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Computer science
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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 with solutions. A further subfield, human-computer interaction, focuses on the challenges in making computers and computations useful, usable and universally accessible to people.
Contents
- 1 History
- 2 Major achievements
- 3 Relationship with other fields
- 4 Fields of computer science
- 4.1 Mathematical foundations
- 4.2 Theory of computation
- 4.3 Algorithms and data structures
- 4.4 Programming languages and compilers
- 4.5 Concurrent, parallel, and distributed systems
- 4.6 Software engineering
- 4.7 Computer architecture
- 4.8 Communications
- 4.9 Databases
- 4.10 Artificial intelligence
- 4.11 Soft computing
- 4.12 Computer graphics
- 4.13 Human-Computer Interaction
- 4.14 Scientific computing
- 5 Computer science education
- 6 See also
- 7 References
- 8 External links
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History
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The history of computer science predates the invention of the modern digital computer by many years. 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[5], and around 1900 the IBM corporation sold punch-card machines[6]. 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.
[7] Since practical computers became available, many applications of computing have become distinct areas of study in their own right.
Major achievements
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
- Applications outside of computing
Relationship with other fields
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- redirect Template:Wikiquote
Despite its name, much of computer science does not involve the study of computers themselves. 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[12] 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[7]. 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.
[13].
Fields of computer science
Computer science searches for concepts and 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.
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 may be computed and what may not.
- Computational complexity theory
- Fundamental bounds (especially time and storage space) on classes of computations.
- Quantum computing theory
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.
- 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 engineering
- The principles and practice of designing, developing, and testing programs, as well as proper engineering practices.
Computer 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
- 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.
- 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.
Soft computing
-
A collective term for techniques used in solving specific problems. See the main article.
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, 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
- Numerical solution of mathematical problems such as root-finding, integration, the solution of ordinary differential equations and the approximation 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
References
- ^ "Computer science is the study of information" Department of Computer and Information Science, Guttenberg Information Technologies
- ^ "Computer science is the study of computation." Computer Science Department, College of Saint Benedict, Saint John's University
- ^ "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
- ^ Nigel Tout (2006). Calculator Timeline. Vintage Calculator Web Museum. Retrieved on 2006-09-18.
- ^ Science Museum - Introduction to Babbage. Retrieved on 2006-09-24.
- ^ IBM Punch Cards in the U.S. Army. Retrieved on 2006-09-24.
- ^ a b Denning, P.J. (2000). "Computer Science: The Discipline". Encyclopedia of Computer Science.
- ^ a b David Kahn, The Codebreakers, 1967, ISBN 0-684-83130-9.
- ^ Constable, R.L. (March 2000). "Computer Science: Achievements and Challenges circa 2000".
- ^ Abelson, H.; G.J. Sussman with J.Sussman (1996). Structure and Interpretation of Computer Programs, 2nd Ed., MIT Press. ISBN 0-262-01153-0. “The computer revolution is a revolution in the way we think and in the way we express what we think. The essence of this change is the emergence of what might best be called procedural epistemology — the study of the structure of knowledge from an imperative point of view, as opposed to the more declarative point of view taken by classical mathematical subjects.”
- ^ a b [1]
- ^ Computer Language, Oct 1990
- ^ Parnas, David L. (1998). "Software Engineering Programmes are not Computer Science Programmes". Annals of Software Engineering 6: 19–37. , 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,....}."
