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Computer science (or computing science) is the study and the science 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
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The early 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. 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.
Many initially believed it impossible that "computers themselves
could actually be a scientific field of study" (Levy 1984, p. 11),
though it was in the "late fifties" (Levy 1984, p.11) that it gradually
became accepted among the greater academic population. It is the now
well-known IBM brand that formed part of the computer science
revolution during this time. 'IBM' (short for International Business
Machines) released the IBM 704 and later the IBM 709 computers, which
were widely used during the exploration period of such devices. "Still,
working with the IBM [computer] was frustrating...if you had misplaced
as much as one letter in one instruction, the program would crash, and
you would have to start the whole process over again" (Levy 1984,
p.13). During the late 1950s, the computer science discipline was very
much in its developmental stages, and such issues were commonplace.
Time has seen significant improvements in the useability and
effectiveness of computer science technology. Modern society has seen a
significant shift from computers being used solely by experts or
professionals to a more widespread user base. By the 1990s, computers
became accepted as being the norm within everyday life. During this
time data entry was a primary component of the use of computers, many
preferring to streamline their business practices through the use of a
computer. This also gave the additional benefit of removing the need of
large amounts of documentation and file records which consumed
much-needed physical space within offices.
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
Despite its name, a significant amount 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 to use the term was 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 and practitioners of the field of computing
were suggested in the Communications are of the ACM—turingineer, turologist, flow-charts-man, applied meta-mathematician, and applied epistemologist.[14] Three months later in the same journal, comptologist was suggested, followed next year by hypologist.[15] Recently the term computics has been suggested.[16] Informatik was a term used in Europe with more frequency.
The renowned computer scientist Edsger Dijkstra
stated, "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[17]
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 cognitive science, economics, mathematics, 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.[18]
The academic, political, and funding aspects of computer science
tend to have roots as to whether a department in the U.S. formed with
either a mathematical emphasis or an engineering emphasis. In general,
electrical engineering-based computer science departments have tended
to succeed as computer science and/or engineering departments.[citation needed] Computer science departments with a mathematics emphasis and with a numerical orientation consider alignment computational science. Both types of departments tend to make efforts to bridge the field educationally if not across all research.
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 breakdown 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.
- Numerical analysis
- Foundations for algorithms in discrete mathematics, as well as the
study of the limitations of floating point computation, including round-off errors.
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 cannot.
- Computational complexity theory
- Fundamental bounds (especially time and storage space) on classes
of computations; in practice, study of which problems a computer can
solve with reasonable resources (while computability theory studies
which problems can be solved at all).
- 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.
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 subsystems (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 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 extraction of relevant data from all sources of data.
- Relational databases
- Study of algorithms for searching and processing information in documents and databases; closely related to information retrieval.
- OLAP
- Online Analytical Processing, or OLAP, is an approach to quickly
provide answers to analytical queries that are multi-dimensional in
nature. OLAP is part of the broader category business intelligence, which also encompasses relational reporting and data mining.
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.
Didactics of computer science/informatics
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The subfield didactics of computer science focuses on cognitive
approaches of developing competencies of computer science and specific
strategies for analysis, design, implementation and evaluation of
excellent lessons in computer science.
Computer science education
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