Computer science

Computer science is the study of processes that interact with data and that can be represented as data in the form of programs. It enables the use of algorithms to manipulate, store, and communicate digital information. A computer scientist studies the theory of computation and the practice of designing software systems.[1]

Its fields can be divided into theoretical and practical disciplines. Computational complexity theory is highly abstract, while computer graphics emphasizes real-world applications. Programming language theory considers approaches to the description of computational processes, while computer programming itself involves the use of programming languages and complex systems. Human–computer interaction considers the challenges in making computers useful, usable, and accessible.

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 computation and practical techniques for their application.

History

Babbage40
Charles Babbage, sometimes referred to as the "father of computing".[2]
Ada lovelace
Ada Lovelace is often credited with publishing the first algorithm intended for processing on a computer.[3]

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.[4] In 1673, Gottfried Leibniz demonstrated a digital mechanical calculator, called the Stepped Reckoner.[5] 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[note 1] 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".[7] "A crucial step was the adoption of a punched card system derived from the Jacquard loom"[7] 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 computer program.[8] 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[9] 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".[10]

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.[11] 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. [12] Ultimately, the close relationship between IBM and the 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. [13] Computer science began to be established as a distinct academic discipline in the 1950s and early 1960s.[14][15] 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.[16] Since practical computers became available, many applications of computing have become distinct areas of study in their own rights.

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.[17][18] 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[19] and later the IBM 709[20] 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".[17] During the late 1950s, the computer science discipline was very much in its developmental stages, and such issues were commonplace.[18]

Time has seen significant improvements in the usability and effectiveness of computing technology.[21] 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 humanitarian 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.

Contributions

Enigma
The German military used the Enigma machine (shown here) during World War II for communications they wanted kept secret. The large-scale decryption of Enigma traffic at Bletchley Park was an important factor that contributed to Allied victory in WWII.[22]

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 BC).

These contributions include:

Etymology

Although first proposed in 1956,[18] the term "computer science" appears in a 1959 article in Communications of the ACM,[30] 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,[31] 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.[32] 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.[33] Certain departments of major universities prefer the term computing science, to emphasize precisely that difference. Danish scientist Peter Naur suggested the term datalogy,[34] 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.

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 ACMturingineer, turologist, flow-charts-man, applied meta-mathematician, and applied epistemologist.[35] Three months later in the same journal, comptologist was suggested, followed next year by hypologist.[36] The term computics has also been suggested.[37] 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 of the University of Edinburgh).[38] "In the U.S., however, informatics is linked with applied computing, or computing in the context of another domain."[39]

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 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, biology, 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.[14] Early computer science was strongly influenced by the work of mathematicians such as Kurt Gödel, Alan Turing, 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.[18]

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.[40] 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.[41]

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.

Philosophy

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.[42] Peter Denning's working group argued that they are theory, abstraction (modeling), and design.[43] 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).[44]

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.[45][46] 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)[47]—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.[45]

Theoretical computer science

Theoretical Computer Science is mathematical and abstract in spirit, but it derives its motivation from practical and everyday computation. Its aim is to understand the nature of computation and, as a consequence of this understanding, provide more efficient methodologies. All studies related to mathematical, logic and formal concepts and methods could be considered as theoretical computer science, provided that the motivation is clearly drawn from the field of computing.

Data structures and algorithms

Data structures and algorithms are the study of commonly used computational methods and their computational efficiency.

O(n2) Sorting quicksort anim Singly linked list TSP Deutschland 3 SimplexRangeSearching
Analysis of algorithms Algorithms Data structures Combinatorial optimization Computational geometry

Theory of computation

According to Peter Denning, the fundamental question underlying computer science is, "What can be (efficiently) automated?"[14] 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,[48] is an open problem in the theory of computation.

DFAexample Wang tiles P = NP? GNITIRW-TERCES Blochsphere
Automata theory Computability theory Computational complexity theory Cryptography Quantum computing theory

Information and coding theory

Information theory 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.[49] 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.

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.

Compiler Python add5 syntax
Type theory Compiler design Programming languages

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.

Computer systems

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.[50] 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 Fivestagespipeline SIMD
Digital logic Microarchitecture Multiprocessing
Roomba original Flowchart Operating system placement
Ubiquitous computing Systems architecture Operating systems

Computer performance analysis

Computer performance analysis is the study of work flowing through computers with the general goals of improving throughput, controlling response time, using resources efficiently, eliminating bottlenecks, and predicting performance under anticipated peak loads.[51] Benchmarks provide a method of comparing the performance of various subsystems across different chip/system architectures.

Concurrent, parallel and distributed systems

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 among themselves to achieve a common goal.

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 an objective of protecting 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.

Databases

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.

Computer applications

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.

Human–computer interaction

Research that develops theories, principles, and guidelines for user interface designers, so they can create satisfactory user experiences with desktop, laptop, and mobile devices.

