Computer science

Page semi-protected
Source: Wikipedia, the free encyclopedia.

Computer science is the study of computation, information, and automation.[1][2][3] Computer science spans theoretical disciplines (such as algorithms, theory of computation, and information theory) to applied disciplines (including the design and implementation of hardware and software).[4][5][6] Though more often considered an academic discipline, computer science is closely related to computer programming.[7]

Algorithms and data structures are central to computer science.[8] The

models of computation and general classes of problems that can be solved using them. The fields of cryptography and computer security involve studying the means for secure communication and for preventing security vulnerabilities. Computer graphics and computational geometry address the generation of images. Programming language theory considers different ways to describe computational processes, and database theory concerns the management of repositories of data. Human–computer interaction investigates the interfaces through which humans and computers interact, and software engineering focuses on the design and principles behind developing software. Areas such as operating systems, networks and embedded systems investigate the principles and design behind complex systems. Computer architecture describes the construction of computer components and computer-operated equipment. Artificial intelligence and machine learning aim to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, planning and learning found in humans and animals. Within artificial intelligence, computer vision aims to understand and process image and video data, while natural language processing
aims to understand and process textual and linguistic data.

The fundamental concern of computer science is determining what can and cannot be automated.[2][9][3][10][11] The Turing Award is generally recognized as the highest distinction in computer science.[12][13]


Gottfried Wilhelm Leibniz (1646–1716) developed logic in a binary number system and has been called the "founder of computer science".[14]
Charles Babbage is sometimes referred to as the "father of computing".[15]
Ada Lovelace published the first algorithm intended for processing on a computer.[16]

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, aiding in computations such as multiplication and division. Algorithms for performing computations have existed since antiquity, even before the development of sophisticated computing equipment.[17]

Electromechanical Arithmometer, a prototype that demonstrated the feasibility of an electromechanical analytical engine,[27] on which commands could be typed and the results printed automatically.[28] In 1937, one hundred years after Babbage's impossible dream, Howard Aiken convinced IBM, which was making all kinds of punched card equipment and was also in the calculator business[29] to develop his giant programmable calculator, the ASCC/Harvard Mark I, based on Babbage's Analytical Engine, which itself used cards and a central computing unit. When the machine was finished, some hailed it as "Babbage's dream come true".[30]

During the 1940s, with the development of new and more powerful

History of informatics


Although first proposed in 1956,[36] the term "computer science" appears in a 1959 article in Communications of the ACM,[37] in which Louis Fein argues for the creation of a Graduate School in Computer Sciences analogous to the creation of Harvard Business School in 1921.[38] Louis justifies the name by arguing that, like management science, the subject is applied and interdisciplinary in nature, while having the characteristics typical of an academic discipline.[37] His efforts, and those of others such as

field of data analysis, including statistics and databases.

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.[42] Three months later in the same journal, comptologist was suggested, followed next year by hypologist.[43] The term computics has also been suggested.[44] In Europe, terms derived from contracted translations of the expression "automatic information" (e.g. "informazione automatica" in Italian) or "information and mathematics" are often used, e.g. informatique (French), Informatik (German), informatica (Italian, Dutch), informática (Spanish, Portuguese), informatika (Slavic languages and Hungarian) or pliroforiki (πληροφορική, which means informatics) in Greek. Similar words have also been adopted in the UK (as in the School of Informatics, University of Edinburgh).[45] "In the U.S., however, informatics is linked with applied computing, or computing in the context of another domain."[46]

A folkloric quotation, often attributed to—but almost certainly not first formulated by—

computer systems and their deployment is often called information technology or information systems. However, there has been exchange of ideas between the various computer-related disciplines. Computer science research also often intersects other disciplines, such as cognitive science, linguistics, mathematics, physics, biology, Earth science, statistics, philosophy, 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.[7] Early computer science was strongly influenced by the work of mathematicians such as Kurt Gödel, Alan Turing, John von Neumann, Rózsa Péter and Alonzo Church 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.[36]

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.[47] 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.[48]

The academic, political, and funding aspects of computer science tend to depend on whether a department is 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.


