# Computer science
Computer science is the study of computation, information, and automation.
Computer science spans theoretical disciplines (such as algorithms, theory of
computation, and information theory) to applied disciplines (including the
design and implementation of hardware and software).
Algorithms and data structures are central to computer science. The theory of
computation concerns abstract models of computation and general classes of
problems that can be solved using them. The fields of cryptography and computer
security involve studying the means for secure communication and preventing
security vulnerabilities. Computer graphics and computational geometry address
the generation of images. Programming language theory considers different ways
to describe computational processes, and database theory concerns the management
of repositories of data. Human–computer interaction investigates the interfaces
through which humans and computers interact, and software engineering focuses on
the design and principles behind developing software. Areas such as operating
systems, networks and embedded systems investigate the principles and design
behind complex systems. Computer architecture describes the construction of
computer components and computer-operated equipment. Artificial intelligence and
machine learning aim to synthesize goal-orientated processes such as
problem-solving, decision-making, environmental adaptation, planning and
learning found in humans and animals. Within artificial intelligence, computer
vision aims to understand and process image and video data, while natural
language processing aims to understand and process textual and linguistic data.
The fundamental concern of computer science is determining what can and cannot
be automated. The Turing Award is generally recognized as the highest
distinction in computer science.
## History
The earliest 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, aiding in
computations such as multiplication and division. Algorithms for performing
computations have existed since antiquity, even before the development of
sophisticated computing equipment.
Wilhelm Schickard designed and constructed the first working mechanical
calculator in 1623. In 1673, Gottfried Leibniz demonstrated a digital mechanical
calculator, called the Stepped Reckoner. Leibniz may be considered the first
computer scientist and information theorist, because of various reasons,
including the fact that he documented the binary number system. In 1820, Thomas
de Colmar launched the mechanical calculator industry[note 1] when he invented
his simplified arithmometer, the first calculating machine strong enough and
reliable enough to be used daily in an office environment. Charles Babbage
started the design of the first automatic mechanical calculator, his Difference
Engine, in 1822, which eventually gave him the idea of the first programmable
mechanical calculator, his Analytical Engine. He started developing this machine
in 1834, and "in less than two years, he had sketched out many of the salient
features of the modern computer". "A crucial step was the adoption of a punched
card system derived from the Jacquard loom" making it infinitely
programmable.[note 2] In 1843, during the translation of a French article on the
Analytical Engine, Ada Lovelace wrote, in one of the many notes she included, an
algorithm to compute the Bernoulli numbers, which is considered to be the first
published algorithm ever specifically tailored for implementation on a computer.
Around 1885, Herman Hollerith invented the tabulator, which used punched cards
to process statistical information; eventually his company became part of IBM.
Following Babbage, although unaware of his earlier work, Percy Ludgate in 1909
published the 2nd of the only two designs for mechanical analytical engines in
history. In 1914, the Spanish engineer Leonardo Torres Quevedo published his
Essays on Automatics, and designed, inspired by Babbage, a theoretical
electromechanical calculating machine which was to be controlled by a read-only
program. The paper also introduced the idea of floating-point arithmetic. In
1920, to celebrate the 100th anniversary of the invention of the arithmometer,
Torres presented in Paris the Electromechanical Arithmometer, a prototype that
demonstrated the feasibility of an electromechanical analytical engine, on which
commands could be typed and the results printed automatically. In 1937, one
hundred years after Babbage's impossible dream, Howard Aiken convinced IBM,
which was making all kinds of punched card equipment and was also in the
calculator business to develop his giant programmable calculator, the
ASCC/Harvard Mark I, based on Babbage's Analytical Engine, which itself used
cards and a central computing unit. When the machine was finished, some hailed
it as "Babbage's dream come true".
