'Computer science', or 'computing science', is the study of the theoretical foundations of
information and
computation and their implementation and application in
computer systems.
[1][2][3] Computer science has many sub-fields; some emphasize the computation of specific results (such as
computer graphics), while others relate to properties of
computational problems (such as
computational complexity theory). Still others focus on the challenges in implementing computations. For example,
programming language theory studies approaches to describing computations, while
computer programming applies specific
programming languages to solve specific computational problems. A further subfield,
human-computer interaction, focuses on the challenges in making computers and computations useful, usable and universally accessible to
people.
History
The history of computer science predates the invention of the modern
digital computer by many centuries. Machines for calculating fixed numerical tasks, such as the
abacus, have existed since antiquity.
Wilhelm Schickard built the first mechanical calculator in 1623.
[4] Charles Babbage designed a
difference engine in
Victorian times (between 1837 and 1901)
[5] helped by
Ada Lovelace.
[6] Around 1900 the
IBM corporation sold punch-card machines.
[7] However all of these machines were constrained to perform a single task, or at best, some subset of all possible tasks.
During the 1940s, as newer and more powerful computing machines were developed, the term ''computer'' came to refer to the machines rather than their human predecessors. As it became clear that computers could be used for more than just mathematical calculations, the field of computer science broadened to study
computation in general. Computer science began to be established as a distinct academic discipline in the 1960s, with the creation of the first computer science departments and degree programs.
[8] Since practical computers became available, many applications of computing have become distinct areas of study in their own right.
Major achievements
German military used the
Enigma machine during
World War II for communication they thought to be secret. The large-scale decryption of Enigma traffic at
Bletchley Park was an important factor that contributed to Allied victory in WWII.
12
Despite its relatively short history as a formal academic discipline, computer science has made a number of fundamental contributions to
science and
society. These include:
;Applications within computer science
★ A formal definition of
computation and
computability, and proof that there are computationally
unsolvable and
intractable problems.
[9]
★ The concept of a
programming language, a tool for the precise expression of methodological information at various levels of abstraction
[10]
;Applications outside of computing
★ Sparked the
Digital Revolution which led to the current
Information Age[11]
★ In
cryptography,
breaking the Enigma machine was an important factor contributing to the Allied victory in World War II.
[12]
★
Scientific computing enabled advanced study of the mind and mapping the human genome was possible with
Human Genome Project.
projects like
Folding@home explore
protein folding.
Relationship with other fields
Despite its name, much of computer science does not involve the study of computers themselves. Because of this several alternative names have been proposed. Danish scientist Peter Naur suggested the term datalogy, to reflect the fact that the scientific discipline revolves around data and data treatment, while not necessarily involving computers. The first scientific institution applying the datalogy term was DIKU, the Department of Datalogy at the University of Copenhagen, founded in 1969, with Peter Naur being the first professor in datalogy. The term is used mainly in the Scandinavian countries. Also, in the early days of computing, a number of terms for the practitioners of the field of computing were suggested in the ''Communications of the ACM''—''turingineer'', ''turologist'', ''flow-charts-man'', ''applied meta-mathematician'', and ''applied epistemologist''.
[13] Three months later in the same journal, ''comptologist'' was suggested, followed next year by ''hypologist''.
[14] Recently the term ''computics'' has been suggested.
[15]
In fact, the renowned computer scientist
Edsger Dijkstra is often quoted as saying, ''"Computer science is no more about computers than astronomy is about telescopes."'' 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. Computer science is sometimes criticized as being insufficiently scientific, a view espoused in the statement ''"Science is to computer science as hydrodynamics is to plumbing"'' credited to
Stan Kelly-Bootle[16] and others. However, there has been much cross-fertilization of ideas between the various computer-related disciplines. Computer science research has also often crossed into other disciplines, such as
artificial intelligence,
cognitive science,
physics (see
quantum computing), and
linguistics.
Computer science is considered by some to have a much closer relationship with
mathematics than many scientific disciplines.
8 Early computer science was strongly influenced by the work of mathematicians such as
Kurt Gödel and
Alan Turing, and there continues to be a useful interchange of ideas between the two fields in areas such as
mathematical logic,
category theory,
domain theory, and
algebra.
The relationship between computer science and
software engineering is a contentious issue, which is further muddied by
disputes over what the term "software engineering" means, and how computer science is defined.
