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 (such as computational complexity theory) relate to properties of computational problems. 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.
History
Main article: History of computer science
The history of computer science predates the invention of the modern digital computer by many years. Machines for calculating fixed numerical tasks have existed since antiquity, such as the abacus. 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.
Prior to the 1920s, the term computer referred to a human clerk who performed calculations. Early researchers in what came to be called computer science, such as Kurt Gödel, Alonzo Church, and Alan Turing, were interested in the question of computability: what things can be computed by a human clerk who simply follows a list of instructions with paper and pencil, for as long as necessary, and without ingenuity or insight?[citation needed] Part of the motivation for this work was the desire to develop computing machines that could automate the often tedious and error-prone work of a human computer. Their key insight was to construct universal computing systems capable (in theory) of performing all possible computable tasks, and thus generalising all previous dedicated-task machines into the single notion of the universal computer. The creation of the concept of a universal computer marked the birth of modern computer science.[citation needed]
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
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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:
A formal definition of computation and computability, and proof that there are computationally unsolvable and intractable problems[8].
The concept of a programming language, a tool for the precise expression of methodological information at various levels of abstraction[9]
The theory and practice of compilers for translating between programming languages[citation needed]
Practical applications: the PC, the internet, search engines, scientific computing[citation needed]
Relationship with other fields
Main article: Diversity of computer science
Wikiquote has a collection of quotations related to:
Edsger DijkstraDespite 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[10] 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. Some people believe that software engineering is a subset of computer science[citation needed]. Others, taking a cue from the relationship between other engineering and science disciplines, believe that the principle focus of computer science is studying the properties of computation in general, while the principle focus of software engineering is the design of specific computations to achieve practical goals, making them different disciplines. This view is promulgated by (among others) David Parnas[11]. Still others maintain that software cannot be engineered at all[citation needed].
Fields of computer science
Computer science searches for concepts and proofs to explain and describe computational systems of interest. It is a science because given a system of interest it performs /analysis/ and seeks general principals to explain that system[citation needed]. 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.
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
Main article: 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. Based heavily on mathematical logic.
Programming languages
Formal language paradigms for expressing algorithms, and the properties of these languages (EG: 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 there by reducing the latency involved in single processor contributions for any task.
Parallel computing
Computing using multiple concurrent threads of execution.
Software engineering
Formal methods
Mathematical approaches for describing and reasoning about software designs.
Software engineering
The principles and practice of designing, developing, and testing programs, as well as proper engineering practices.
Reverse engineering
The application of the scientific method to the understanding of arbitrary existing software
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
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).
Operating systems
Systems for managing computer programs and providing the basis of a useable system.
Communications
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
Relational databases
Data mining
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.
Robotics
Algorithms for controlling the behavior of robots.
Computer vision
Algorithms for identifying three dimensional objects from a two dimensional picture.
Machine learning
Automated creation of a set of rules and axioms based on input.
Soft computing
Main article: 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
The study and design of computer interfaces that people use.
Scientific computing
Computational physics
Numerical simulations of large non-analytic systems
Computational chemistry
Bioinformatics
The use of computer science to maintain, analyse, store biological data and to assist in solving biological problems such as protein folding.
Computational neuroscience
Computational modelling of real brains
Cognitive Science
Computational modelling of real minds
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
Computing
Informatics
List of basic computer science topics
List of computer science conferences
List of open problems in computer science
List of publications in computer science
List of prominent pioneers in computer science
List of software engineering topics
Career domains in computer science