During the 1940s, with the development of new and more powerful computing
machines such as the Atanasoff–Berry computer and ENIAC, 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. In
1945, IBM founded the Watson Scientific Computing Laboratory at Columbia
University in New York City. The renovated fraternity house on Manhattan's West
Side was IBM's first laboratory devoted to pure science. The lab is the
forerunner of IBM's Research Division, which today operates research facilities
around the world. Ultimately, the close relationship between IBM and Columbia
University was instrumental in the emergence of a new scientific discipline,
with Columbia offering one of the first academic-credit courses in computer
science in 1946. Computer science began to be established as a distinct academic
discipline in the 1950s and early 1960s. The world's first computer science
degree program, the Cambridge Diploma in Computer Science, began at the
University of Cambridge Computer Laboratory in 1953. The first computer science
department in the United States was formed at Purdue University in 1962. Since
practical computers became available, many applications of computing have become
distinct areas of study in their own rights.
## Etymology and scope
Although first proposed in 1956, the term "computer science" appears in a 1959
article in Communications of the ACM, in which Louis Fein argues for the
creation of a Graduate School in Computer Sciences analogous to the creation of
Harvard Business School in 1921. Louis justifies the name by arguing that, like
management science, the subject is applied and interdisciplinary in nature,
while having the characteristics typical of an academic discipline. His efforts,
and those of others such as numerical analyst George Forsythe, were rewarded:
universities went on to create such departments, starting with Purdue in 1962.
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. Certain departments of major universities prefer the term
computing science, to emphasize precisely that difference. 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. An alternative term, also proposed by Naur, is data
science; this is now used for a multi-disciplinary field of data analysis,
including statistics and databases.
In the early days of computing, a number of terms for the practitioners of the
field of computing were suggested (albeit facetiously) in the Communications of
the ACM—turingineer, turologist, flow-charts-man, applied meta-mathematician,
and applied epistemologist. Three months later in the same journal, comptologist
was suggested, followed next year by hypologist. The term computics has also
been suggested. In Europe, terms derived from contracted translations of the
expression "automatic information" (e.g. "informazione automatica" in Italian)
or "information and mathematics" are often used, e.g. informatique (French),
Informatik (German), informatica (Italian, Dutch), informática (Spanish,
Portuguese), informatika (Slavic languages and Hungarian) or pliroforiki
(πληροφορική, which means informatics) in Greek. Similar words have also been
adopted in the UK (as in the School of Informatics, University of Edinburgh).
"In the U.S., however, informatics is linked with applied computing, or
computing in the context of another domain."
A folkloric quotation, often attributed to—but almost certainly not first
formulated by—Edsger Dijkstra, states that "computer science is no more about
computers than astronomy is about telescopes."[note 3] 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. However, there has been exchange of ideas
between the various computer-related disciplines. Computer science research also
often intersects other disciplines, such as cognitive science, linguistics,
mathematics, physics, biology, Earth science, statistics, philosophy, and logic.
Computer science is considered by some to have a much closer relationship with
mathematics than many scientific disciplines, with some observers saying that
computing is a mathematical science. Early computer science was strongly
influenced by the work of mathematicians such as Kurt Gödel, Alan Turing, John
von Neumann, Rózsa Péter and Alonzo Church 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.
The academic, political, and funding aspects of computer science tend to depend
on whether a department is formed with a mathematical emphasis or with an
engineering emphasis. Computer science departments with a mathematics emphasis
and with a numerical orientation consider alignment with computational science.
Both types of departments tend to make efforts to bridge the field educationally
if not across all research.
## Philosophy
### Epistemology of computer science
Despite the word science in its name, there is debate over whether or not
computer science is a discipline of science, mathematics, or engineering. Allen
Newell and Herbert A. Simon argued in 1975,
> Computer science is an empirical discipline. We would have called it an
> experimental science, but like astronomy, economics, and geology, some of its
> unique forms of observation and experience do not fit a narrow stereotype of
> the experimental method. Nonetheless, they are experiments. Each new machine
> that is built is an experiment. Actually constructing the machine poses a
> question to nature; and we listen for the answer by observing the machine in
> operation and analyzing it by all analytical and measurement means available.
It has since been argued that computer science can be classified as an empirical
science since it makes use of empirical testing to evaluate the correctness of
programs, but a problem remains in defining the laws and theorems of computer
science (if any exist) and defining the nature of experiments in computer
science. Proponents of classifying computer science as an engineering discipline
argue that the reliability of computational systems is investigated in the same
way as bridges in civil engineering and airplanes in aerospace engineering. They
also argue that while empirical sciences observe what presently exists, computer
science observes what is possible to exist and while scientists discover laws
from observation, no proper laws have been found in computer science and it is
instead concerned with creating phenomena.