David Parnas, taking a cue from the relationship between other engineering and science disciplines, has claimed that the principal focus of computer science is studying the properties of computation in general, while the principal focus of software engineering is the design of specific computations to achieve practical goals, making the two separate but complementary disciplines.
[17]
Fields of computer science
Computer science searches for concepts and
formal proofs to explain and describe computational systems of interest. As with all sciences, these theories can then be utilised to synthesize practical engineering applications, which in turn may suggest new systems to be studied and analysed. While the
ACM Computing Classification System can be used to split computer science up into different topics of fields a more descriptive break down follows:
Mathematical foundations
;
Mathematical logic
: Boolean logic and other ways of modeling logical queries; the uses and limitations of formal proof methods.
;
Number theory
: Theory of proofs and heuristics for finding proofs in the simple domain of integers. Used in
cryptography as well as a test domain in
artificial intelligence.
;
Graph theory
: Foundations for data structures and searching algorithms.
;
Type Theory
: Formal analysis of the types of data, and the use of these types to understand properties of programs — especially program safety.
;
Category Theory
: Category theory provides a means of capturing all of math and computation in a single synthesis.
;
Computational geometry
: The study of
algorithms to solve problems stated in terms of
geometry
Theory of computation
;
Automata theory
: Different logical structures for solving problems.
;
Computability theory
: What is calculable with the current models of computers. Proofs developed by
Alan Turing and others provide insight into the possibilities of what can be computed and what can not.
;
Computational complexity theory
: Fundamental bounds (especially time and storage space) on classes of computations.
;
Quantum computing theory
: Representation and manipulation of data using the quantum properties of particles and quantum mechanism.
Algorithms and data structures
;
Analysis of algorithms
: Time and space complexity of algorithms.
;
Algorithms
: Formal logical processes used for computation, and the efficiency of these processes.
;
Data structures
: The organization of and rules for the manipulation of data.
Programming languages and compilers
;
Compilers
: Ways of translating computer programs, usually from
higher level languages to
lower level ones.
;
Interpreters
: A program that takes in as input a computer program and executes it.
;
Programming languages
: Formal language paradigms for expressing algorithms, and the properties of these languages (e.g. what problems they are suited to solve).
Concurrent, parallel, and distributed systems
;
Concurrency
: The theory and practice of simultaneous computation; data safety in any multitasking or multithreaded environment.
;
Distributed computing
: Computing using multiple computing devices over a network to accomplish a common objective or task and thereby reducing the latency involved in single processor contributions for any task.
;
Parallel computing
: Computing using multiple concurrent threads of execution.
Software engineering
;
Algorithm design
: Using ideas from algorithm theory to creatively design solutions to real tasks
;
Computer programming
: The practice of using a programming language to implement algorithms
;
Formal methods
: Mathematical approaches for describing and reasoning about software designs.
;
Reverse engineering
: The application of the scientific method to the understanding of arbitrary existing software
;
Software development
: The principles and practice of designing, developing, and testing programs, as well as proper engineering practices.
System architecture
;
Computer architecture
: The design, organization, optimization and verification of a computer system, mostly about
CPUs and
Memory subsystem (and the bus connecting them).
;
Computer organization
: The implementation of computer architectures, in terms of descriptions of their specific
electrical circuitry
;
Operating systems
: Systems for managing computer programs and providing the basis of a useable system.
Communications
;
Computer audio
: Algorithms and data structures for the creation, manipulation, storage, and transmission of
digital audio recordings. Also important in
voice recognition applications.
;
Networking
: Algorithms and protocols for reliably communicating data across different shared or dedicated media, often including
error correction.
;
Cryptography
: Applies results from complexity, probability and number theory to invent and break codes.
Databases
;
Data mining
: Data mining is the extracting of the relevant data from all the sources of data
;
Relational databases
: Study of algorithms for searching and processing information in documents and databases; closely related to
information retrieval.
Artificial intelligence
;
Artificial intelligence
: The implementation and study of systems that exhibit an autonomous intelligence or behaviour of their own.
;
Artificial Life
: The study of digital organisms to learn about biological systems and evolution.
;
Automated reasoning
: Solving engines, such as used in
Prolog, which produce steps to a result given a query on a fact and rule database.
;
Computer vision
: Algorithms for identifying three dimensional objects from one or more two dimensional pictures.
;
Machine learning
: Automated creation of a set of rules and axioms based on input.