Epistemology of computer science

Despite the word "science" in its name, there is debate over whether or not computer science is a discipline of science,[49] mathematics,[50] or engineering.[51] Allen Newell and Herbert A. Simon argued in 1975,

Computer science is an empirical discipline. We would have called it an experimental science, but like astronomy, economics, and geology, some of its unique forms of observation and experience do not fit a narrow stereotype of the experimental method. Nonetheless, they are experiments. Each new machine that is built is an experiment. Actually constructing the machine poses a question to nature; and we listen for the answer by observing the machine in operation and analyzing it by all analytical and measurement means available.[51]

It has since been argued that computer science can be classified as an empirical science since it makes use of empirical testing to evaluate the correctness of programs, but a problem remains in defining the laws and theorems of computer science (if any exist) and defining the nature of experiments in computer science.[51] Proponents of classifying computer science as an engineering discipline argue that the reliability of computational systems is investigated in the same way as bridges in civil engineering and airplanes in aerospace engineering.[51] They also argue that while empirical sciences observe what presently exists, computer science observes what is possible to exist and while scientists discover laws from observation, no proper laws have been found in computer science and it is instead concerned with creating phenomena.[51]

Proponents of classifying computer science as a mathematical discipline argue that computer programs are physical realizations of mathematical entities and programs can be deductively reasoned through mathematical formal methods.[51] Computer scientists Edsger W. Dijkstra and Tony Hoare regard instructions for computer programs as mathematical sentences and interpret formal semantics for programming languages as mathematical axiomatic systems.[51]

Paradigms of computer science

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.[52] Peter Denning's working group argued that they are theory, abstraction (modeling), and design.[7] Amnon H. Eden described them as the "rationalist paradigm" (which treats computer science as a 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,[53] identifiable in some branches of artificial intelligence).[54] Computer science focuses on methods involved in design, specification, programming, verification, implementation and testing of human-made computing systems.[55]


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.[56][57]

symbolic computation as being important areas of computer science.[56]

Computer science is no more about computers than astronomy is about telescopes.

Edsger Dijkstra

Theoretical computer science

Theoretical Computer Science is mathematical and abstract in spirit, but it derives its motivation from the practical and everyday computation. Its aim is to understand the nature of computation and, as a consequence of this understanding, provide more efficient methodologies.

Theory of computation

According to

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,[59] is an open problem in the theory of computation.

Automata theory Formal languages Computability theory Computational complexity theory
Models of computation
Quantum computing theory
Logic circuit theory
Cellular automata

Information and coding theory

Information theory, closely related to probability and statistics, is related to the quantification of information. This was developed by Claude Shannon to find fundamental limits on signal processing operations such as compressing data and on reliably storing and communicating data.[60] Coding theory is the study of the properties of

data transmission
methods. [61]

Coding theory Channel capacity Algorithmic information theory
Signal detection theory
Kolmogorov complexity

Data structures and algorithms

Data structures and algorithms are the studies of commonly used computational methods and their computational efficiency.

Analysis of algorithms Algorithm design
Data structures
Combinatorial optimization Computational geometry
Randomized algorithms

Programming language theory and formal methods

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 an active research area, with numerous dedicated academic journals.

Formal methods are a particular kind of

algebraic data types
to problems in software and hardware specification and verification.

Formal semantics Type theory
Compiler design
Programming languages Formal verification Automated theorem proving

Applied computer science

Computer graphics and visualization

Computer graphics is the study of digital visual contents and involves the synthesis and manipulation of image data. The study is connected to many other fields in computer science, including

image processing, and computational geometry, and is heavily applied in the fields of special effects and video games

2D computer graphics Computer animation Rendering Mixed reality Virtual reality Solid modeling

Image and sound processing

information engineering and has applications in medical image computing and speech synthesis, among others. What is the lower bound on the complexity of fast Fourier transform algorithms? is one of unsolved problems in theoretical computer science

FFT algorithms
Image processing
Speech recognition Data compression Medical image computing Speech synthesis

Computational science, finance and engineering

Scientific computing (or computational science) is the field of study concerned with constructing mathematical models and quantitative analysis techniques and using computers to analyze and solve scientific problems. A major usage of scientific computing is simulation of various processes, including computational fluid dynamics, physical, electrical, and electronic systems and circuits, as well as societies and social situations (notably war games) along with their habitats, among many others. Modern computers enable optimization of such designs as complete aircraft. Notable in electrical and electronic circuit design are SPICE,[63] as well as software for physical realization of new (or modified) designs. The latter includes essential design software for integrated circuits.[64]

Numerical analysis Computational physics Computational chemistry Bioinformatics Neuroinformatics Psychoinformatics
Medical informatics
Computational engineering Computational musicology

Social computing and human–computer interaction

Social computing is an area that is concerned with the intersection of social behavior and computational systems. Human–computer interaction research develops theories, principles, and guidelines for user interface designers.

