Computer science

Computer science is the study of computation and information.[1][2] Computer science deals with theory of computation, algorithms, computational problems and the design of computer systems hardware, software and applications.[3][4] Computer science addresses both human-made and natural information processes, such as communication, control, perception, learning and intelligence especially in human-made computing systems and machines.[5][6][7] According to Peter Denning, the fundamental question underlying computer science is, What can be automated?[8][5]

Computer science deals with the theoretical foundations of information, its computation and practical techniques for their application.

Its fields can be divided into theoretical and practical disciplines. Computational complexity theory is highly abstract, while computer graphics and computational geometry emphasizes real-world applications. Algorithmics is called the heart of computer science.[9] Programming language theory considers approaches to the description of computational processes, while software engineering involves the use of programming languages and complex systems. Computer architecture and computer engineering deals with construction of computer components and computer-controlled equipment.[5][10] Human–computer interaction considers the challenges in making computers useful, usable, and accessible. Artificial intelligence aims to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, motion planning, learning, and communication found in humans and animals.

History

Charles Babbage, sometimes referred to as the "father of computing".[11]

Ada Lovelace is often credited with publishing the first algorithm intended for processing on a computer.[12]

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.

Wilhelm Schickard designed and constructed the first working mechanical calculator in 1623.[13] In 1673, Gottfried Leibniz demonstrated a digital mechanical calculator, called the Stepped Reckoner.[14] Leibniz may be considered the first computer scientist and information theorist, for, among other reasons, documenting the binary number system. In 1820, Thomas de Colmar launched the mechanical calculator industry[note 1] when he invented his simplified arithmometer, the first calculating machine strong enough and reliable enough to be used daily in an office environment. Charles Babbage started the design of the first automatic mechanical calculator, his Difference Engine, in 1822, which eventually gave him the idea of the first programmable mechanical calculator, his Analytical Engine.[15] He started developing this machine in 1834, and "in less than two years, he had sketched out many of the salient features of the modern computer".[16] "A crucial step was the adoption of a punched card system derived from the Jacquard loom"[16] making it infinitely programmable.[note 2] In 1843, during the translation of a French article on the Analytical Engine, Ada Lovelace wrote, in one of the many notes she included, an algorithm to compute the Bernoulli numbers, which is considered to be the first published algorithm ever specifically tailored for implementation on a computer.[17] Around 1885, Herman Hollerith invented the tabulator, which used punched cards to process statistical information; eventually his company became part of IBM. Following Babbage, although unaware of his earlier work, Percy Ludgate in 1909 published [18] the 2nd of the only two designs for mechanical analytical engines in history. 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[19] 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".[20]

During the 1940s, with the development of new and more powerful computing machines such as the Atanasoff–Berry computer and ENIAC, the term computer came to refer to the machines rather than their human predecessors.[21] 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. In 1945, IBM founded the Watson Scientific Computing Laboratory at Columbia University in New York City. The renovated fraternity house on Manhattan's West Side was IBM's first laboratory devoted to pure science. The lab is the forerunner of IBM's Research Division, which today operates research facilities around the world.[22] Ultimately, the close relationship between IBM and the university was instrumental in the emergence of a new scientific discipline, with Columbia offering one of the first academic-credit courses in computer science in 1946.[23] Computer science began to be established as a distinct academic discipline in the 1950s and early 1960s.[5][24] 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 department in the United States was formed at Purdue University in 1962.[25] Since practical computers became available, many applications of computing have become distinct areas of study in their own rights.

