@@ Line 16: / Line 16: @@
 There are many different areas in [[computer]] [[science]]. Some areas consider problems in an [[Abstraction|abstract]] manner, while some need special [[machine]]s, called [[computer]]s.
-A person who works with computers will often need [[mathematics]], [[science]], and [[logic]] in order to design and work with computers.
+A person who works with computers will often need [[mathematics]],<ref>Rana, S. (2021, May 4). [https://www.evidyalam.com/2021/04/computer-number-systems.html ''Computer number systems: Binary, decimal, octal''.] eVidyalam. Retrieved January 12, 2022.</ref> [[science]], and [[logic]] in order to design and work with computers.
 ==History==
 {{main|History of computer science}}
@@ Line 23: / Line 22: @@
 [[File:Babbage40.png|upright|thumb|[[Charles Babbage]], sometimes referred to as the "father of computing".<ref>{{cite web|url=http://www.cbi.umn.edu/about/babbage.html|title=Charles Babbage Institute: Who Was Charles Babbage?|website=cbi.umn.edu|access-date=28 December 2016}}</ref> ]]
 [[File:Ada lovelace.jpg|upright|thumb|[[Ada Lovelace]] published the first [[algorithm]] intended for processing on a computer.<ref>{{cite web|url=http://www.computerhistory.org/babbage/adalovelace/|title=Ada Lovelace {{!}} Babbage Engine {{!}} Computer History Museum|website=www.computerhistory.org|access-date=28 December 2016}}</ref> ]]
 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. [[Algorithm]]s for performing computations have existed since antiquity, even before the development of sophisticated computing equipment.
@@ Line 29: / Line 27: @@
 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.<ref>The [[Association for Computing Machinery]] (ACM) was founded in 1947.</ref> 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.<ref>{{cite web|url=https://www.ibm.com/ibm/history/history/year_1945.html |title=IBM Archives: 1945 |publisher=Ibm.com |access-date=2019-03-19}}</ref> 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.<ref>{{cite web|url=https://www.ibm.com/ibm/history/ibm100/us/en/icons/compsci/ |title=IBM100 – The Origins of Computer Science |publisher=Ibm.com |date=1995-09-15 |access-date=2019-03-19}}</ref> Computer science began to be established as a distinct academic discipline in the 1950s and early 1960s.<ref name="Denning_cs_discipline"/><ref>{{cite web |url=http://www.cl.cam.ac.uk/conference/EDSAC99/statistics.html |title=Some EDSAC statistics |publisher=University of Cambridge |access-date=19 November 2011}}</ref> The world's first computer science degree program, the [[Cambridge Diploma in Computer Science]], began at the [[University of Cambridge]] [[Cambridge Computer Lab|Computer Laboratory]] in 1953. The first computer science department in the United States was formed at [[Purdue University]] in 1962.<ref>{{cite web |url=http://www.cs.purdue.edu/about/conte.html |title=Computer science pioneer Samuel D. Conte dies at 85 |date=July 1, 2002 |publisher=Purdue Computer Science |access-date=December 12, 2014}}</ref> Since practical computers became available, many applications of computing have become distinct areas of study in their own rights.{{see also|History of computing|History of informatics}}
 ==Etymology==
 {{see also|Informatics#Etymology}}
 Although first proposed in 1956,<ref name="Tedre2014">{{cite book|last=Tedre|first=Matti|title=The Science of Computing: Shaping a Discipline|publisher=Taylor and Francis / CRC Press|year=2014}}</ref> the term "computer science" appears in a 1959 article in ''[[Communications of the ACM]]'',<ref name="Fine_1959">
 {{cite journal
@@ Line 55: / Line 51: @@
   }}</ref> 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 [[epistemology|epistemologist]]''.<ref>{{cite journal |date=<!-- missing! --> |volume=1 |issue=4 |page=6| doi = 10.1145/368796.368802|last1=Weiss |first1=E.A. |title=Letters to the editor |journal= Communications of the ACM |last2=Corley |first2=Henry P.T. |s2cid=5379449 }}</ref> Three months later in the same journal, ''comptologist'' was suggested, followed next year by ''hypologist''.<ref>Communications of the ACM 2(1):p.4</ref> The term ''computics'' has also been suggested.<ref>IEEE Computer 28(12): p.136</ref> {{anchor|Name of the field in Europe}}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 language|Hungarian]]) or ''pliroforiki'' (''πληροφορική'', which means informatics) in [[Greek language|Greek]]. Similar words have also been adopted in the UK (as in ''the School of Informatics of the University of Edinburgh'').<ref>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.</ref>  "In the U.S., however, [[informatics]] is linked with applied computing, or computing in the context of another domain."<ref>{{cite web|last=Groth |first=Dennis P. |url=http://cacm.acm.org/magazines/2010/2/69363-why-an-informatics-degree |title=Why an Informatics Degree? |date = February 2010|work= Communications of the ACM |publisher=Cacm.acm.org}}</ref>
