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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 (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
1950s, 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
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
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[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 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 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 ¡ª 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.
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
Game theory
Recently game theory has drawn attention
from computer scientists because of its use in artificial intelligence and
cybernetics.
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
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.
Computational physics
Numerical simulations of large
non-analytic systems
Computational chemistry
Computational modelling of theoretical
chemistry in order to determine chemical structures and properties
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.
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
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.
^ Nigel Tout (2006).
Calculator Timeline. Vintage Calculator Web Museum. Retrieved
on 2006-09-18.
5.
^
Science Museum - Introduction to Babbage. Retrieved on 2006-09-24.
6.
^
IBM Punch Cards in the U.S. Army. Retrieved on 2006-09-24.
7.
^
Denning, P.J. (2000). "Computer
science:the discipline". Encyclopedia of Computer
Science.
8.
^ Constable, R.L. (March 2000). "Computer
Science: Achievements and Challenges circa 2000".
9.
^
Abelson (1996) Computer Science: Achievements and Challenges
circa 2000, 2nd Ed., MIT Press. ISBN
0-262-01153-0.{{ #if: 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 ¡ª 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. |
¡°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
¡ª 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.?
}}
10.
^ Computer Language, Oct 1990
11.
^
Parnas, David L. (1998). "Software
Engineering Programmes are not Computer Science Programmes".
Annals of Software Engineering
6: 19¨C37., p. 19: "Rather than treat software
engineering as a subfield ofcomputer science, I treat it as an element of
the set, {Civil Engineering, Mechanical Engineering,Chemical Engineering,
Electrical Engineering,....}."
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