Software engineering

Software engineering is the study of designing, implementing, and modifying the 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 of software—it does not just deal with the creation or manufacture of new software, but its internal arrangement and maintenance. For example software testing, systems engineering, technical debt and software development processes.

Artificial intelligence

Artificial intelligence (AI) aims to or is required to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, learning, and communication found in humans and animals. From its origins in cybernetics and in the Dartmouth Conference (1956), artificial intelligence 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. 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.

Computational learning theory Computer vision Neural networks Planning and scheduling
Natural language processing Computational game theory Evolutionary computation Autonomic computing
Representation and reasoning Pattern recognition Robotics Swarm intelligence

Computer systems

Computer architecture and organization

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.[65] Computer engineers study computational logic and design of computer hardware, from individual processor components, microcontrollers, personal computers to supercomputers and embedded systems. The term "architecture" in computer literature can be traced to the work of Lyle R. Johnson and Frederick P. Brooks, Jr., members of the Machine Organization department in IBM's main research center in 1959.

Processing unit Microarchitecture Multiprocessing Processor design
Ubiquitous computing Systems architecture Operating systems Input/output
Embedded system Real-time computing Dependability Interpreter

Concurrent, parallel and distributed computing

Concurrency is a property of systems in which several computations are executing simultaneously, and potentially interacting with each other.

Parallel Random Access Machine model.[67] When multiple computers are connected in a network while using concurrency, this is known as a distributed system. Computers within that distributed system have their own private memory, and information can be exchanged to achieve common goals.[68]

Computer networks

This branch of computer science aims to manage networks between computers worldwide.

Computer security and cryptography

Computer security is a branch of computer technology with the objective of protecting information from unauthorized access, disruption, or modification while maintaining the accessibility and usability of the system for its intended users.

Historical cryptography is the art of writing and deciphering secret messages. Modern cryptography is the scientific study of problems relating to distributed computations that can be attacked.[69] Technologies studied in modern cryptography include symmetric and asymmetric encryption, digital signatures, cryptographic hash functions, key-agreement protocols, blockchain, zero-knowledge proofs, and garbled circuits.

Databases and data mining

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. Data mining is a process of discovering patterns in large data sets.


The philosopher of computing Bill Rapaport noted three Great Insights of Computer Science:[70]

All the information about any computable problem can be represented using only 0 and 1 (or any other bistable pair that can flip-flop between two easily distinguishable states, such as "on/off", "magnetized/de-magnetized", "high-voltage/low-voltage", etc.).
  • Alan Turing's insight: there are only five actions that a computer has to perform in order to do "anything".
Every algorithm can be expressed in a language for a computer consisting of only five basic instructions:[71]
  • move left one location;
  • move right one location;
  • read symbol at current location;
  • print 0 at current location;
  • print 1 at current location.
  • Corrado Böhm and Giuseppe Jacopini's insight: there are only three ways of combining these actions (into more complex ones) that are needed in order for a computer to do "anything".[72]
Only three rules are needed to combine any set of basic instructions into more complex ones:
  • sequence: first do this, then do that;
  • selection: IF such-and-such is the case, THEN do this, ELSE do that;
  • repetition: WHILE such-and-such is the case, DO this.
The three rules of Boehm's and Jacopini's insight can be further simplified with the use of goto (which means it is more elementary than structured programming).

