Computer Science
(abbreviated CS or CompSci) is the scientific and practical approach to
computation and its applications. It is the systematic study of the
feasibility, structure, expression, and mechanization of the methodical
processes (or algorithms) that underlie the acquisition, representation,
processing, storage, communication of, and access to information, whether such
information is encoded in bits and bytes in a computer memory or transcribed
engines and protein structures in a human cell.[1] A computer scientist
specializes in the theory of computation and the design of computational
systems.[2]
Its subfields can be
divided into a variety of theoretical and practical disciplines. Some fields,
such as computational complexity theory (which explores the fundamental
properties of Computational and intractable problems), are highly abstract,
while fields such as computer graphics emphasize real-world visual
applications. Still other fields focus on the challenges in implementing
computation. For example, programming language theory considers various
approaches to the description of computation, whilst the study of computer
programming itself investigates various aspects of the use of programming
language and complex systems. Human-computer interaction considers the
challenges in making computers and computations useful, usable, and universally
accessible to humans.
large capital lambda Plot of a quicksort algorithm
Utah teapot
representing computer graphics Microsoft
Tastenmaus mouse representing human-computer interaction
Computer science deals
with the theoretical foundations of information and computation, together with
practical techniques for the implementation and application of these
foundations
Contents [hide]
1 History
1.1 Major achievements
2 Philosophy
2.1 Name of the field
3 Areas of computer
science
3.1 Theoretical
computer science
3.1.1 Theory of
computation
3.1.2 Information and
coding theory
3.1.3 Algorithms and
data structures
3.1.4 Programming
language theory
3.1.5 Formal methods
3.2 Applied computer
science
3.2.1 Artificial
intelligence
3.2.2 Computer
architecture and engineering
3.2.3 Computer
graphics and visualization
3.2.4 Computer
security and cryptography
3.2.5 Computational
science
3.2.6 Computer
Networks
3.2.7 Concurrent,
parallel and distributed systems
3.2.8 Databases
3.2.9 Health
Informatics
3.2.10 Information
science
3.2.11 Software
engineering
4 The great insights
of computer science
5 Academia
5.1 Conferences
5.2 Journals
6 Education
7 See also
8 Notes
9 References
10 Further reading
11 External links
History[edit]
Main article: History
of computer science
Charles Babbage is
credited with inventing the first mechanical computer.
Ada Lovelace is
credited with writing the first algorithm intended for processing on a
computer.
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.
Blaise Pascal designed
and constructed the first working mechanical calculator, Pascal's calculator,
in 1642.[3] In 1673 Gottfried Leibniz demonstrated a digital mechanical
calculator, called the 'stepped reckoner'.[4] He may be considered the first
computer scientist and information theorist, for, among other reasons documenting
the binary number system. In 1820, Thomas de Colmar launched the mechanical
calculator industry[5] when he released his simplified arithmometer, which was
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.[6] 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"[7] making it infinitely programmable.[8]
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 computer
program.[9] Around 1885, Herman Hollerith invented the tabulator which used
punched cards to process statistical information; eventually his company became
part of IBM. 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[10] 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".[11]
During the 1940s, as
new and more powerful computing machines were developed, the term computer came
to refer to the machines rather than their human predecessors.[12] 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 1950s and early 1960s.[13][14] 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
degree program in the United States was formed at Purdue University in 1962.[15]
Since practical computers became available, many applications of computing have
become distinct areas of study in their own right.
Although many
initially believed it was impossible that computers themselves could actually
be a scientific field of study, in the late fifties it gradually became
accepted among the greater academic population.[16] 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[17] and
later the IBM 709[18] 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".[16] 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 usability and effectiveness of computing
technology. Modern society has seen a significant shift in the users of
computer technology, from usage only by experts and professionals, to a
near-ubiquitous user base. Initially, computers were quite costly, and some
degree of human aid was needed for efficient use - in part from professional
computer operators. As computer adoption became more widespread and affordable,
less human assistance was needed for common usage.
Major
achievements[edit]
The German military
used the Enigma machine (shown here) during World War II for communication they
thought to be secret. The large-scale decryption of Enigma traffic at Bletchley
Park was an important factor that contributed to Allied victory in WWII.[19]
Despite its short
history as a formal academic discipline, computer science has made a number of
fundamental contributions to science and society - in fact, along with
electronics, it is a founding science of the current epoch of human history
called the Information Age and a driver of the Information Revolution, seen as
the third major leap in human technological progress after the Industrial
Revolution (1750-1850 CE) and the Agricultural Revolution (8000-5000 BCE).