+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 [[epistemology|epistemologist]]''.<ref>{{cite journal |date=<!-- missing! --> |volume=1 |issue=4 |page=6| doi = 10.1145/368796.368802|last1=Weiss |first1=E.A. |title=Letters to the editor |journal= Communications of the ACM |last2=Corley |first2=Henry P.T. |s2cid=5379449 }}</ref> Three months later in the same journal, ''comptologist'' was suggested, followed next year by ''hypologist''.<ref>Communications of the ACM 2(1):p.4</ref> The term ''computics'' has also been suggested.<ref>IEEE Computer 28(12): p.136</ref> {{anchor|Name of the field in Europe}}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 language|Hungarian]]) or ''pliroforiki'' (''πληροφορική'', which means informatics) in [[Greek language|Greek]]. Similar words have also been adopted in the UK (as in ''the School of Informatics of the University of Edinburgh'').<ref>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.</ref> "In the U.S., however, [[informatics]] is linked with applied computing, or computing in the context of another domain."<ref>{{cite web|last=Groth |first=Dennis P. |url=http://cacm.acm.org/magazines/2010/2/69363-why-an-informatics-degree |title=Why an Informatics Degree? |date = February 2010|work= Communications of the ACM |publisher=Cacm.acm.org}}</ref>
 A folkloric quotation, often attributed to—but almost certainly not first formulated by—[[Edsger W. Dijkstra|Edsger Dijkstra]], states that "computer science is no more about computers than astronomy is about telescopes."<ref group=note>See the entry
@@ Line 63: / Line 59: @@
 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.
 == Common tasks for a computer scientist ==
 === Asking questions ===
@@ Line 71: / Line 66: @@
 The relationship between Computer Science and Software Engineering is a contentious issue, which is further muddied by [[Software engineer#Use of the title "Engineer"|disputes]] over what the term "Software Engineering" means, and how computer science is defined.<ref>{{Cite journal | last1 = Tedre | first1 = M. | title = Computing as a Science: A Survey of Competing Viewpoints | doi = 10.1007/s11023-011-9240-4 | journal = Minds and Machines | volume = 21 | issue = 3 | pages = 361–387 | year = 2011 | s2cid = 14263916 }}</ref> [[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.<ref>{{Cite journal | last1 = Parnas | first1 = D.L. | journal = Annals of Software Engineering | volume = 6 | pages = 19–37 | year = 1998 | doi = 10.1023/A:1018949113292|title=Software engineering programmes are not computer science programmes| s2cid = 35786237 }}, 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, […]"</ref>
 ==Philosophy==
 {{main|Philosophy 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.<ref>{{cite conference |author=Wegner, P. |title=Research paradigms in computer science—Proceedings of the 2nd international Conference on Software Engineering |location=San Francisco, California, United States |date=October 13–15, 1976 |publisher=IEEE Computer Society Press, Los Alamitos, CA}}</ref> [[Peter J. Denning|Peter Denning]]'s working group argued that they are theory, abstraction (modeling), and design.<ref>{{Cite journal | last1 = Denning | first1 = P.J. | last2 = Comer | first2 = D.E. | last3 = Gries | first3 = D. | last4 = Mulder | first4 = M.C. | last5 = Tucker | first5 = A. | last6 = Turner | first6 = A.J. | last7 = Young | first7 = P.R. | title = Computing as a discipline | journal = Communications of the ACM | volume = 32 | pages = 9–23 | date = January 1989 | doi = 10.1145/63238.63239| s2cid = 723103 }}</ref> 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 science]]s, identifiable in some branches of [[artificial intelligence]]).<ref>{{Cite journal | first1 = A.H. | title = Three Paradigms of Computer Science | journal = [[Minds and Machines]] | last1 = Eden | volume = 17 | issue = 2 | year = 2007 | url = http://www.eden-study.org/articles/2007/three_paradigms_of_computer_science.pdf | doi = 10.1007/s11023-007-9060-8 | pages = 135–167 | url-status=dead | archive-url = https://web.archive.org/web/20160215100211/http://www.eden-study.org/articles/2007/three_paradigms_of_computer_science.pdf | archive-date = February 15, 2016 | df = mdy-all | citeseerx = 10.1.1.304.7763 | s2cid = 3023076 }}</ref>