Programming paradigms

Programming languages can be used to accomplish different tasks in different ways. Common programming paradigms include:

  • Functional programming, a style of building the structure and elements of computer programs that treats computation as the evaluation of mathematical functions and avoids state and mutable data. It is a declarative programming paradigm, which means programming is done with expressions or declarations instead of statements.[73]
  • Imperative programming, a programming paradigm that uses statements that change a program's state.[74] In much the same way that the imperative mood in natural languages expresses commands, an imperative program consists of commands for the computer to perform. Imperative programming focuses on describing how a program operates.
  • Object-oriented programming, a programming paradigm based on the concept of "objects", which may contain data, in the form of fields, often known as attributes; and code, in the form of procedures, often known as methods. A feature of objects is that an object's procedures can access and often modify the data fields of the object with which they are associated. Thus object-oriented computer programs are made out of objects that interact with one another.[75]
  • Service-oriented programming, a programming paradigm that uses "services" as the unit of computer work, to design and implement integrated business applications and mission critical software programs

Many languages offer support for multiple paradigms, making the distinction more a matter of style than of technical capabilities.[76]


Conferences are important events for computer science research. During these conferences, researchers from the public and private sectors present their recent work and meet. Unlike in most other academic fields, in computer science, the prestige of

conference papers is greater than that of journal publications.[77][78] One proposed explanation for this is the quick development of this relatively new field requires rapid review and distribution of results, a task better handled by conferences than by journals.[79]


Computer Science, known by its near synonyms, Computing, Computer Studies, has been taught in UK schools since the days of

National Curriculum, for Key Stage 3 & 4. In September 2014 it became an entitlement for all pupils over the age of 4.[81]

In the US, with 14,000 school districts deciding the curriculum, provision was fractured.[82] According to a 2010 report by the Association for Computing Machinery (ACM) and Computer Science Teachers Association (CSTA), only 14 out of 50 states have adopted significant education standards for high school computer science.[83] According to a 2021 report, only 51% of high schools in the US offer computer science.[84]

Israel, New Zealand, and South Korea have included computer science in their national secondary education curricula,[85][86] and several others are following.[87]

See also


  1. ^ In 1851
  2. ^ "The introduction of punched cards into the new engine was important not only as a more convenient form of control than the drums, or because programs could now be of unlimited extent, and could be stored and repeated without the danger of introducing errors in setting the machine by hand; it was important also because it served to crystallize Babbage's feeling that he had invented something really new, something much more than a sophisticated calculating machine." Bruce Collier, 1970
  3. ^ See the entry "Computer science" on Wikiquote for the history of this quotation.
  4. ^ The word "anything" is written in quotation marks because there are things that computers cannot do. One example is: to answer the question if an arbitrary given computer program will eventually finish or run forever (the Halting problem).