Proponents of classifying computer science as a mathematical discipline argue
that computer programs are physical realizations of mathematical entities and
programs that can be deductively reasoned through mathematical formal methods.
Computer scientists Edsger W. Dijkstra and Tony Hoare regard instructions for
computer programs as mathematical sentences and interpret formal semantics for
programming languages as mathematical axiomatic systems.
### Paradigms of computer science
A number of computer scientists have argued for the distinction of three
separate paradigms in computer science. Peter Wegner argued that those paradigms
are science, technology, and mathematics. Peter Denning's working group argued
that they are theory, abstraction (modeling), and design. Amnon H. Eden
described them as the "rationalist paradigm" (which treats computer science as a
branch of mathematics, which is prevalent in theoretical computer science, and
mainly employs deductive reasoning), the "technocratic paradigm" (which might be
found in engineering approaches, most prominently in software engineering), and
the "scientific paradigm" (which approaches computer-related artifacts from the
empirical perspective of natural sciences, identifiable in some branches of
artificial intelligence). Computer science focuses on methods involved in
design, specification, programming, verification, implementation and testing of
human-made computing systems.
## Fields
As a discipline, computer science spans a range of topics from theoretical
studies of algorithms and the limits of computation to the practical issues of
implementing computing systems in hardware and software. CSAB, formerly called
Computing Sciences Accreditation Board—which is made up of representatives of
the Association for Computing Machinery (ACM), and the IEEE Computer Society
(IEEE CS)—identifies four areas that it considers crucial to the discipline of
computer science: theory of computation, algorithms and data structures,
programming methodology and languages, and computer elements and architecture.
In addition to these four areas, CSAB also identifies fields such as software
engineering, artificial intelligence, computer networking and communication,
database systems, parallel computation, distributed computation, human–computer
interaction, computer graphics, operating systems, and numerical and symbolic
computation as being important areas of computer science.
### Theoretical computer science
Theoretical computer science is mathematical and abstract in spirit, but it
derives its motivation from practical and everyday computation. It aims to
understand the nature of computation and, as a consequence of this
understanding, provide more efficient methodologies.
#### Theory of computation
According to Peter Denning, the fundamental question underlying computer science
is, "What can be automated?" Theory of computation is focused on answering
fundamental questions about what can be computed and what amount of resources
are required to perform those computations. In an effort to answer the first
question, computability theory examines which computational problems are
solvable on various theoretical models of computation. The second question is
addressed by computational complexity theory, which studies the time and space
costs associated with different approaches to solving a multitude of
computational problems.
The famous P = NP? problem, one of the Millennium Prize Problems, is an open
problem in the theory of computation.
#### Information and coding theory
Information theory, closely related to probability and statistics, is related to
the quantification of information. This was developed by Claude Shannon to find
fundamental limits on signal processing operations such as compressing data and
on reliably storing and communicating data. Coding theory is the study of the
properties of codes (systems for converting information from one form to
another) and their fitness for a specific application. Codes are used for data
compression, cryptography, error detection and correction, and more recently
also for network coding. Codes are studied for the purpose of designing
efficient and reliable data transmission methods.
#### Data structures and algorithms
Data structures and algorithms are the studies of commonly used computational
methods and their computational efficiency.
#### Programming language theory and formal methods
Programming language theory is a branch of computer science that deals with the
design, implementation, analysis, characterization, and classification of
programming languages and their individual features. It falls within the
discipline of computer science, both depending on and affecting mathematics,
software engineering, and linguistics. It is an active research area, with
numerous dedicated academic journals.
Formal methods are a particular kind of mathematically based technique for the
specification, development and verification of software and hardware systems.