These contributions
include:
The start of the
"digital revolution," which includes the current Information Age and
the Internet.[20]
A formal definition of
computation and computability, and proof that there are computationally
unsolvable and intractable problems.[21]
The concept of a
programming language, a tool for the precise expression of methodological
information at various levels of abstraction.[22]
In cryptography,
breaking the Enigma code was an important factor contributing to the Allied
victory in World War II.[19]
Scientific computing
enabled practical evaluation of processes and situations of great complexity,
as well as experimentation entirely by software. It also enabled advanced study
of the mind, and mapping of the human genome became possible with the Human
Genome Project.[20] Distributed computing projects such as Folding@home explore
protein folding.
Algorithmic trading
has increased the efficiency and liquidity of financial markets by using
artificial intelligence, machine learning, and other statistical and numerical
techniques on a large scale.[23] High frequency algorithmic trading can also
exacerbate volatility.[24]
Computer graphics and
computer-generated imagery have become ubiquitous in modern entertainment,
particularly in television, cinema, advertising, animation and video games.
Even films that feature no explicit CGI are usually "filmed" now on
digital cameras, or edited or postprocessed using a digital video
editor.[citation needed]
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.[citation needed]
Artificial
intelligence is becoming increasingly important as it gets more efficient and
complex. There are many applications of the AI, some of which can be seen at
home, such as robotic vacuum cleaners. It is also present in video games and on
the modern battlefield in drones, anti-missile systems, and squad support
robots.
Philosophy[edit]
Main article:
Philosophy 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.[25] Peter Denning's working group argued that they
are theory, abstraction (modeling), and design.[26] 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).[27]
Name of the
field[edit]
The term
"computer science" appears in a 1959 article in Communications of the
ACM,[28] 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,[29] justifying 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.[30] His efforts, and those
of others such as numerical analyst George Forsythe, were rewarded:
universities went on to create such programs, starting with Purdue in 1962.[31]
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.[32] Certain departments of major universities prefer the term
computing science, to emphasize precisely that difference. Danish scientist
Peter Naur suggested the term datalogy,[33] 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 practitioners of the field of computing were suggested
in the Communications of the ACM – turingineer, turologist, flow-charts-man,
applied meta-mathematician, and applied epistemologist.[34] Three months later
in the same journal, comptologist was suggested, followed next year by
hypologist.[35] The term computics has also been suggested.[36] 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 (Italy, The Netherlands),
informática (Spain, Portugal), informatika (Slavic languages) or pliroforiki
(πληροφορική, which means informatics) in Greek. Similar words have also been
adopted in the UK (as in the School of Informatics of the University of
Edinburgh).[37]
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 1] 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 much
cross-fertilization of ideas between the various computer-related disciplines.
Computer science research also often intersects other disciplines, such as
philosophy, cognitive science, linguistics, mathematics, physics, statistics,
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.[13] 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.[38] 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.[39]
The academic,
political, and funding aspects of computer science tend to depend on whether a
department 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.
Areas of computer
science[edit]
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.[40][41] 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)[42] – 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,
computer-human interaction, computer graphics, operating systems, and numerical
and symbolic computation as being important areas of computer science.[40]
Theoretical computer
science[edit]
Main article:
Theoretical computer science
The broader field of
theoretical computer science encompasses both the classical theory of
computation and a wide range of other topics that focus on the more abstract,
logical, and mathematical aspects of computing.
Theory of
computation[edit]
Main article: Theory
of computation
According to Peter J.
Denning, the fundamental question underlying computer science is, "What
can be (efficiently) automated?"[13] The study of the 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,[43] is an open
problem in the theory of computation.
DFAexample.svg Wang tiles.png P = NP ? GNITIRW-TERCES Blochsphere.svg
Automata theory Computability theory Computational complexity theory Cryptography Quantum computing theory
Information and coding
theory[edit]
Main articles:
Information theory and Coding theory
Information theory is
related to the quantification of information. This was developed by Claude E.
Shannon to find fundamental limits on signal processing operations such as
compressing data and on reliably storing and communicating data.[44] 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.