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.[26][27] 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[28] and later the IBM 709[29] 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".[26] During the late 1950s, the computer science discipline was very much in its developmental stages, and such issues were commonplace.[27]

The concept of a field-effect transistor was proposed by Julius Edgar Lilienfeld in 1925. John Bardeen and Walter Brattain, while working under William Shockley at Bell Labs, built the first working transistor, the point-contact transistor, in 1947.[30][31] In 1953, the University of Manchester built the first transistorized computer, called the Transistor Computer.[32] However, early junction transistors were relatively bulky devices that were difficult to manufacture on a mass-production basis, which limited them to a number of specialised applications.[33] The metal–oxide–silicon field-effect transistor (MOSFET, or MOS transistor) was invented by Mohamed Atalla and Dawon Kahng at Bell Labs in 1959.[34][35] It was the first truly compact transistor that could be miniaturised and mass-produced for a wide range of uses.[33] The MOSFET made it possible to build high-density integrated circuit chips,[36][37] leading to what is known as the computer revolution[38] or microcomputer revolution.[39]

Time has seen significant improvements in the usability and effectiveness of computing technology.[40] Modern society has seen a significant shift in the demographics which make use of computer technology; usage has shifted from being mostly exclusive to experts and professionals, to a near-ubiquitous user base. Initially, computers were quite costly, and some degree of humanitarian aid was needed for efficient use—in part from professional computer operators. As computer adoption became more widespread and affordable, less human assistance was needed for common usage.

Etymology

Although first proposed in 1956,[27] the term "computer science" appears in a 1959 article in Communications of the ACM,[41] 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,[42] justifying 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.[41] His efforts, and those of others such as numerical analyst George Forsythe, were rewarded: universities went on to create such departments, starting with Purdue in 1962.[43] 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.[44] Certain departments of major universities prefer the term computing science, to emphasize precisely that difference. Danish scientist Peter Naur suggested the term datalogy,[45] 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. An alternative term, also proposed by Naur, is data science; this is now used for a multi-disciplinary 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 ACM—turingineer, turologist, flow-charts-man, applied meta-mathematician, and applied epistemologist.[46] Three months later in the same journal, comptologist was suggested, followed next year by hypologist.[47] The term computics has also been suggested.[48] 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 of the University of Edinburgh).[49] "In the U.S., however, informatics is linked with applied computing, or computing in the context of another domain."[50]

A folkloric quotation, often attributed to—but almost certainly not first formulated by—Edsger Dijkstra, states that "computer science is no more about computers than astronomy is about telescopes."[note 3] 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, biology, 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.[5] 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.[27]

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.[51] 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.[52]

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.

Philosophy

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.[53] Peter Denning's working group argued that they are theory, abstraction (modeling), and design.[54] 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, identifiable in some branches of artificial intelligence).[55] Computer science focuses on methods involved in design, specification, programming, verification, implementation and testing of human-made computing systems.[56]

Fields

Computer science is no more about computers than astronomy is about telescopes.
— Edsger Dijkstra

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.[57][58] 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)[59]—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, human–computer interaction, computer graphics, operating systems, and numerical and symbolic computation as being important areas of computer science.[57]

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. All studies related to mathematical, logic and formal concepts and methods could be considered as theoretical computer science, provided that the motivation is clearly drawn from the field of computing.

Theory of computation

According to Peter Denning, the fundamental question underlying computer science is, "What can be automated?"[5] 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,[60] is an open problem in the theory of computation.

	$M=\{X:X\not \in X\}$
Automata theory	Computability theory	Computational complexity theory
GNITIRW-TERCES
Cryptography	Quantum computing theory

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.[61] Coding theory is the study of the properties of codes (systems for converting information from one form to another) and their fitness for a specific application. Codes are used for data compression, cryptography, error detection and correction, and more recently also for network coding. Codes are studied for the purpose of designing efficient and reliable data transmission methods. [62]


	Information theory

Source coding	Channel capacity	Signal detection theory
`gcc -Os foo.c`		${\hat {\theta }}={\underset {\theta }{\operatorname {argmax} }}\;P(x\|\theta )$
Algorithmic information theory	Information geometry	Estimation theory

Data structures and algorithms

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

$O (n 2)$
Analysis of algorithms	Algorithms	Data structures	Combinatorial optimization	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 an active research area, with numerous dedicated academic journals.