Scientific computing

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. 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 Quark wiki Naphthalene-3D-balls 1u04-argonaute
Numerical analysis Computational physics Computational chemistry Bioinformatics

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.

Nicolas P. Rougier's rendering of the human brain Human eye, rendered from Eye Corner
Machine learning Computer vision Image processing
KnnClassification Julia iteration data Sky
Pattern recognition Data mining Evolutionary computation
Neuron English HONDA ASIMO
Knowledge representation and reasoning Natural language processing Robotics

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.

Discoveries

The philosopher of computing Bill Rapaport noted three Great Insights of Computer Science:[52]

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.
Note that 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.

Many languages offer support for multiple paradigms, making the distinction more a matter of style than of technical capabilities.[53]

Academia

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.[54][55] 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.[56]

Education

Computer Science, known by its near synonyms, Computing, Computer Studies, Information Technology (IT) and Information and Computing Technology (ICT), has been taught in UK schools since the days of batch processing, mark sensitive cards and paper tape but usually to a select few students.[57] In 1981, the BBC produced a micro-computer and classroom network and Computer Studies became common for GCE O level students (11–16-year-old), and Computer Science to A level students. Its importance was recognised, and it became a compulsory part of the National Curriculum, for Key Stage 3 & 4. In September 2014 it became an entitlement for all 7,000,000 pupils over the age of 4.[58]

In the US, with 14,000 school districts deciding the curriculum, provision was fractured.[59] According to a 2010 report by the Association for Computing Machinery (ACM) and Computer Science Teachers Association (CSTA), only 14 out of 50 states have adopted significant education standards for high school computer science.[60]

Institute of Electrical and Electronics Engineers (IEEE) produces over 30% of the world's literature in the electrical and electronics engineering and computer science fields, publishing well over 100 peer-reviewed journals.

Israel, New Zealand, and South Korea have included computer science in their national secondary education curricula,[61][62] and several others are following.[63]

Challenges

In many countries, there is a significant gender gap in computer science education. In 2012, only 20 percent of computer science degrees in the United States were awarded to women.[64] The gender gap is also a problem in other western countries.[65] The gap is smaller, or nonexistent, in some parts of the world. In 2011, women earned half of the computer science degrees in Malaysia.[66] In 2001, 55 percent of computer science graduates in Guyana were women.[65]

See also

Computer science – Wikipedia book

Notes

  1. ^ In 1851
  2. ^ "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
  3. ^ See the entry "Computer science" on Wikiquote for the history of this quotation.
  4. ^ The word "anything" is written in quotation marks because there are things that computers cannot do. One example is: to answer the question if an arbitrary given computer program will eventually finish or run forever (the Halting problem).

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Further reading

Overview
  • Tucker, Allen B. (2004). Computer Science Handbook (2nd ed.). Chapman and Hall/CRC. ISBN 978-1-58488-360-9.
    • "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 978-0-262-72020-5.
    • "[…] 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 978-1-56159-248-7.
    • "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 literature
  • Knuth, Donald E. (1996). Selected Papers on Computer Science. CSLI Publications, Cambridge University Press.
  • Collier, Bruce (1990). The little engine that could've: The calculating machines of Charles Babbage. Garland Publishing Inc. ISBN 978-0-8240-0043-1.
  • Cohen, Bernard (2000). Howard Aiken, Portrait of a computer pioneer. The MIT press. ISBN 978-0-262-53179-5.
  • Tedre, Matti (2014). The Science of Computing: Shaping a Discipline. CRC Press, Taylor & Francis.
  • Randell, Brian (1973). The origins of Digital computers, Selected Papers. Springer-Verlag. ISBN 978-3-540-06169-4.
    • "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
Curriculum and classification

External links

Bibliography and academic search engines

Professional organizations

Misc

Computer Science and Engineering

Computer science and engineering (CSE) is an academic program at some universities that integrates the fields of computer engineering and computer science. It is a sub-field of electronic engineering, covering only the digital aspects of electronics engineering, specializing in hardware-systems areas like computer architecture, processor design, high-performance computing, parallel processing, computer networks and embedded systems. CSE programs also include core subjects of computer science such as operating systems, theory of computation, design and analysis of algorithms, data structures and database systems. The program aims at designing, developing and troubleshooting computing devices (such as personal computers, supercomputers, robots, smartphones, networking devices, embedded devices), focusing the underlying fundamental issues (like processor architecture design, operating system design, memory management, digital system design, communication protocol design, software development and database management) in the most efficient and effective way.

Computer science programs typically centers primarily around theory and software, with only some hardware; upper division courses tend to allow a lot of freedom to specialize in software and theory related areas (e.g. algorithms, artificial intelligence, cryptography/security, graphics/visualization, numerical and symbolic computing, operating systems/distributed processing, software engineering).