The use of formal methods for software and hardware design is motivated by the
expectation that, as in other engineering disciplines, performing appropriate
mathematical analysis can contribute to the reliability and robustness of a
design. They form an important theoretical underpinning for software
engineering, especially where safety or security is involved. Formal methods are
a useful adjunct to software testing since they help avoid errors and can also
give a framework for testing. For industrial use, tool support is required.
However, the high cost of using formal methods means that they are usually only
used in the development of high-integrity and life-critical systems, where
safety or security is of utmost importance. Formal methods are best described as
the application of a fairly broad variety of theoretical computer science
fundamentals, in particular logic calculi, formal languages, automata theory,
and program semantics, but also type systems and algebraic data types to
problems in software and hardware specification and verification.
### Applied computer science
#### Computer graphics and visualization
Computer graphics is the study of digital visual contents and involves the
synthesis and manipulation of image data. The study is connected to many other
fields in computer science, including computer vision, image processing, and
computational geometry, and is heavily applied in the fields of special effects
and video games.
#### Image and sound processing
Information can take the form of images, sound, video or other multimedia. Bits
of information can be streamed via signals. Its processing is the central notion
of informatics, the European view on computing, which studies information
processing algorithms independently of the type of information carrier – whether
it is electrical, mechanical or biological. This field plays important role in
information theory, telecommunications, information engineering and has
applications in medical image computing and speech synthesis, among others. What
is the lower bound on the complexity of fast Fourier transform algorithms? is
one of the unsolved problems in theoretical computer science.
#### Computational science, finance and engineering
Scientific computing (or computational science) is the field of study concerned
with constructing mathematical models and quantitative analysis techniques and
using computers to analyze and solve scientific problems. A major usage of
scientific computing is simulation of various processes, including computational
fluid dynamics, physical, electrical, and electronic systems and circuits, as
well as societies and social situations (notably war games) along with their
habitats, among many others. Modern computers enable optimization of such
designs as complete aircraft. Notable in electrical and electronic circuit
design are SPICE, as well as software for physical realization of new (or
modified) designs. The latter includes essential design software for integrated
circuits.
#### Human–computer interaction
Human–computer interaction (HCI) is the field of study and research concerned
with the design and use of computer systems, mainly based on the analysis of the
interaction between humans and computer interfaces. HCI has several subfields
that focus on the relationship between emotions, social behavior and brain
activity with computers.
#### Software engineering
Software engineering is the study of designing, implementing, and modifying the
software in order to ensure it is of high quality, affordable, maintainable, and
fast to build. It is a systematic approach to software design, involving the
application of engineering practices to software. Software engineering deals
with the organizing and analyzing of software—it does not just deal with the
creation or manufacture of new software, but its internal arrangement and
maintenance. For example software testing, systems engineering, technical debt
and software development processes.
#### Artificial intelligence
Artificial intelligence (AI) aims to or is required to synthesize
goal-orientated processes such as problem-solving, decision-making,
environmental adaptation, learning, and communication found in humans and
animals. From its origins in cybernetics and in the Dartmouth Conference (1956),
artificial intelligence research has been necessarily cross-disciplinary,
drawing on areas of expertise such as applied mathematics, symbolic logic,
semiotics, electrical engineering, philosophy of mind, neurophysiology, and
social intelligence. AI is associated in the popular mind with robotic
development, but the main field of practical application has been as an embedded
component in areas of software development, which require computational
understanding. The starting point in the late 1940s was Alan Turing's question
"Can computers think?", and the question remains effectively unanswered,
although the Turing test is still used to assess computer output on the scale of
human intelligence. But the automation of evaluative and predictive tasks has
been increasingly successful as a substitute for human monitoring and
intervention in domains of computer application involving complex real-world
data.
### Computer systems
#### Computer architecture and microarchitecture
Computer architecture, or digital computer organization, is the conceptual
design and fundamental operational structure of a computer system. It focuses
largely on the way by which the central processing unit performs internally and
accesses addresses in memory. Computer engineers study computational logic and
design of computer hardware, from individual processor components,
microcontrollers, personal computers to supercomputers and embedded systems. The
term "architecture" in computer literature can be traced to the work of Lyle R.
Johnson and Frederick P. Brooks Jr., members of the Machine Organization
department in IBM's main research center in 1959.