  1. ^ "What is Computer Science? – Computer Science. The University of York". Archived from the original on June 11, 2020. Retrieved June 11, 2020.
  2. ^
    ISBN 978-0262010603. Archived from the original on January 9, 2021. {{cite book}}: |website= ignored (help
  3. ^ from the original on March 3, 2022. Retrieved March 3, 2022. The discipline of computing is the systematic study of algorithmic processes that describe and transform information, their theory, analysis, design, efficiency, implementation, and application. The fundamental question underlying all of computing is, 'What can be (efficiently) automated?'
  4. ^ "WordNet Search—3.1". WordNet Search. Archived from the original on October 18, 2017. Retrieved May 14, 2012.
  5. ^ "Definition of computer science |". Archived from the original on June 11, 2020. Retrieved June 11, 2020.
  6. ^ "What is Computer Science? | Undergraduate Computer Science at UMD". Archived from the original on November 27, 2020. Retrieved July 15, 2022.
  7. ^ from the original on March 3, 2022. Retrieved March 3, 2022.
  8. from the original on June 17, 2020. Retrieved June 17, 2020.
  9. from the original on January 11, 2023, retrieved March 3, 2022
  10. ^ Forsythe, George (August 5–10, 1969). "Computer Science and Education". Proceedings of IFIP Congress 1968. The question 'What can be automated?' is one of the most inspiring philosophical and practical questions of contemporary civilization.
  11. S2CID 12512057
  12. from the original on March 3, 2022. Retrieved March 3, 2022.
  13. .
  14. ^ "2021: 375th birthday of Leibniz, father of computer science". Archived from the original on September 21, 2022. Retrieved February 4, 2023.
  15. ^ "Charles Babbage Institute: Who Was Charles Babbage?". Archived from the original on January 9, 2007. Retrieved December 28, 2016.
  16. ^ "Ada Lovelace | Babbage Engine | Computer History Museum". Archived from the original on December 25, 2018. Retrieved December 28, 2016.
  17. ^ "History of Computer Science". Archived from the original on July 29, 2017. Retrieved July 15, 2022.
  18. ^ "Wilhelm Schickard – Ein Computerpionier" (PDF) (in German). Archived from the original (PDF) on September 19, 2020. Retrieved December 4, 2016.
  19. ^ Keates, Fiona (June 25, 2012). "A Brief History of Computing". The Repository. The Royal Society. Archived from the original on June 29, 2012. Retrieved January 19, 2014.
  20. ^ "Science Museum, Babbage's Analytical Engine, 1834–1871 (Trial model)". Archived from the original on August 30, 2019. Retrieved May 11, 2020.
  21. ^ .
  22. ^ "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". Archived from the original on February 10, 2006. Retrieved May 4, 2006.
  23. ^ "The John Gabriel Byrne Computer Science Collection" (PDF). Archived from the original on April 16, 2019. Retrieved August 8, 2019.
  24. ^ Torres Quevedo, L. (1914). "Ensayos sobre Automática – Su definicion. Extension teórica de sus aplicaciones". Revista de la Academia de Ciencias Exacta, 12, pp. 391–418.
  25. ^ Torres Quevedo, Leonardo. Automática: Complemento de la Teoría de las Máquinas, (pdf), pp. 575-583, Revista de Obras Públicas, 19 November 1914.
  26. ^ Randell, Brian. Digital Computers, History of Origins, (pdf), p. 545, Digital Computers: Origins, Encyclopedia of Computer Science, January 2003.
  27. ^ Randell 1982, p. 6, 11–13.
  28. ^ "In this sense Aiken needed IBM, whose technology included the use of punched cards, the accumulation of numerical data, and the transfer of numerical data from one register to another", Bernard Cohen, p.44 (2000)
  29. ^ Brian Randell, p. 187, 1975
  30. ^ The Association for Computing Machinery (ACM) was founded in 1947.
  31. ^ "IBM Archives: 1945". January 23, 2003. Archived from the original on January 5, 2019. Retrieved March 19, 2019.
  32. ^ "IBM100 – The Origins of Computer Science". September 15, 1995. Archived from the original on January 5, 2019. Retrieved March 19, 2019.
  33. ^ "Some EDSAC statistics". University of Cambridge. Archived from the original on September 3, 2007. Retrieved November 19, 2011.
  34. ^ "Computer science pioneer Samuel D. Conte dies at 85". Purdue Computer Science. July 1, 2002. Archived from the original on October 6, 2014. Retrieved December 12, 2014.
  35. ^ a b Tedre, Matti (2014). The Science of Computing: Shaping a Discipline. Taylor and Francis / CRC Press.
  36. ^ a b Louis Fine (1960). "The Role of the University in Computers, Data Processing, and Related Fields". Communications of the ACM. 2 (9): 7–14.
    S2CID 6740821
  37. ^ "Stanford University Oral History". Stanford University. Archived from the original on April 4, 2017. Retrieved May 30, 2013.