Computer engineering programs tend to resemble computer science at the lower division with similar introductory programming and math courses, but diverges from computer science at the upper division with heavy electrical engineering requirements (e.g. digital and analog circuits, integrated circuit design, VLSI design and control systems). Despite the overlap with computer science at the lower division level, computer engineering skews much more heavily toward the electronics side that it has more in common with electrical engineering.

Computer Science and Engineering integrates all of the above and is intended to develop a solid understanding of the entire machine (computer hardware and software). The higher unit count required to complete the program often means that a CSE student will need to spend an extra year in university.

Although Computer Science and Engineering is the common designation for the combined program, some universities (such as Berkeley and MIT) deviate by calling their program Electrical Engineering and Computer Science (EECS). Furthermore, there are some universities (such as UCI and UC Merced) that named their department EECS and the program housed within CSE.

Computer engineering

Computer engineering is a branch of engineering that integrates several fields of computer science and electronic engineering required to develop computer hardware and software. Computer engineers usually have training in electronic engineering (or electrical engineering), software design, and hardware-software integration instead of only software engineering or electronic engineering. Computer engineers are involved in many hardware and software aspects of computing, from the design of individual microcontrollers, microprocessors, personal computers, and supercomputers, to circuit design. This field of engineering not only focuses on how computer systems themselves work but also how they integrate into the larger picture.Usual tasks involving computer engineers include writing software and firmware for embedded microcontrollers, designing VLSI chips, designing analog sensors, designing mixed signal circuit boards, and designing operating systems. Computer engineers are also suited for robotics research, which relies heavily on using digital systems to control and monitor electrical systems like motors, communications, and sensors.

In many institutions of higher learning, computer engineering students are allowed to choose areas of in-depth study in their junior and senior year because the full breadth of knowledge used in the design and application of computers is beyond the scope of an undergraduate degree. Other institutions may require engineering students to complete one or two years of general engineering before declaring computer engineering as their primary focus.

Computing

Computing is any activity that uses computers. It includes developing hardware and software, and using computers to manage and process information, communicate and entertain. Computing is a critically important, integral component of modern industrial technology. Major computing disciplines include computer engineering, software engineering, computer science, information systems, and information technology.

Concurrency (computer science)

In computer science, concurrency refers to the ability of different parts or units of a program, algorithm, or problem to be executed out-of-order or in partial order, without affecting the final outcome. This allows for parallel execution of the concurrent units, which can significantly improve overall speed of the execution in multi-processor and multi-core systems. In more technical terms, concurrency refers to the decomposability property of a program, algorithm, or problem into order-independent or partially-ordered components or units.A number of mathematical models have been developed for general concurrent computation including Petri nets, process calculi, the parallel random-access machine model, the actor model and the Reo Coordination Language.

DBLP

DBLP is a computer science bibliography website. Starting in 1993 at the University of Trier, Germany, it grew from a small collection of HTML files and became an organization hosting a database and logic programming bibliography site. DBLP listed more than 3.66 million journal articles, conference papers, and other publications on computer science in July 2016, up from about 14,000 in 1995. All important journals on computer science are tracked. Proceedings papers of many conferences are also tracked. It is mirrored at three sites across the Internet.For his work on maintaining DBLP, Michael Ley received an award from the Association for Computing Machinery and the VLDB Endowment Special Recognition Award in 1997.

DBLP originally stood for DataBase systems and Logic Programming. As a backronym, it has been taken to stand for Digital Bibliography & Library Project; however, it is now preferred that the acronym be simply a name, hence the new title "The DBLP Computer Science Bibliography".Users of dblp remain unaffected by some additional attributes in the DTD as of February 2016, which are meant to support future versions of the data file. Thus consumers of the raw dblp.xml file should update their local dblp.dtd file, according to the notice on the home page, as of February 2016.

Data structure

In computer science, a data structure is a data organization, management and storage format that enables efficient access and modification. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.

Data type

In computer science and computer programming, a data type or simply type is an attribute of data which tells the compiler or interpreter how the programmer intends to use the data. Most programming languages support common data types of real, integer and boolean. A data type constrains the values that an expression, such as a variable or a function, might take. This data type defines the operations that can be done on the data, the meaning of the data, and the way values of that type can be stored. A type of value from which an expression may take its value.

Foobar

The terms foobar (), or foo and others are used as placeholder names (also referred to as metasyntactic variables) in computer programming or computer-related documentation. They have been used to name entities such as variables, functions, and commands whose exact identity is unimportant and serve only to demonstrate a concept.

Formal language

In mathematics, computer science, and linguistics, a formal language consists of words whose letters are taken from an alphabet and are well-formed according to a specific set of rules.

