Computer science

Study of computation

For other uses, see Computer science (disambiguation).

Fundamental areas of computer science

Programming language theory

Computational complexity theory

Artificial intelligence

Computer architecture

Computer science
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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]

Algorithms and data structures are central to computer science.^[7] The theory of computation concerns abstract

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]^{[8][3][9][10]} The Turing Award is generally recognized as the highest distinction in computer science.^[11][12]

History

Main article: History of computer science

History of computing
Hardware
Hardware before 1960 Hardware 1960s to present
Software
Software Software configuration management Unix Free software and open-source software
Computer science
Artificial intelligence Compiler construction Early computer science Operating systems Programming languages Prominent pioneers Software engineering
Modern concepts
General-purpose CPUs Graphical user interface Internet Laptops Personal computers Video games World Wide Web Cloud
By country
Bulgaria Eastern Bloc Poland Romania South America Soviet Union Yugoslavia
Timeline of computing
before 1950 1950–1979 1980–1989 1990–1999 2000–2009 2010–2019 2020–present more timelines ...
Glossary of computer science
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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.^[16]

Howard Aiken convinced IBM, which was making all kinds of punched card equipment and was also in the calculator business^[28] 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".^[29]

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

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

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.[33] 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.

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 that 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]

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.^[33] 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]

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

According to

is an open problem in the theory of computation.

		${\displaystyle M=\{X:X\not \in X\}}$
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

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

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

O(n2)
Analysis of algorithms	Algorithm design	Data structures	Combinatorial optimization	Computational geometry	Randomized algorithms

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

	${\displaystyle \Gamma \vdash x:{\text{Int}}}$
Formal semantics	Type theory	Compiler design	Programming languages	Formal verification	Automated theorem proving

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

.

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

Image and sound processing

Information can take the form of images, sound, video or other multimedia. Bits of information can be streamed via signals. Its processing is the central notion of informatics, the European view on computing, which studies information processing algorithms independently of the type of information carrier – whether it is electrical, mechanical or biological. This field plays important role in information theory, telecommunications, 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 the unsolved problems in theoretical computer science.

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

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 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 (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 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.

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.

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

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.

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]

• 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:^[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 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

Further reading

• Tucker, Allen B. (2004). Computer Science Handbook (2nd ed.). Chapman and Hall/CRC.
  ISBN 978-1-58488-360-9
  .
• ISBN 978-1-56159-248-7. Archived
  from the original on June 8, 2020. Retrieved February 6, 2011.
• Edwin D. Reilly (2003). Milestones in Computer Science and Information Technology. Greenwood Publishing Group.
  ISBN 978-1-57356-521-9
  .
• Knuth, Donald E. (1996). Selected Papers on Computer Science. CSLI Publications, Cambridge University Press.
• Collier, Bruce (1990). The little engine that could've: The calculating machines of Charles Babbage. Garland Publishing Inc.
  ISBN 978-0-8240-0043-1. Archived
  from the original on January 20, 2007. Retrieved May 4, 2013.
• Cohen, Bernard (2000). Howard Aiken, Portrait of a computer pioneer. The MIT press.
  ISBN 978-0-262-53179-5
  .
• Tedre, Matti (2014). The Science of Computing: Shaping a Discipline. CRC Press, Taylor & Francis.
• ISBN 978-3-540-06169-4
  .
• S2CID 1737953. Archived from the original
  (PDF) on September 21, 2013.
• Peter J. Denning. Is computer science science?, Communications of the ACM, April 2005.
• Peter J. Denning, Great principles in computing curricula, Technical Symposium on Computer Science Education, 2004.

External links

Wikibooks has a book on the topic of: Informatics Practices for Class XI (CBSE)

Library resources about
Computer science

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• Resources in your library
• Resources in other libraries

• DBLP Computer Science Bibliography
• Association for Computing Machinery
• Institute of Electrical and Electronics Engineers