  38. ^ Donald Knuth (1972). "George Forsythe and the Development of Computer Science". Comms. ACM. Archived October 20, 2013, at the Wayback Machine
  39. ^ Matti Tedre (2006). "The Development of Computer Science: A Sociocultural Perspective" (PDF). p. 260. Archived (PDF) from the original on October 9, 2022. Retrieved December 12, 2014.
  40. ^ Peter Naur (1966). "The science of datalogy". Communications of the ACM. 9 (7): 485.
    S2CID 47558402
  41. .
  42. ^ Communications of the ACM 2(1):p.4
  43. ^ IEEE Computer 28(12): p.136
  44. ^ P. Mounier-Kuhn, L'Informatique en France, de la seconde guerre mondiale au Plan Calcul. L'émergence d'une science, Paris, PUPS, 2010, ch. 3 & 4.
  45. ^ Groth, Dennis P. (February 2010). "Why an Informatics Degree?". Communications of the ACM. Archived from the original on January 11, 2023. Retrieved June 14, 2016.
  46. S2CID 14263916
  47. ., 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, [...]"
  48. .
  49. .
  50. ^ a b c d e f g "The Philosophy of Computer Science". The Philosophy of Computer Science (Stanford Encyclopedia of Philosophy). Metaphysics Research Lab, Stanford University. 2021. Archived from the original on September 16, 2021. Retrieved September 16, 2021.
  51. ^ 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.
  52. S2CID 20045303
  53. S2CID 3023076. Archived from the original
    (PDF) on February 15, 2016.
  54. ^ Turner, Raymond; Angius, Nicola (2019). "The Philosophy of Computer Science". In Zalta, Edward N. (ed.). The Stanford Encyclopedia of Philosophy. Archived from the original on October 14, 2019. Retrieved October 14, 2019.
  55. ^ a b "Computer Science as a Profession". Computing Sciences Accreditation Board. May 28, 1997. Archived from the original on June 17, 2008. Retrieved May 23, 2010.
  56. from the original on February 18, 2011. Retrieved August 31, 2008.
  57. ^ "CSAB Leading Computer Education". CSAB. August 3, 2011. Archived from the original on January 20, 2019. Retrieved November 19, 2011.
  58. ^ Clay Mathematics Institute P = NP Archived October 14, 2013, at the Wayback Machine
  59. ^ P. Collins, Graham (October 14, 2002). "Claude E. Shannon: Founder of Information Theory". Scientific American. Archived from the original on January 16, 2014. Retrieved December 12, 2014.
  60. .
  61. .
  62. .
  63. ^ "What is an integrated circuit (IC)? A vital component of modern electronics". Archived from the original on November 15, 2021. Retrieved November 15, 2021.
  64. ^ A. Thisted, Ronald (April 7, 1997). "Computer Architecture" (PDF). The University of Chicago. Archived (PDF) from the original on October 9, 2022.
  65. .
  66. .
  67. .
  68. from the original on May 6, 2022. Retrieved November 17, 2021.
  69. ^ Rapaport, William J. (September 20, 2013). "What Is Computation?". State University of New York at Buffalo. Archived from the original on February 14, 2001. Retrieved August 31, 2013.
  70. .
  71. .
  72. .
  73. .
  74. .
  75. ^ "Multi-Paradigm Programming Language". Mozilla Foundation. Archived from the original on August 21, 2013.
  76. S2CID 8625066
  77. ^ Patterson, David (August 1999). "Evaluating Computer Scientists and Engineers For Promotion and Tenure". Computing Research Association. Archived from the original on July 22, 2015. Retrieved July 19, 2015.
  78. .
  79. ^ Burns, Judith (April 3, 2016). "Computer science A-level 1970s style". Archived from the original on February 9, 2019. Retrieved February 9, 2019.
  80. ^ Jones, Michael (October 1915). "Developing a Computer Science Curriculum in England: Exploring Approaches in the USA" (PDF). Winston Churchill Memorial Trust. Archived from the original (PDF) on October 22, 2016. Retrieved February 9, 2019.
  81. ^ "Computer Science: Not Just an Elective Anymore". Education Week. February 25, 2014. Archived from the original on December 1, 2016. Retrieved July 20, 2015.
  82. ^ Wilson, Cameron; Sudol, Leigh Ann; Stephenson, Chris; Stehlik, Mark (2010). "Running on Empty: The Failure to Teach K–12 Computer Science in the Digital Age" (PDF). ACM. Archived (PDF) from the original on June 12, 2013. Retrieved July 20, 2015.
  83. ^ "2021 State of computer science education: Accelerating action through advocacy" (PDF)., CSTA, & ECEP Alliance. 2021. Archived (PDF) from the original on October 9, 2022.
  84. ^ "A is for algorithm". The Economist. April 26, 2014. Archived from the original on October 18, 2017. Retrieved August 26, 2017.
  85. ^ "Computing at School International comparisons" (PDF). Archived from the original (PDF) on May 8, 2013. Retrieved July 20, 2015.
  86. ^ "Adding Coding to the Curriculum". The New York Times. March 23, 2014. Archived from the original on January 1, 2022.

Further reading

External links