 Computer science focuses on methods involved in design, specification, programming, verification, implementation and testing of human-made computing systems.<ref>{{cite encyclopedia |last1=Turner |first1=Raymond |last2=Angius |first2=Nicola |editor1-last=Zalta |editor1-first=Edward N. |title=The Philosophy of Computer Science |encyclopedia=The Stanford Encyclopedia of Philosophy |date=2019 |url=https://plato.stanford.edu/archives/spr2019/entries/computer-science/}}</ref>
 ==Fields==
 {{Quote
@@ Line 85: / Line 78: @@
 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.<ref name="CSAB1997">{{cite web|publisher=Computing Sciences Accreditation Board|title=Computer Science as a Profession|url=http://www.csab.org/comp_sci_profession.html |date=May 28, 1997| access-date=23 May 2010 |archive-url = https://web.archive.org/web/20080617030847/http://www.csab.org/comp_sci_profession.html |archive-date = June 17, 2008}}</ref><ref>{{cite book |author=Committee on the Fundamentals of Computer Science: Challenges and Opportunities, National Research Council |title=Computer Science: Reflections on the Field, Reflections from the Field|url=http://www.nap.edu/catalog.php?record_id=11106#toc|publisher=National Academies Press|isbn=978-0-309-09301-9|year=2004}}</ref>
 [[CSAB (professional organization)|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)<ref>{{cite web |url=http://www.csab.org/ |title=CSAB Leading Computer Education |publisher=CSAB |date=August 3, 2011 |access-date=19 November 2011}}</ref>—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.<ref name="CSAB1997"/>
 ===Theoretical computer science===
 {{main|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====
 {{main|Theory of computation}}
 According to [[Peter J. Denning|Peter Denning]], the fundamental question underlying computer science is, "What can be automated?"<ref name="Denning_cs_discipline">{{cite journal | last=Denning | first=Peter J. | author-link=Peter J. Denning | year=2000 | title=Computer Science: The Discipline | url=http://www.idi.ntnu.no/emner/dif8916/denning.pdf | journal=Encyclopedia of Computer Science |archive-url = https://web.archive.org/web/20060525195404/http://www.idi.ntnu.no/emner/dif8916/denning.pdf |archive-date = May 25, 2006}}</ref> 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 versus NP problem|P = NP?]] problem, one of the [[Millennium Prize Problems]],<ref>[http://www.claymath.org/millennium/P_vs_NP/ Clay Mathematics Institute] P = NP {{webarchive |url=https://web.archive.org/web/20131014194456/http://www.claymath.org/millennium/P_vs_NP/ |date=October 14, 2013 }}</ref> is an open problem in the theory of computation.
+The famous [[P versus NP problem|P = NP?]] problem, one of the [[Millennium Prize Problems]],<ref>[https://www.claymath.org/millennium/P_vs_NP/ Clay Mathematics Institute] P = NP {{webarchive |url=https://web.archive.org/web/20131014194456/http://www.claymath.org/millennium/P_vs_NP/ |date=October 14, 2013 }}</ref> is an open problem in the theory of computation.
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 | [[Cellular automata]]
 |}
 ====Information and coding theory====
 {{main|Information theory|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.<ref>{{cite web |date=October 14, 2002 |last=P. Collins |first=Graham |title=Claude E. Shannon: Founder of Information Theory |url=http://www.scientificamerican.com/article.cfm?id=claude-e-shannon-founder |work=Scientific American |access-date=December 12, 2014}}</ref>
 Coding theory is the study of the properties of [[code]]s (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 [[Linear network coding|network coding]]. Codes are studied for the purpose of designing efficient and reliable [[data transmission]] methods.
 <ref>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}}.</ref>
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 | [[Kolmogorov complexity]]
 |}
 ====Data structures and algorithms====
 {{main|Data structure|Algorithm}}Data structures and algorithms are the studies of commonly used computational methods and their computational efficiency.