Algorithms and data
structures[edit]
O(n^2) Sorting quicksort anim.gif Singly linked list.png SimplexRangeSearching.png
Analysis of algorithms Algorithms Data
structures Computational geometry
Programming language
theory[edit]
Main article:
Programming language theory
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.
\Gamma\vdash x:
\text{Int} Ideal compiler.png Python add5 syntax.svg
Type theory Compiler design Programming languages
Formal methods[edit]
Main article: Formal
methods
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[edit]
Applied Computer
Science aims at identifying certain Computer Science concepts that can be used
directly in solving real world problems.
Artificial
intelligence[edit]
Main article:
Artificial intelligence
This branch of
computer science aims to or is required to synthesise goal-orientated processes
such as problem-solving, decision-making, environmental adaptation, learning
and communication which are found in humans and animals. From its origins in
cybernetics and in the Dartmouth Conference (1956), artificial intelligence
(AI) 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 and modeling such as
finance and economics, data mining and the physical sciences. 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.
Nicolas P. Rougier's rendering
of the human brain.png Human eye,
rendered from Eye.svg.png Corner.png KnnClassification.svg
Machine learning Computer vision Image processing Pattern recognition
User-FastFission-brain.gif Data.png Sky.png Earth.png
Cognitive science Data mining Evolutionary computation Information
retrieval
Neuron.svg English.png HONDA ASIMO.jpg MeningiomaMRISegmentation.png
Knowledge
representation Natural language
processing Robotics Medical Image Computing
Computer architecture
and engineering[edit]
Main articles:
Computer architecture and Computer engineering
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.[45] The field often involves disciplines of computer engineering and
electrical engineering, selecting and interconnecting hardware components to
create computers that meet functional, performance, and cost goals.
NOR ANSI.svg Fivestagespipeline.png SIMD.svg
Digital logic Microarchitecture Multiprocessing
Operating system
placement.svg NETWORK-Library-LAN.png Emp Tables (Database).PNG Padlock.svg
Operating systems Computer networks Databases Information security
Roomba original.jpg Flowchart.png Ideal compiler.png Python
add5 syntax.svg
Ubiquitous computing Systems architecture Compiler design Programming
languages
Computer graphics and
visualization[edit]
Main article: Computer
graphics (computer science)
Computer graphics is
the study of digital visual contents, and involves synthese and manipulations
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.
Computer security and
cryptography[edit]
Main articles:
Computer security and Cryptography
Computer security is a
branch of computer technology, whose objective includes protection of
information from unauthorized access, disruption, or modification while
maintaining the accessibility and usability of the system for its intended
users. Cryptography is the practice and study of hiding (encryption) and
therefore deciphering (decryption) information. Modern cryptography is largely
related to computer science, for many encryption and decryption algorithms are
based on their computational complexity.
Computational
science[edit]
Computational science
(or scientific computing) is the field of study concerned with constructing
mathematical models and quantitative analysis techniques and using computers to
analyze and solve scientific problems. In practical use, it is typically the
application of computer simulation and other forms of computation to problems
in various scientific disciplines.
Lorenz attractor
yb.svg Quark wiki.jpg Naphthalene-3D-balls.png 1u04-argonaute.png
Numerical analysis Computational physics Computational chemistry Bioinformatics
Computer
Networks[edit]
Main article: Computer
network
This branch of
computer science aims to manage networks between computers worldwide.
Concurrent, parallel
and distributed systems[edit]
Main articles:
Concurrency (computer science) 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. A distributed
system extends the idea of concurrency onto multiple computers connected
through a network. Computers within the same distributed system have their own
private memory, and information is often exchanged amongst themselves to
achieve a common goal.
Databases[edit]
Main articles:
Database and Database management systems
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.
Health
Informatics[edit]
Main article: Health
Informatics
Health Informatics in
computer science deals with computational techniques for solving problems in
health care.
Information
science[edit]
Main article:
Information science
Earth.png Neuron.png English.png Wacom
graphics tablet and pen.png
Information retrieval Knowledge representation Natural language processing Human–computer interaction
Software
engineering[edit]
Main article: Software
engineering
Software engineering
is the study of designing, implementing, and modifying 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 doesn't just deal with the creation or
manufacture of new software, but its internal maintenance and arrangement. Both
computer applications software engineers and computer systems software
engineers are projected to be among the fastest growing occupations from 2008
and 2018.