		$\Gamma \vdash x:{\text{Int}}$
Formal languages	Formal semantics	Type theory	Compiler design	Programming languages

Formal methods

Formal methods are a particular kind of mathematically based technique for the specification, development and verification of software and hardware systems.[63] 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. They form an important theoretical underpinning for software engineering, especially where safety or security is involved. Formal methods are a useful adjunct to software testing since they help avoid errors and can also give a framework for testing. For industrial use, tool support is required. However, the high cost of using formal methods means that they are usually only used in the development of high-integrity and life-critical 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.


Formal verification	Formal semantics	Automated theorem proving

Computer systems

Computer architecture and computer 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.[64] The field often involves disciplines of computer engineering and electrical engineering, selecting and interconnecting hardware components to create computers that meet functional, performance, and cost goals.


Digital logic	Microarchitecture	Multiprocessing

Ubiquitous computing	Systems architecture	Operating systems

Computer performance analysis

Computer performance analysis is the study of work flowing through computers with the general goals of improving throughput, controlling response time, using resources efficiently, eliminating bottlenecks, and predicting performance under anticipated peak loads.[65] Benchmarks are used to compare the performance of systems carrying different chips and/or system architectures.[66]

Concurrent, parallel and distributed systems

Concurrency is a property of systems in which several computations are executing simultaneously, and potentially interacting with each other.[67] A number of mathematical models have been developed for general concurrent computation including Petri nets, process calculi and the Parallel Random Access Machine model.[68] 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.[69]

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 an objective of protecting 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.

Databases

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.

Computer applications

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

Human–computer interaction

Research that develops theories, principles, and guidelines for user interface designers, so they can create satisfactory user experiences with desktop, laptop, and mobile devices.

Scientific computing and simulation

Main article: Computational science

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,[70] as well as software for physical realization of new (or modified) designs. The latter includes essential design software for integrated circuits.


Numerical analysis	Computational physics	Computational chemistry	Bioinformatics	Computational neuroscience

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.


Machine learning	Computer vision	Image processing

Pattern recognition	Data mining	Evolutionary computation

Knowledge representation and reasoning	Natural language processing	Robotics

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 doesn't just deal with the creation or manufacture of new software, but its internal arrangement and maintenance.

Discoveries

The philosopher of computing Bill Rapaport noted three Great Insights of Computer Science:[71]

Gottfried Wilhelm Leibniz's, George Boole's, Alan Turing's, Claude Shannon's, and Samuel Morse's insight: there are only two objects that a computer has to deal with in order to represent "anything".[note 4]

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:[72]

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".[73]

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.

Note that 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.[74]
Imperative programming, a programming paradigm that uses statements that change a program's state.[75] 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.[76]

Many languages offer support for multiple paradigms, making the distinction more a matter of style than of technical capabilities.[77]

Academia

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.[78][79] 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.[80]

Education

Computer Science, known by its near synonyms, Computing, Computer Studies, Information Technology (IT) and Information and Computing Technology (ICT), has been taught in UK schools since the days of batch processing, mark sensitive cards and paper tape but usually to a select few students.[81] In 1981, the BBC produced a micro-computer and classroom network and Computer Studies became common for GCE O level students (11–16-year-old), and Computer Science to A level students. Its importance was recognised, and it became a compulsory part of the National Curriculum, for Key Stage 3 & 4. In September 2014 it became an entitlement for all pupils over the age of 4.[82]

In the US, with 14,000 school districts deciding the curriculum, provision was fractured.[83] 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.[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]

gollark: Although they all have annoying tradeoffs, so the best approach is probably just to destroy all quantum computers?

gollark: Some of the post-quantum approaches seem at least vaguely related to number theory.

gollark: The Navier-Stokes thing.

gollark: Did you know? 3325581707333960528 is the smallest number that cannot be written as a sum of two primes where one is smaller than 9781.

gollark: ||Surely the communication and seeing other people's hat color thing is irrelevant too! It provides no information about your hat.||

Notes

In 1851
"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
See the entry "Computer science" on Wikiquote for the history of this quotation.
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).