#### Concurrent, parallel and distributed computing
Concurrency is a property of systems in which several computations are executing
simultaneously, and potentially interacting with each other. A number of
mathematical models have been developed for general concurrent computation
including Petri nets, process calculi and the parallel random access machine
model. When multiple computers are connected in a network while using
concurrency, this is known as a distributed system. Computers within that
distributed system have their own private memory, and information can be
exchanged to achieve common goals.
#### Computer networks
This branch of computer science aims to manage networks between computers
worldwide.
#### Computer security and cryptography
Computer security is a branch of computer technology with the objective of
protecting information from unauthorized access, disruption, or modification
while maintaining the accessibility and usability of the system for its intended
users.
Historical cryptography is the art of writing and deciphering secret messages.
Modern cryptography is the scientific study of problems relating to distributed
computations that can be attacked. Technologies studied in modern cryptography
include symmetric and asymmetric encryption, digital signatures, cryptographic
hash functions, key-agreement protocols, blockchain, zero-knowledge proofs, and
garbled circuits.
#### Databases and data mining
A database is intended to organize, store, and retrieve large amounts of data
easily. Digital databases are managed using database management systems to
store, create, maintain, and search data, through database models and query
languages. Data mining is a process of discovering patterns in large data sets.
## Discoveries
The philosopher of computing Bill Rapaport noted three Great Insights of
Computer Science:
- Gottfried Wilhelm Leibniz's, George Boole's, Alan Turing's, Claude Shannon's,
and Samuel Morse's insight: there are only two objects that a computer has to
deal with in order to represent "anything".[note 4]
> All the information about any computable problem can be represented using
> only 0 and 1 (or any other bistable pair that can flip-flop between two
> easily distinguishable states, such as "on/off", "magnetized/de-magnetized",
> "high-voltage/low-voltage", etc.).
- Alan Turing's insight: there are only five actions that a computer has to
perform in order to do "anything".
Every algorithm can be expressed in a language for a computer consisting of
only five basic instructions:
- move left one location;
- move right one location;
- read symbol at current location;
- print 0 at current location;
- print 1 at current location.
- Corrado Böhm and Giuseppe Jacopini's insight: there are only three ways of
combining these actions (into more complex ones) that are needed in order for
a computer to do "anything".
Only three rules are needed to combine any set of basic instructions into more
complex ones:
- sequence: first do this, then do that;
- selection: IF such-and-such is the case, THEN do this, ELSE do that;
- repetition: WHILE such-and-such is the case, DO this. The three rules of
Boehm's and Jacopini's insight can be further simplified with the use of
goto (which means it is more elementary than structured programming).
## Programming paradigms
Programming languages can be used to accomplish different tasks in different
ways. Common programming paradigms include:
- Functional programming, a style of building the structure and elements of
computer programs that treats computation as the evaluation of mathematical
functions and avoids state and mutable data. It is a declarative programming
paradigm, which means programming is done with expressions or declarations
instead of statements.
- Imperative programming, a programming paradigm that uses statements that
change a program's state. In much the same way that the imperative mood in
natural languages expresses commands, an imperative program consists of
commands for the computer to perform. Imperative programming focuses on
describing how a program operates.
- Object-oriented programming, a programming paradigm based on the concept of
"objects", which may contain data, in the form of fields, often known as
attributes; and code, in the form of procedures, often known as methods. A
feature of objects is that an object's procedures can access and often modify
the data fields of the object with which they are associated. Thus
object-oriented computer programs are made out of objects that interact with
one another.
- Service-oriented programming, a programming paradigm that uses "services" as
the unit of computer work, to design and implement integrated business
applications and mission critical software programs.
Many languages offer support for multiple paradigms, making the distinction more
a matter of style than of technical capabilities.
## Research
Conferences are important events for computer science research. During these
conferences, researchers from the public and private sectors present their
recent work and meet. Unlike in most other academic fields, in computer science,
the prestige of conference papers is greater than that of journal publications.
One proposed explanation for this is the quick development of this relatively
new field requires rapid review and distribution of results, a task better
handled by conferences than by journals.