See also: computer
programming
The great insights of
computer science[edit]
The philosopher of
computing Bill Rapaport noted three Great Insights of Computer Science [46]
Leibniz's, Boole's,
Alan Turing's, Shannon's, & Morse's insight: There are only 2 objects that
a computer has to deal with in order to represent "anything"
All the information
about any computable problem can be represented using only 0 & 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.).
See also: digital
physics
Alan Turing's insight:
There are only 5 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 5 basic instructions:
* move left one
location
* move right one
location
* print 0 at current-location
* print 1 at
current-location
* erase
current-location[citation needed]
See also: Turing
machine
Boehm and Jacopini's
insight: There are only 3 ways of combining these actions (into more complex
ones) that are needed in order for a computer to do "anything"
Only 3 rules are
needed to combine any set of basic instructions into more complex ones:
sequence:
first do this; then do
that
selection :
IF such-&-such is
the case,
THEN do this
ELSE do that
repetition:
WHILE such & such
is the case DO this
Note that the 3 rules
of Boehm's and Jacopini's insight can be further simplified with the use of
goto (which means it's more elementary than structured programming.)
See also: Elementary
function arithmetic#Friedman's grand conjecture
Academia[edit]
Conferences[edit]
Further information:
List of computer science conferences
Conferences are
strategic events of the Academic Research in computer science. During those
conferences, researchers from the public and private sectors present their
recent work and meet. Proceedings of these conferences are an important part of
the computer science literature.
Journals[edit]
Further information:
Category:Computer science journals
Education[edit]
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. The
ACM/IEEE-CS Joint Curriculum Task Force "Computing Curriculum 2005" (and
2008 update) [47] gives a guideline for university curriculum.
Other colleges and
universities, as well as secondary schools and vocational programs that teach
computer science, emphasize the practice of advanced 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 process aspects of computer programming are
often referred to as software engineering.
While computer science
professions increasingly drive the U.S. economy, computer science education is
absent in most American K-12 curricula. A report entitled "Running on
Empty: The Failure to Teach K-12 Computer Science in the Digital Age" was
released in October 2010 by Association for Computing Machinery (ACM) and
Computer Science Teachers Association (CSTA), and revealed that only 14 states
have adopted significant education standards for high school computer science.
The report also found that only nine states count high school computer science
courses as a core academic subject in their graduation requirements. In tandem
with "Running on Empty", a new non-partisan advocacy coalition -
Computing in the Core (CinC) - was founded to influence federal and state
policy, such as the Computer Science Education Act, which calls for grants to
states to develop plans for improving computer science education and supporting
computer science teachers.
Within the United
States a gender gap in computer science education has been observed as well.
Research conducted by the WGBH Educational Foundation and the Association for
Computing Machinery (ACM) revealed that more than twice as many high school
boys considered computer science to be a “very good” or “good” college major than
high school girls.[48] In addition, the high school Advanced Placement (AP)
exam for computer science has displayed a disparity in gender. Compared to
other AP subjects it has the lowest number of female participants, with a
composition of about 15 percent women.[49] This gender gap in computer science
is further witnessed at the college level, where 31 percent of undergraduate
computer science degrees are earned by women and only 8 percent of computer
science faculty consists of women.[50] According to an article published by the
Epistemic Games Group in August 2012, the number of women graduates in the
computer science field has declined to 13 percent.[51]
See also[edit]
Main article: Outline
of computer science
Portal icon Computer science portal
Academic genealogy of
computer scientists
Informatics (academic
field)
List of academic
computer science departments
List of computer
science conferences
List of computer
scientists
List of publications
in computer science
List of pioneers in
computer science
List of software
engineering topics
List of unsolved
problems in computer science
Women in computing
Wikipedia book
Computer science at Wikipedia books
Notes[edit]
Jump up ^ See the
entry "Computer science" on Wikiquote for the history of this
quotation.
References[edit]
Jump up ^
http://www.cs.bu.edu/AboutCS/WhatIsCS.pdf.
Jump up ^
"WordNet Search - 3.1". Wordnetweb.princeton.edu. Retrieved
2012-05-14.
Jump up ^ "Blaise
Pascal". School of Mathematics and Statistics University of St Andrews,
Scotland.
Jump up ^ "A
Brief History of Computing".
Jump up ^ In 1851
Jump up ^
"Science Museum - Introduction to Babbage". Archived from the
original on 2006-09-08. Retrieved 2006-09-24.