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Parnas, D.L. (1998). "Software engineering programmes are not computer science programmes". Annals of Software Engineering. 6: 19–37. doi:10.1023/A:1018949113292., 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, […]"
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.
Denning, P.J.; Comer, D.E.; Gries, D.; Mulder, M.C.; Tucker, A.; Turner, A.J.; Young, P.R. (January 1989). "Computing as a discipline". Communications of the ACM. 32: 9–23. doi:10.1145/63238.63239.
Eden, A.H. (2007). "Three Paradigms of Computer Science" (PDF). Minds and Machines. 17 (2): 135–167. CiteSeerX 10.1.1.304.7763. doi:10.1007/s11023-007-9060-8. Archived from the original (PDF) on February 15, 2016.
Turner, Raymond; Angius, Nicola (2019). "The Philosophy of Computer Science". In Zalta, Edward N. (ed.). The Stanford Encyclopedia of Philosophy.
"Computer Science as a Profession". Computing Sciences Accreditation Board. May 28, 1997. Archived from the original on June 17, 2008. Retrieved May 23, 2010.
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.
"CSAB Leading Computer Education". CSAB. August 3, 2011. Retrieved November 19, 2011.
Clay Mathematics Institute P = NP Archived October 14, 2013, at the Wayback Machine
P. Collins, Graham (October 14, 2002). "Claude E. Shannon: Founder of Information Theory". Scientific American. Retrieved December 12, 2014.
Van-Nam Huynh; Vladik Kreinovich; Songsak Sriboonchitta; 2012. Uncertainty Analysis in Econometrics with Applications. Springer Science & Business Media. p. 63. ISBN 978-3-642-35443-4.
Phillip A. Laplante, 2010. Encyclopedia of Software Engineering Three-Volume Set (Print). CRC Press. p. 309. ISBN 978-1-351-24926-3.
A. Thisted, Ronald (April 7, 1997). "Computer Architecture" (PDF). The University of Chicago.
Wescott, Bob (2013). The Every Computer Performance Book, Chapter 3: Useful laws. CreateSpace. ISBN 978-1-4826-5775-3.
Lawrence A. Tomei, 2009. Lexicon of Online and Distance Learning. R&L Education. p. 29. ISBN 978-1-60709-285-8.
Jiacun Wang, 2017. Real-Time Embedded Systems. Wiley. p. 12. ISBN 978-1-119-42070-5.
Gordana Dodig-Crnkovic; Raffaela Giovagnoli; 2013. Computing Nature: Turing Centenary Perspective. Springer Science & Business Media. p. 247. ISBN 978-3-642-37225-4.
Simon Elias Bibri; 2018. Smart Sustainable Cities of the Future: The Untapped Potential of Big Data Analytics and Context-Aware Computing for Advancing Sustainability. Springer. p. 74. ISBN 978-3-319-73981-6.
Muhammad H. Rashid, 2016. SPICE for Power Electronics and Electric Power. CRC Press. p. 6. ISBN 978-1-4398-6047-2.
Rapaport, William J. (September 20, 2013). "What Is Computation?". State University of New York at Buffalo.
B. Jack Copeland, 2012. Alan Turing's Electronic Brain: The Struggle to Build the ACE, the World's Fastest Computer. OUP Oxford. p. 107. ISBN 978-0-19-960915-4.
Charles W. Herbert, 2010. An Introduction to Programming Using Alice 2.2. Cengage Learning. p. 122. ISBN 0-538-47866-7.
Md. Rezaul Karim; Sridhar Alla; 2017. Scala and Spark for Big Data Analytics: Explore the concepts of functional programming, data streaming, and machine learning. Packt Publishing Ltd. p. 87. ISBN 978-1-78355-050-0.
Lex Sheehan, 2017. Learning Functional Programming in Go: Change the way you approach your applications using functional programming in Go. Packt Publishing Ltd. p. 16. ISBN 978-1-78728-604-7.
Evelio Padilla, 2015. Substation Automation Systems: Design and Implementation. Wiley. p. 245. ISBN 978-1-118-98730-8.
"Multi-Paradigm Programming Language". developer.mozilla.org. Mozilla Foundation. Archived from the original on August 21, 2013.
Meyer, Bertrand (April 2009). "Viewpoint: Research evaluation for computer science". Communications of the ACM. 25 (4): 31–34. doi:10.1145/1498765.1498780.
Patterson, David (August 1999). "Evaluating Computer Scientists and Engineers For Promotion and Tenure". Computing Research Association.
Fortnow, Lance (August 2009). "Viewpoint: Time for Computer Science to Grow Up". Communications of the ACM. 52 (8): 33–35. doi:10.1145/1536616.1536631.
Burns, Judith (April 3, 2016). "Computer science A-level 1970s style". Retrieved February 9, 2019.
Jones, Michael (October 1915). "Developing a Computer Science Curriculum in England: Exploring Approaches in the USA" (PDF). Winston Churchill Memorial Trust. Retrieved February 9, 2019.
"Computer Science: Not Just an Elective Anymore". Education Week. February 25, 2014.
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.
"A is for algorithm". The Economist. April 26, 2014.
"Computing at School International comparisons" (PDF). Retrieved July 20, 2015.
"Adding Coding to the Curriculum". The New York Times. March 23, 2014.