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 | [[Randomized algorithms]]
 |}
 ====Programming language theory and formal methods====
 {{main|Programming language theory|Formal methods}}
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 Formal methods are a particular kind of [[Mathematics|mathematically]] based technique for the [[formal specification|specification]], development and [[formal verification|verification]] of software and [[computer hardware|hardware]] systems.<ref>Phillip A. Laplante, 2010. Encyclopedia of Software Engineering Three-Volume Set (Print). CRC Press. p. 309. {{ISBN|978-1-351-24926-3}}.</ref> 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 system]]s, where safety or [[computer security|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 in computer science|logic]] calculi, [[formal language]]s, [[automata theory]], and [[program semantics]], but also [[type systems]] and [[algebraic data types]] to problems in software and hardware specification and verification.
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 | [[Automated theorem proving]]
 |}
 ===Computer systems and computational processes===
 ====Artificial intelligence====
 {{main|Artificial intelligence|Bio-inspired computing}}
 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 [[History of artificial intelligence|Dartmouth Conference]] (1956), artificial intelligence research has been necessarily cross-disciplinary, drawing on areas of expertise such as [[applied mathematics]], [[Mathematical logic|symbolic logic]], [[semiotics]], [[electrical engineering]], [[philosophy of mind]], [[neurophysiology]], and [[social intelligence]]. AI is associated in the popular mind with [[Robotics|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.
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 | [[Swarm intelligence]]
 |}
 ====Computer architecture and organization====
 {{main|Computer architecture|Computer organisation|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.<ref>{{cite web|last=A. Thisted|first=Ronald|title=Computer Architecture |url=http://galton.uchicago.edu/~thisted/Distribute/comparch.pdf|publisher=The University of Chicago|date=April 7, 1997}}</ref> Computer engineers study [[computational logic]] and design of [[computer hardware]], from individual [[Processor (computing)|processor]] components, [[microcontroller]]s, [[personal computer]]s to [[supercomputer]]s and [[embedded system]]s. The term “architecture” in computer literature can be traced to the work of Lyle R. Johnson and [[Fred Brooks|Frederick P. Brooks, Jr.]], members of the Machine Organization department in IBM's main research center in 1959.
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 | [[Interpreter (computing)|Interpreter]]
 |}
 ====Concurrent, parallel and distributed computing====
 {{main|Concurrency (computer science)|Distributed computing}}
 Concurrency is a property of systems in which several computations are executing simultaneously, and potentially interacting with each other.<ref>Jiacun Wang, 2017. Real-Time Embedded Systems. Wiley. p. 12. {{ISBN|978-1-119-42070-5}}.</ref> A number of mathematical models have been developed for general concurrent computation including [[Petri net]]s, [[Process calculus|process calculi]] and the [[Parallel random-access machine|Parallel Random Access Machine]] model.<ref>Gordana Dodig-Crnkovic; Raffaela Giovagnoli; 2013. Computing Nature: Turing Centenary Perspective. Springer Science & Business Media. p. 247. {{ISBN|978-3-642-37225-4}}.</ref> 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.<ref>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}}.</ref>
 ====Computer networks====
 {{main|Computer network}}
 This branch of computer science aims to manage networks between computers worldwide.
 ====Computer security and cryptography====
 {{main|Computer security|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. [[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 and data mining====
 {{main|Database|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 model]]s and [[query language]]s. Data mining is a process of discovering patterns in large data sets.
 ====Computer graphics and visualization====
 {{main|Computer graphics (computer science)}}
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 | [[Solid modeling]]
 |}
 ====Image and sound processing====
 {{main|Information processing}}
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 | [[Speech synthesis]]
 |}
 ===Applied computer science===
 ====Computational science, finance and engineering====
 {{main|Computational science|Computational finance|Computational engineering}}
 [[Scientific computing]] (or [[computational science]]) is the field of study concerned with constructing [[scientific modelling|mathematical models]] and [[numerical analysis|quantitative analysis]] techniques and using computers to analyze and solve [[Science|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,<ref>Muhammad H. Rashid, 2016. SPICE for Power Electronics and Electric Power. CRC Press. p. 6. {{ISBN|978-1-4398-6047-2}}.</ref> as well as software for physical realization of new (or modified) designs. The latter includes essential design software for [[integrated circuit]]s.{{Citation needed|date=October 2010}}
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 | [[Computational musicology]]
 |}
 ====Social computing and human-computer interaction====
 {{main|Social computing|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====
 {{main|Software engineering}}
 {{see also|Computer programming}}
 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. For example [[software testing]], [[systems engineering]], [[technical debt]] and [[software development process]]es.
