is a branch of computer technology, whose objective includes protection of information from unauthorized access, disruption, or modification while maintaining the accessibility and usability of the system for its intended users. Cryptography is the practice and study of hiding (encryption) and therefore deciphering (decryption) information. Modern cryptography is largely related to computer science, for many encryption and decryption algorithms are based on their computational complexity.

Computational science

Computational science (or scientific computing) is the field of study concerned with constructing mathematical models and quantitative analysis techniques and using computers to analyze and solve scientific problems. In practical use, it is typically the application of computer simulation and other forms of computation to problems in various scientific disciplines.

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Numerical analysis Computational physics Computational chemistry Bioinformatics

Information science

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Information Retrieval Knowledge Representation Natural Language Processing Human–computer interaction

Software engineering

Software engineering is the study of designing, implementing, and modifying software in order to ensure it is of high quality, affordable, maintainable, and fast to build. It is a systematic approach to software design, involving the application of engineering practices to software.

Software engineering deals with the organizing and analyzing software to get the best out of them. It doesn't just deal with the creation or manufacture of new software, but its internal maintenance and arrangement.





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 science, emphasize the practice of advanced 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 process aspects of computer programming are often referred to as software engineering.

While computer science professions increasingly drive the U.S. economy, computer science education is absent in most American K-12 curricula. A report entitled "Running on Empty: The Failure to Teach K-12 Computer Science in the Digital Age" was released in October 2010 by Association for Computing Machinery (ACM) and Computer Science Teachers Association (CSTA), and revealed that only 14 states have adopted significant education standards for high school computer science. The report also found that only nine states count high school computer science courses as a core academic subject in their graduation requirements. In tandem with "Running on Empty," a new, non-partisan advocacy coalition--Computing in the Core (CinC)--was founded to influence federal and state policy, such as the Computer Science Education Act, which calls for grants to states to develop plans for improving computer science education and supporting computer science teachers.

Within the United States a gender gap in computer science education has been observed as well. Research conducted by the WGBH Educational Foundation and the Association for Computing Machinery (ACM) revealed that more than twice as many high school boys considered computer science to be a “very good” or “good” college major than high school girls. [29] In addition, the high school Advanced Placement (AP) exam for computer science has displayed a disparity in gender. Compared to other AP subjects it has the lowest number of female participants, with a composition of about 15 percent women.[30] This gender gap in computer science is further witnessed at the college level, where 31 percent of undergraduate computer science degrees are earned by women and only 8 percent of computer science faculty consists of women. [31]