External links

Bibliography and academic search engines

CiteSeer^x (article): search engine, digital library and repository for scientific and academic papers with a focus on computer and information science.
DBLP Computer Science Bibliography (article): computer science bibliography website hosted at Universität Trier, in Germany.
The Collection of Computer Science Bibliographies (article)

Professional organizations

Misc

Computer Science—Stack Exchange: a community-run question-and-answer site for computer science
What is computer science
Is computer science science?
Computer Science (Software) Must be Considered as an Independent Discipline.

This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.

[15] In 1851

[18] "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

[53] See the entry "Computer science" on Wikiquote for the history of this quotation.

[75] 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". www.cs.york.ac.uk. Retrieved June 11, 2020.

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[23] The Association for Computing Machinery (ACM) was founded in 1947.

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[Moskowitz-35] Moskowitz, Sanford L. (2016). Advanced Materials Innovation: Managing Global Technology in the 21st century. John Wiley & Sons. pp. 165–167. ISBN 9780470508923.

[computerhistory-36] "1960 - Metal Oxide Semiconductor (MOS) Transistor Demonstrated". The Silicon Engine. Computer History Museum.

[Lojek-37] Lojek, Bo (2007). History of Semiconductor Engineering. Springer Science & Business Media. pp. 321–3. ISBN 9783540342588.

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[41] Malmstadt, Howard V.; Enke, Christie G.; Crouch, Stanley R. (1994). Making the Right Connections: Microcomputers and Electronic Instrumentation. American Chemical Society. p. 389. ISBN 9780841228610. The relative simplicity and low power requirements of MOSFETs have fostered today's microcomputer revolution.

[42] "Timeline of Computer History". Computer History Museum. Retrieved November 24, 2015.

[Fine_1959-43] Louis Fine (1959). "The Role of the University in Computers, Data Processing, and Related Fields". Communications of the ACM. 2 (9): 7–14. doi:10.1145/368424.368427.

[44] "Stanford University Oral History". Stanford University. Retrieved May 30, 2013.

[45] Donald Knuth (1972). "George Forsythe and the Development of Computer Science". Comms. ACM. Archived October 20, 2013, at the Wayback Machine

[46] Matti Tedre (2006). "The Development of Computer Science: A Sociocultural Perspective" (PDF). p. 260. Retrieved December 12, 2014.

[47] Peter Naur (1966). "The science of datalogy". Communications of the ACM. 9 (7): 485. doi:10.1145/365719.366510.

[48] Weiss, E.A.; Corley, Henry P.T. "Letters to the editor". Communications of the ACM. 1 (4): 6. doi:10.1145/368796.368802.

[49] Communications of the ACM 2(1):p.4

[50] IEEE Computer 28(12): p.136

[51] 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.

[52] Groth, Dennis P. (March 13, 2010). "Why an Informatics Degree? | February 2010 | Communications of the ACM". Cacm.acm.org. Retrieved March 19, 2019.