Jump up ^ Anthony
Hyman, Charles Babbage, pioneer of the computer, 1982
Jump up ^ "The
introduction of punched cards into the new engine was important not only as a
more convenient form of control than the drums, or because programs could now
be of unlimited extent, and could be stored and repeated without the danger of
introducing errors in setting the machine by hand; it was important also
because it served to crystallize Babbage's feeling that he had invented
something really new, something much more than a sophisticated calculating
machine." Bruce Collier, 1970
Jump up ^ "A
Selection and Adaptation From Ada's Notes found in "Ada, The Enchantress
of Numbers," by Betty Alexandra Toole Ed.D. Strawberry Press, Mill Valley,
CA". Retrieved 2006-05-04.
Jump up ^ "In
this sense Aiken needed IBM, whose technology included the use of punched
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Further reading[edit]
Overview
Tucker, Allen B.
(2004). Computer Science Handbook (2nd ed.). Chapman and Hall/CRC. ISBN
1-58488-360-X.
"Within more than
70 chapters, every one new or significantly revised, one can find any kind of
information and references about computer science one can imagine. [...] all in
all, there is absolute nothing about Computer Science that can not be found in
the 2.5 kilogram-encyclopaedia with its 110 survey articles [...]."
(Christoph Meinel, Zentralblatt MATH)
van Leeuwen, Jan
(1994). Handbook of Theoretical Computer Science. The MIT Press. ISBN
0-262-72020-5.
"[...] this set
is the most unique and possibly the most useful to the [theoretical computer
science] community, in support both of teaching and research [...]. The books
can be used by anyone wanting simply to gain an understanding of one of these
areas, or by someone desiring to be in research in a topic, or by instructors
wishing to find timely information on a subject they are teaching outside their
major areas of expertise." (Rocky Ross, SIGACT News)
Ralston, Anthony;
Reilly, Edwin D.; Hemmendinger, David (2000). Encyclopedia of Computer Science
(4th ed.). Grove's Dictionaries. ISBN 1-56159-248-X.
"Since 1976, this
has been the definitive reference work on computer, computing, and computer
science. [...] Alphabetically arranged and classified into broad subject areas,
the entries cover hardware, computer systems, information and data, software,
the mathematics of computing, theory of computation, methodologies,
applications, and computing milieu. The editors have done a commendable job of
blending historical perspective and practical reference information. The
encyclopedia remains essential for most public and academic library reference
collections." (Joe Accardin, Northeastern Illinois Univ., Chicago)
Edwin D. Reilly
(2003). Milestones in Computer Science and Information Technology. Greenwood
Publishing Group. ISBN 978-1-57356-521-9.
Selected papers
Knuth, Donald E.
(1996). Selected Papers on Computer Science. CSLI Publications, Cambridge
University Press.
Collier, Bruce. The
little engine that could've: The calculating machines of Charles Babbage.
Garland Publishing Inc. ISBN 0-8240-0043-9.
Cohen, Bernard (2000).
Howard Aiken, Portrait of a computer pioneer. The MIT press. ISBN 978-0-2625317-9-5.
Randell, Brian (1973).
The origins of Digital computers, Selected Papers. Springer-Verlag. ISBN
3-540-06169-X.
"Covering a
period from 1966 to 1993, its interest lies not only in the content of each of
these papers — still timely today — but also in their being put together so
that ideas expressed at different times complement each other nicely." (N.
Bernard, Zentralblatt MATH)
Articles
Peter J. Denning. Is
computer science science?, Communications of the ACM, April 2005.
Peter J. Denning,
Great principles in computing curricula, Technical Symposium on Computer
Science Education, 2004.
Research evaluation
for computer science, Informatics Europe report[dead link]. Shorter journal
version: Bertrand Meyer, Christine Choppy, Jan van Leeuwen and Jorgen
Staunstrup, Research evaluation for computer science, in Communications of the
ACM, vol. 52, no. 4, pp. 31–34, April 2009.
Curriculum and
classification
Association for
Computing Machinery. 1998 ACM Computing Classification System. 1998.
Joint Task Force of
Association for Computing Machinery (ACM), Association for Information Systems
(AIS) and IEEE Computer Society (IEEE-CS). Computing Curricula 2005: The
Overview Report. September 30, 2005.
Norman Gibbs, Allen
Tucker. "A model curriculum for a liberal arts degree in computer
science". Communications of the ACM, Volume 29 Issue 3, March 1986.
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