 ==Discoveries==
 The philosopher of computing [[William J. Rapaport|Bill Rapaport]] noted three ''Great Insights of Computer Science'':<ref>{{cite web|url=http://www.cse.buffalo.edu/~rapaport/computation.html|title=What Is Computation?|publisher=State University of New York at Buffalo|last = Rapaport|first = William J.|date = 20 September 2013}}</ref>
@@ Line 372: / Line 334: @@
 {{see also|Turing machine}}
 * [[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".<ref>Charles W. Herbert, 2010. An Introduction to Programming Using Alice 2.2. Cengage Learning. p. 122. {{ISBN|0-538-47866-7}}.</ref>
 :: Only three rules are needed to combine any set of basic instructions into more complex ones:
 ::*''sequence'': first do this, then do that;
@@ Line 382: / Line 343: @@
 === Answering the question ===
-[[Algorithm]]s are a specific set of instructions or steps on how to complete a task. For example, a computer scientist wants to sort [[playing cards]]. There are many ways to sort them - by suits (diamonds, clubs, hearts, and spades) or by numbers (2, 3, 4, 5, 6, 7, 8, 9, 10, Jack, Queen, King, and Ace).  By deciding on a set of steps to sort the cards, the scientist has created an algorithm. The scientist then needs to test whether this algorithm works. This shows how well and how fast the algorithm sorts cards.
+[[Algorithm]]s are a specific set of instructions or steps on how to complete a task. For example, a computer scientist wants to sort [[playing cards]]. There are many ways to sort them - by suits (diamonds, clubs, hearts, and spades) or by numbers (2, 3, 4, 5, 6, 7, 8, 9, 10, Jack, Queen, King, and Ace). By deciding on a set of steps to sort the cards, the scientist has created an algorithm. The scientist then needs to test whether this algorithm works. This shows how well and how fast the algorithm sorts cards.
 A simple but slow algorithm is: pick up two cards and check whether they are sorted correctly. If they are not, reverse them. Then do it again with another two, and repeat them all until they are all sorted. This "bubble sort" method will work, but it will take a very long time.
 A better algorithm is: find the first card with the smallest suit and smallest number (2 of diamonds), and place it at the start. After this, look for the second card, and so on. This algorithm is much faster, and does not need much space. This algorithm is a "selection sort".
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 Computer science looks at the [[theory|theoretical]] parts of computers. [[Computer engineering]] looks at the physical [[computer hardware|parts]] of computers (hardware). [[Software engineering]] looks at the use of [[software|computer programs]] and how to make them.
 == Parts of computer science ==
 === Central math ===
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 * [[Symbolic logic]] (clear ways of talking about math)
 *[[Order of operations]] (which [[Operation (mathematics)|math operations]] are performed first)
 === How an ideal computer works ===
 * [[Algorithmic information theory]] (how easily can a computer answer a question?)
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 * [[Lexical analysis]] (how to turn words into data)
 * [[Microprogram]]ming (how to control the most important part of a computer)
-* [[Operating system]]s (big computer programs, e.g. [[Linux]], [[Microsoft Windows]], [[Mac OS]])  to control the computer hardware and software.
+* [[Operating system]]s (big computer programs, e.g. [[Linux]], [[Microsoft Windows]], [[Mac OS]]) to control the computer hardware and software.
 * [[Cryptography]] (hiding data)
 * [[Parallel computing]] (many instructions are carried out simultaneously)
 === Computer science at work ===
 * [[Artificial intelligence]] (making computers learn and talk, similar to people)
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 * [[Robot]]s (using computers to control machines)
 * [[Software engineering]] (how [[programmer]]s write programs)
 === What computer science does ===
 * [[Benchmark]] (testing a computer's power or speed)
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 * [[Software metric]]s (ways to measure computer programs, such as counting lines of code or number of operations)
 * [[Very large system integration|VLSI design]] (the making of a very large and complex computer system)
 ==Related pages==
 * [[Computing]]
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 * [[Computer jargon]]
 * [[List of words about computers|Encyclopedia of Computer Terms]]
 ==References==
 {{Sister project links| wikt=computer science|c=Category:Computer science | b=Computer science | q=Computer science|n=no|s=no| v=Computer science | voy=no | species=no | d=no}}
 {{reflist}}
 {{authority control}}
 [[Category:Computer science| ]]

Computer science: Difference between revisions

Computer science (edit)

Revision as of 08:45, 12 January 2022