[54] Tedre, M. (2011). "Computing as a Science: A Survey of Competing Viewpoints". Minds and Machines. 21 (3): 361–387. doi:10.1007/s11023-011-9240-4.

[55] Parnas, D.L. (1998). "Software engineering programmes are not computer science programmes". Annals of Software Engineering. 6: 19–37. doi:10.1023/A:1018949113292., 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, […]"

[56] 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.

[57] Denning, P.J.; Comer, D.E.; Gries, D.; Mulder, M.C.; Tucker, A.; Turner, A.J.; Young, P.R. (January 1989). "Computing as a discipline". Communications of the ACM. 32: 9–23. doi:10.1145/63238.63239.

[58] Eden, A.H. (2007). "Three Paradigms of Computer Science" (PDF). Minds and Machines. 17 (2): 135–167. CiteSeerX 10.1.1.304.7763. doi:10.1007/s11023-007-9060-8. Archived from the original (PDF) on February 15, 2016.

[59] Turner, Raymond; Angius, Nicola (2019). "The Philosophy of Computer Science". In Zalta, Edward N. (ed.). The Stanford Encyclopedia of Philosophy.

[CSAB1997-60] "Computer Science as a Profession". Computing Sciences Accreditation Board. May 28, 1997. Archived from the original on June 17, 2008. Retrieved May 23, 2010.

[61] 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.

[62] "CSAB Leading Computer Education". CSAB. August 3, 2011. Retrieved November 19, 2011.

[63] Clay Mathematics Institute P = NP Archived October 14, 2013, at the Wayback Machine

[64] P. Collins, Graham (October 14, 2002). "Claude E. Shannon: Founder of Information Theory". Scientific American. Retrieved December 12, 2014.

[65] Van-Nam Huynh; Vladik Kreinovich; Songsak Sriboonchitta; 2012. Uncertainty Analysis in Econometrics with Applications. Springer Science & Business Media. p. 63. ISBN 978-3-642-35443-4.

[66] Phillip A. Laplante, 2010. Encyclopedia of Software Engineering Three-Volume Set (Print). CRC Press. p. 309. ISBN 978-1-351-24926-3.

[67] A. Thisted, Ronald (April 7, 1997). "Computer Architecture" (PDF). The University of Chicago.

[68] Wescott, Bob (2013). The Every Computer Performance Book, Chapter 3: Useful laws. CreateSpace. ISBN 978-1-4826-5775-3.

[69] Lawrence A. Tomei, 2009. Lexicon of Online and Distance Learning. R&L Education. p. 29. ISBN 978-1-60709-285-8.

[70] Jiacun Wang, 2017. Real-Time Embedded Systems. Wiley. p. 12. ISBN 978-1-119-42070-5.

[71] Gordana Dodig-Crnkovic; Raffaela Giovagnoli; 2013. Computing Nature: Turing Centenary Perspective. Springer Science & Business Media. p. 247. ISBN 978-3-642-37225-4.

[72] Simon Elias Bibri; 2018. Smart Sustainable Cities of the Future: The Untapped Potential of Big Data Analytics and Context-Aware Computing for Advancing Sustainability. Springer. p. 74. ISBN 978-3-319-73981-6.

[73] Muhammad H. Rashid, 2016. SPICE for Power Electronics and Electric Power. CRC Press. p. 6. ISBN 978-1-4398-6047-2.

[74] Rapaport, William J. (September 20, 2013). "What Is Computation?". State University of New York at Buffalo.

[76] B. Jack Copeland, 2012. Alan Turing's Electronic Brain: The Struggle to Build the ACE, the World's Fastest Computer. OUP Oxford. p. 107. ISBN 978-0-19-960915-4.

[77] Charles W. Herbert, 2010. An Introduction to Programming Using Alice 2.2. Cengage Learning. p. 122. ISBN 0-538-47866-7.

[78] Md. Rezaul Karim; Sridhar Alla; 2017. Scala and Spark for Big Data Analytics: Explore the concepts of functional programming, data streaming, and machine learning. Packt Publishing Ltd. p. 87. ISBN 978-1-78355-050-0.

