Computer science or computing science (sometimes abbreviated CS) is the study of the theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems. It is frequently described as the systematic study of algorithmic processes that create, describe, and transform information. According to Peter J. Denning, the fundamental question underlying computer science is, "What can be (efficiently) automated?" Computer science has many sub-fields; some, such as computer graphics, emphasize the computation of specific results, while others, such as computational complexity theory, study the 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, and human-computer interaction focuses on the challenges in making computers and computations useful, usable, and universally accessible to people.
The general public sometimes confuses computer science with careers that deal with computers (such as the noun Information Technology), or think that it relates to their own experience of computers, which typically involves activities such as gaming, web-browsing, and word-processing. However, the focus of computer science is more on understanding the properties of the programs used to implement software such as games and web-browsers, and using that understanding to create new programs or improve existing ones.
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. Charles Babbage designed a difference engine in Victorian times helped by Ada Lovelace. Around 1900, punch-card machines were introduced. 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 1950s and early 1960s. The first computer science degree program 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 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. 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". 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 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.
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. The Computer Sciences Accreditation Board (CSAB)  – which is made up of representatives of the Association for Computing Machinery (ACM), the Institute of Electrical and Electronics Engineers Computer Society, and the Association for Information Systems – 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.
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.
|Mathematical logic||Automata theory||Number theory||Graph theory|
|Type theory||Category theory||Computational geometry||Quantum computing theory|
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 computational problem.
|Numerical analysis||Computational physics||Computational chemistry||Bioinformatics|
|Computational neuroscience||Computational sociology||Computational economics||Biometrics|
This branch of computer science aims to create synthetic systems which solve computational problems, reason and/or communicate like animals and humans do. This theoretical and applied subfield requires a very rigorous and integrated expertise in multiple subject areas such as applied mathematics, logic, semiotics, electrical engineering, philosophy of mind neurophysiology, and social intelligence which can be used to advance the field of intelligence research or be applied to other subject areas which require computational understanding and modelling such as in finance or the physical sciences. It all started with the grandfather of computer science and artificial intelligence, Alan Turing, who proposed the Turing Test for the purpose of answering the ultimate question... "Can computers think ?".
|Machine Learning||Computer vision||Image Processing||Pattern Recognition|
|Cognitive Science||Data Mining||Evolutionary Computation||Information Retrieval|
|Knowledge Representation||Natural Language Processing||Robotics||Human–computer interaction|
The area of software engineering specializes in storage, transfer and communication of data rather than the computational analysis of data. Although many computer scientists seek software engineering positions it is not necessarily computer science related. In 2004, a newly established degree of software engineering established by both ACM and IEEE was formed to address these issues; a document called CCSE was written to explain the details. In addition those with degrees in information technology or management information systems are often found to be necessary supportive roles for both software engineering and computational work.
|Operating systems||Computer networks||Databases||Computer security|
|Ubiquitous computing||Systems architecture||Compiler design||Programming languages|
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. 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. Three months later in the same journal, comptologist was suggested, followed next year by hypologist. The term computics has also been suggested. In continental Europe, names such as informatique (French), Informatik (German) or informatica (Dutch), derived from information and possibly mathematics or automatic, are more common than names derived from computer/computation.
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. 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 philosophy, cognitive science, linguistics, mathematics, physics, statistics, and economics.
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 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.
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 computational science. Both types of departments tend to make efforts to bridge the field educationally if not across all research.
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 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 the meaning of the term, and whether or not it is the same thing as programming.
Computer science, or computing science, is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. Computer science has many sub-fields; some emphasize the computation of specific results (such as computer graphics), while others (such as computational complexity theory) relate to properties of computational problems. Still others focus on the challenges in implementing computations. For example, programming language theory studies approaches to describing computations, while computer programming applies specific programming languages to solve specific computational problems.
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The following is a list of all computer-related bookshelves:
All Programming language books...
TEXT PROCESSING – AWK – Regular Expressions — MARKUP – CSS – HTML – XHTML – XForms – XML: Managing Data Exchange — CONFIGURATION MANAGEMENT – Ant- — TYPESETTING – LaTeX – PostScript FAQ – TeX — HARDWARE PROGRAMMING – Programmable Logic — DATABASE – MySQL – SQL – XQuery
All Domain-specific language books...
|Associated Wikimedia for Computer science|
Computer science is the science of how to treat information. There are many different areas in computer science. Some of the areas consider problems in a more abstract way. Some areas need special machines, called computers. A person who works with computers will often need math, science, and logic in order to make and use computers.
This is so that they can find new and easier ways to do things.
Computers can do some things easily (for example: simple math, or sorting out a list of names from A-to-Z). Computers cannot do some things, though. Computers cannot answer questions when there is not enough information, or when there is no real answer. Also, computers may take too much time to finish long tasks. For example, it may take too long to find the shortest way through all of the towns in the USA - so instead a computer will try to make a close guess. A computer will answer these simpler questions much faster.
Algorithms are ways to solve problems or do things. Think about playing cards, for example. A computer scientist wants to sort the cards. First he wants to sort them out by color. Then he wants to order them by number (2, 3, 4, 5, 6, 7, 8, 9, 10, Jack, Queen, King, and Ace). The computer scientist may see different ways to sort the playing cards. He must now think about of how he will do it. When he decides, he has created an algorithm. After making the algorithm, the scientist needs to test whether the algorithm always does what it should. Then, the scientist can see how well his program sorts the cards.
A simple but very slow algorithm could be: drop the cards, pick them up, and check whether they are sorted. If they are not, do it again. This method will work, but it will often take a very long time.
A person may do this better by looking through all the cards, finding the first card (2 of diamonds), and putting it at the start. After this, he looks for the second card, and so on. This works much faster, and does not need much space.
Computer science began during World War II and separated from the other sciences during the 1960's and 1970's. Now, computer science uses special methods of doing things, and has its own special words. It is related to electrical engineering, mathematics, and language science.
Computer science looks at the theoretical parts of computers. Computer engineering looks at the physical parts of computers (the parts that a person can touch), and software engineering looks at the use of computer programs and how to make them.
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