See also


  1. ^ Denning, P. J.; Comer, D. E.; Gries, D.; Mulder, M. C.; Tucker, A.; Turner, A. J.; Young, P. R. (Jan 1989). "Computing as a discipline". Communications of the ACM 32: 9–23. doi:10.1145/63238.63239.  volume = 64 edit "Computer science and engineering is the systematic study of algorithmic processes-their theory, analysis, design, efficiency, implementation, and application-that describe and transform information."
  2. ^ Wegner, P. (October 13–15, 1976). "Research paradigms in computer science". Proceedings of the 2nd international Conference on Software Engineering. San Francisco, California, United States: IEEE Computer Society Press, Los Alamitos, CA. "Computer science is the study of information structures" 
  3. ^ "Common myths and preconceptions about Cambridge Computer Science" Computer Science Department, University of Cambridge
  4. ^ Nigel Tout (2006). "Calculator Timeline". Vintage Calculator Web Museum. Retrieved 2006-09-18. 
  5. ^ "Science Museum - Introduction to Babbage". Archived from the original on 2006-09-08. Retrieved 2006-09-24. 
  6. ^ "A Selection and Adaptation From Ada's Notes found in "Ada, The Enchantress of Numbers," by Betty Alexandra Toole Ed.D. Strawberry Press, Mill Valley, CA". Retrieved 2006-05-04. 
  7. ^ The Association for Computing Machinery (ACM) was founded in 1947.
  8. ^ a b c Denning, P.J. (2000). "Computer Science: The Discipline" (PDF). Encyclopedia of Computer Science. Archived from the original on 2006-05-25. 
  9. ^ "Some EDSAC statistics". Retrieved 2011-11-19. 
  10. ^ Computer science pioneer Samuel D. Conte dies at 85 July 1, 2002
  11. ^ a b Levy, Steven (1984). Hackers: Heroes of the Computer Revolution. Doubleday. ISBN 0-385-19195-2. 
  12. ^ a b David Kahn, The Codebreakers, 1967, ISBN 0-684-83130-9.
  13. ^ a b
  14. ^ Constable, R.L. (March 2000) (PDF). Computer Science: Achievements and Challenges circa 2000. 
  15. ^ Abelson, H.; G.J. Sussman with J. Sussman (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." 
  16. ^ Black box traders are on the march The Telegraph, August 26, 2006
  17. ^ Wegner, P. (October 13–15, 1976). "Research paradigms in computer science". Proceedings of the 2nd international Conference on Software Engineering. San Francisco, California, United States: IEEE Computer Society Press, Los Alamitos, CA. 
  18. ^ Denning, P. J.; Comer, D. E.; Gries, D.; Mulder, M. C.; Tucker, A.; Turner, A. J.; Young, P. R. (Jan 1989). "Computing as a discipline". Communications of the ACM 32: 9–23. doi:10.1145/63238.63239.  volume = 64 edit
  19. ^ Eden, A. H. (2007). "Three Paradigms of Computer Science". Minds and Machines 17 (2): 135–167. doi:10.1007/s11023-007-9060-8.  edit
  20. ^ Communications of the ACM 1(4):p.6
  21. ^ Communications of the ACM 2(1):p.4
  22. ^ IEEE Computer 28(12):p.136
  23. ^ Research Methods for Science By Michael P. Marder page 14. Published by Cambridge University Press
  24. ^ Parnas, D. L. (1998). Annals of Software Engineering 6: 19–37. doi:10.1023/A:1018949113292.  edit, 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, [...]"
  25. ^ a b Computing Sciences Accreditation Board (28 May 1997). "Computer Science as a Profession". Archived from the original on 2008-06-17. Retrieved 2010-05-23. 
  26. ^ Committee on the Fundamentals of Computer Science: Challenges and Opportunities, National Research Council (2004). Computer Science: Reflections on the Field, Reflections from the Field. National Academies Press. ISBN 978-0-309-09301-9. 
  27. ^ "Csab, Inc". 2011-08-03. Retrieved 2011-11-19. 
  28. ^ Clay Mathematics Institute P=NP
  29. ^
  30. ^ Gilbert, Alorie. "Newsmaker: Computer science's gender gap". CNET News. 
  31. ^ Dovzan, Nicole. "Examining the Gender Gap in Technology". University of Michigan. 

Further reading

  • Tucker, Allen B. (2004). Computer Science Handbook (2nd ed.). Chapman and Hall/CRC. ISBN 158488360X. 
    • "Within more than 70 chapters, every one new or significantly revised, one can find any kind of information and references about computer science one can imagine. [...] all in all, there is absolute nothing about Computer Science that can not be found in the 2.5 kilogram-encyclopaedia with its 110 survey articles [...]." (Christoph Meinel, Zentralblatt MATH)
  • van Leeuwen, Jan (1994). Handbook of Theoretical Computer Science. The MIT Press. ISBN 0262720205. 
    • "[...] this set is the most unique and possibly the most useful to the [theoretical computer science] community, in support both of teaching and research [...]. The books can be used by anyone wanting simply to gain an understanding of one of these areas, or by someone desiring to be in research in a topic, or by instructors wishing to find timely information on a subject they are teaching outside their major areas of expertise." (Rocky Ross, SIGACT News)
  • Ralston, Anthony; Reilly, Edwin D.; Hemmendinger, David (2000). Encyclopedia of Computer Science (4th ed.). Grove's Dictionaries. ISBN 156159248X. 
    • "Since 1976, this has been the definitive reference work on computer, computing, and computer science. [...] Alphabetically arranged and classified into broad subject areas, the entries cover hardware, computer systems, information and data, software, the mathematics of computing, theory of computation, methodologies, applications, and computing milieu. The editors have done a commendable job of blending historical perspective and practical reference information. The encyclopedia remains essential for most public and academic library reference collections." (Joe Accardin, Northeastern Illinois Univ., Chicago)
Selected papers
  • Knuth, Donald E. (1996). Selected Papers on Computer Science. CSLI Publications, Cambridge University Press. 
    • "Covering a period from 1966 to 1993, its interest lies not only in the content of each of these papers — still timely today — but also in their being put together so that ideas expressed at different times complement each other nicely." (N. Bernard, Zentralblatt MATH)
Curriculum and classification

External links

Bibliography and academic search engines

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