[79] Lex Sheehan, 2017. Learning Functional Programming in Go: Change the way you approach your applications using functional programming in Go. Packt Publishing Ltd. p. 16. ISBN 978-1-78728-604-7.

[80] Evelio Padilla, 2015. Substation Automation Systems: Design and Implementation. Wiley. p. 245. ISBN 978-1-118-98730-8.

[81] "Multi-Paradigm Programming Language". developer.mozilla.org. Mozilla Foundation. Archived from the original on August 21, 2013.

[82] Meyer, Bertrand (April 2009). "Viewpoint: Research evaluation for computer science". Communications of the ACM. 25 (4): 31–34. doi:10.1145/1498765.1498780.

[83] Patterson, David (August 1999). "Evaluating Computer Scientists and Engineers For Promotion and Tenure". Computing Research Association.

[84] Fortnow, Lance (August 2009). "Viewpoint: Time for Computer Science to Grow Up". Communications of the ACM. 52 (8): 33–35. doi:10.1145/1536616.1536631.

[85] Burns, Judith (April 3, 2016). "Computer science A-level 1970s style". Retrieved February 9, 2019.

[Jones-86] Jones, Michael (October 1915). "Developing a Computer Science Curriculum in England: Exploring Approaches in the USA" (PDF). Winston Churchill Memorial Trust. Retrieved February 9, 2019.

[87] "Computer Science: Not Just an Elective Anymore". Education Week. February 25, 2014.

[88] 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.

[89] "A is for algorithm". The Economist. April 26, 2014.

[90] "Computing at School International comparisons" (PDF). Retrieved July 20, 2015.

[91] "Adding Coding to the Curriculum". The New York Times. March 23, 2014.

Computer science
Note: This template roughly follows the 2012 ACM Computing Classification System.
Hardware	Printed circuit board Peripheral Integrated circuit Very Large Scale Integration Systems on Chip (SoCs) Energy consumption (Green computing) Electronic design automation Hardware acceleration
Computer systems organization	Computer architecture Embedded system Real-time computing Dependability
Networks	Network architecture Network protocol Network components Network scheduler Network performance evaluation Network service
Software organization	Interpreter Middleware Virtual machine Operating system Software quality
Software notations and tools	Programming paradigm Programming language Compiler Domain-specific language Modeling language Software framework Integrated development environment Software configuration management Software library Software repository
Software development	Software development process Requirements analysis Software design Software construction Software deployment Software maintenance Programming team Open-source model
Theory of computation	Model of computation Formal language Automata theory Computability theory Computational complexity theory Logic Semantics
Algorithms	Algorithm design Analysis of algorithms Algorithmic efficiency Randomized algorithm Computational geometry
Mathematics of computing	Discrete mathematics Probability Statistics Mathematical software Information theory Mathematical analysis Numerical analysis
Information systems	Database management system Information storage systems Enterprise information system Social information systems Geographic information system Decision support system Process control system Multimedia information system Data mining Digital library Computing platform Digital marketing World Wide Web Information retrieval
Security	Cryptography Formal methods Security services Intrusion detection system Hardware security Network security Information security Application security
Human–computer interaction	Interaction design Social computing Ubiquitous computing Visualization Accessibility
Concurrency	Concurrent computing Parallel computing Distributed computing Multithreading Multiprocessing
Artificial intelligence	Natural language processing Knowledge representation and reasoning Computer vision Automated planning and scheduling Search methodology Control method Philosophy of artificial intelligence Distributed artificial intelligence
Machine learning	Supervised learning Unsupervised learning Reinforcement learning Multi-task learning Cross-validation
Graphics	Animation Rendering Image manipulation Graphics processing unit Mixed reality Virtual reality Image compression Solid modeling
Applied computing	E-commerce Enterprise software Computational mathematics Computational physics Computational chemistry Computational biology Computational social science Computational engineering Computational healthcare Digital art Electronic publishing Cyberwarfare Electronic voting Video games Word processing Operations research Educational technology Document management
Book Category Outline WikiProject Commons