Computer science

Computer science, or computing science, is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. 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
The history of computer science predates the invention of the modern digital computer. 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? 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.

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.

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:
 * A formal definition of computation and computability, and proof that there are computationally unsolvable and intractable problems.
 * The concept of a programming language, a tool for the precise expression of methodological information at various levels of abstraction
 * Revolutionary technologies such as general-purpose computers, the Internet, digital signatures, electronic commerce, and search engines;
 * The enabling of new types of scientific research, such as computational physics and computational chemistry.

Relationship with other fields
Despite its name, computer science rarely involves 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 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. 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. 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. Still others maintain that software cannot be engineered at all.

Mathematical foundations

 * Cryptography
 * Algorithms for protecting private data, including encryption.


 * Graph theory
 * Foundations for data structures and searching algorithms.


 * Mathematical logic
 * Boolean logic and other ways of modeling logical queries.


 * 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.

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.


 * Genetic algorithms
 * A genetic algorithm is a search technique to find approximate solutions to optimization and search problems.

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).

Databases

 * Data mining
 * Study of algorithms for searching and processing information in documents and databases; closely related to information retrieval.

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.


 * Networking
 * Algorithms and protocols for reliably communicating data across different shared or dedicated media, often including error correction.


 * Parallel computing
 * Computing using multiple concurrent threads of execution.

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.



Software engineering

 * Computer programming
 * The act of writing algorithms in a programming language.


 * 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.

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.

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

 * Bioinformatics
 * The use of computer science to maintain, analyse, store biological data and to assist in solving biological problems such as protein folding.

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.