;
Natural language processing/
Computational linguistics
: Automated understanding and generation of human language
;
Robotics
: Algorithms for controlling the behavior of robots.
Visual rendering (or Computer graphics)
;
Computer graphics
: Algorithms both for generating visual images synthetically, and for integrating or altering visual and spatial information sampled from the real world.
;
Image processing
: Determining information from an image through computation.
Human-Computer Interaction
;
Human computer interaction
: The study of making computers and computations useful, usable and universally accessible to
people, including the study and design of computer interfaces through which people use computers.
Scientific computing
;
Bioinformatics
: The use of computer science to maintain, analyse, and store
biological data, and to assist in solving biological problems such as
Protein folding, function prediction and
Phylogeny.
;
Cognitive Science
: Computational modelling of real minds
;
Computational chemistry
: Computational modelling of theoretical chemistry in order to determine chemical structures and properties
;
Computational neuroscience
: Computational modelling of real brains
;
Computational physics
: Numerical simulations of large non-analytic systems
;
Numerical algorithms
: Algorithms for the numerical solution of mathematical problems such as
root-finding,
integration, the
solution of ordinary differential equations and the approximation/evaluation of
special functions.
;
Symbolic mathematics
: Manipulation and solution of expressions in symbolic form, also known as
Computer algebra.
Computer science education
Some universities teach computer science as a theoretical study of computation and algorithmic reasoning. These programs often feature the
theory of computation,
analysis of algorithms,
formal methods,
concurrency theory,
databases,
computer graphics and
systems analysis, among others. They typically also teach
computer programming, but treat it as a vessel for the support of other fields of computer science rather than a central focus of high-level study.
Other colleges and universities, as well as
secondary schools and vocational programs that teach computer science, emphasize the practice of advanced
computer programming rather than the theory of algorithms and computation in their computer science curricula. Such curricula tend to focus on those skills that are important to workers entering the software industry. The practical aspects of computer programming are often referred to as
software engineering. However, there is a lot of
disagreement over what the term "software engineering" actually means, and whether it is the same thing as programming.
: See
Peter J. Denning, ''
Great principles in computing curricula'', Technical Symposium on Computer Science Education, 2004.
See also
: ''Main list:
List of basic computer science topics''
★
Career domains in computer science
★
Computing
★
Informatics
★
List of computer science conferences
★
List of open problems in computer science
★
List of prominent pioneers in computer science
★
List of publications in computer science
★
List of software engineering topics
★
List of computer scientists
References
1. "''Computer science is the study of information''" Department of Computer and Information Science, Guttenberg Information Technologies
2. "''Computer science is the study of computation.''" Computer Science Department, College of Saint Benedict, Saint John's University
3. "''Computer Science is the study of all aspects of computer systems, from the theoretical foundations to the very practical aspects of managing large software projects.''" Massey University
4. Calculator Timeline Nigel Tout
5. Science Museum - Introduction to Babbage
6. A Selection and Adaptation From Ada's Notes found in "Ada, The Enchantress of Numbers," by Betty Alexandra Toole Ed.D. Strawberry Press, Mill Valley, CA
7. IBM Punch Cards in the U.S. Army
8. Computer Science: The Discipline, , P.J., Denning, Encyclopedia of Computer Science, 2000
9.
10. Structure and Interpretation of Computer Programs, , H., Abelson, MIT Press, 1996, ISBN 0-262-01153-0
11. [1]
12. David Kahn, The Codebreakers, 1967, ISBN 0-684-83130-9.
13. Communications of the ACM 1(4):p.6
14. Communications of the ACM 2(1):p.4
15. IEEE Computer 28(12):p.136
16. 'Computer Language', Oct 1990
17. Software Engineering Programmes are not Computer Science Programmes, , David L., Parnas, Annals of Software Engineering, 1998 , 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, ....}."
★
Association for Computing Machinery.
1998 ACM Computing Classification System. 1998.
★
IEEE Computer Society and the
Association for Computing Machinery.
Computing Curricula 2001: Computer Science. December 15, 2001.
★
Peter J. Denning. ''
Is computer science science?'', Communications of the ACM, April 2005.
External links
★
★
Computer Science Directory - search engine and directory dedicated to computer science.
★
Directory of free university lectures in Computer Science
★
Collection of Computer Science Bibliographies
★
Photographs of computer scientists (
Bertrand Meyer's gallery)
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