The alphabet of a formal language consist of symbols, letters, or tokens that concatenate into strings of the language. Each string concatenated from symbols of this alphabet is called a word, and the words that belong to a particular formal language are sometimes called well-formed words or well-formed formulas. A formal language is often defined by means of a formal grammar such as a regular grammar or context-free grammar, which consists of its formation rules.

The field of formal language theory studies primarily the purely syntactical aspects of such languages—that is, their internal structural patterns. Formal language theory sprang out of linguistics, as a way of understanding the syntactic regularities of natural languages.

In computer science, formal languages are used among others as the basis for defining the grammar of programming languages and formalized versions of subsets of natural languages in which the words of the language represent concepts that are associated with particular meanings or semantics. In computational complexity theory, decision problems are typically defined as formal languages, and complexity classes are defined as the sets of the formal languages that can be parsed by machines with limited computational power. In logic and the foundations of mathematics, formal languages are used to represent the syntax of axiomatic systems, and mathematical formalism is the philosophy that all of mathematics can be reduced to the syntactic manipulation of formal languages in this way.

Information system

Information systems (IS) are formal, sociotechnical, organizational systems designed to collect, process, store, and distribute information. In a sociotechnical perspective, information systems are composed by four components: task, people, structure (or roles), and technology.A computer information system is a system composed of people and computers that processes or interprets information. The term is also sometimes used in more restricted senses to refer to only the software used to run a computerized database or to refer to only a computer system.

Information Systems is an academic study of systems with a specific reference to information and the complementary networks of hardware and software that people and organizations use to collect, filter, process, create and also distribute data. An emphasis is placed on an information system having a definitive boundary, users, processors, storage, inputs, outputs and the aforementioned communication networks.Any specific information system aims to support operations, management and decision-making. An information system is the information and communication technology (ICT) that an organization uses, and also the way in which people interact with this technology in support of business processes.Some authors make a clear distinction between information systems, computer systems, and business processes. Information systems typically include an ICT component but are not purely concerned with ICT, focusing instead on the end use of information technology. Information systems are also different from business processes. Information systems help to control the performance of business processes.Alter argues for advantages of viewing an information system as a special type of work system. A work system is a system in which humans or machines perform processes and activities using resources to produce specific products or services for customers. An information system is a work system whose activities are devoted to capturing, transmitting, storing, retrieving, manipulating and displaying information.As such, information systems inter-relate with data systems on the one hand and activity systems on the other. An information system is a form of communication system in which data represent and are processed as a form of social memory. An information system can also be considered a semi-formal language which supports human decision making and action.

Information systems are the primary focus of study for organizational informatics.

Inheritance (object-oriented programming)

In object-oriented programming, inheritance is the mechanism of basing an object or class upon another object (prototype-based inheritance) or class (class-based inheritance), retaining similar implementation. Also defined as deriving new classes (sub classes) from existing ones (super class or base class) and forming them into a hierarchy of classes. In most class-based object-oriented languages, an object created through inheritance (a "child object") acquires all the properties and behaviors of the parent object (except: constructors, destructor, overloaded operators and friend functions of the base class). Inheritance allows programmers to create classes that are built upon existing classes, to specify a new implementation while maintaining the same behaviors (realizing an interface), to reuse code and to independently extend original software via public classes and interfaces. The relationships of objects or classes through inheritance give rise to a directed graph. Inheritance was invented in 1969 for Simula.An inherited class is called a subclass of its parent class or super class. The term "inheritance" is loosely used for both class-based and prototype-based programming, but in narrow use the term is reserved for class-based programming (one class inherits from another), with the corresponding technique in prototype-based programming being instead called delegation (one object delegates to another).

Inheritance should not be confused with subtyping. In some languages inheritance and subtyping agree, whereas in others they differ; in general, subtyping establishes an is-a relationship, whereas inheritance only reuses implementation and establishes a syntactic relationship, not necessarily a semantic relationship (inheritance does not ensure behavioral subtyping). To distinguish these concepts, subtyping is also known as interface inheritance, whereas inheritance as defined here is known as implementation inheritance or code inheritance. Still, inheritance is a commonly used mechanism for establishing subtype relationships.Inheritance is contrasted with object composition, where one object contains another object (or objects of one class contain objects of another class); see composition over inheritance. Composition implements a has-a relationship, in contrast to the is-a relationship of subtyping.

Interface (computing)

In computing, an interface is a shared boundary across which two or more separate components of a computer system exchange information. The exchange can be between software, computer hardware, peripheral devices, humans, and combinations of these. Some computer hardware devices, such as a touchscreen, can both send and receive data through the interface, while others such as a mouse or microphone may only provide an interface to send data to a given system.

Ontology (information science)

In computer science and information science, an ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains.

Every field creates ontologies to limit complexity and organize information into data and knowledge. As new ontologies are made, their use hopefully improves problem solving within that domain. Translating research papers within every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their languages.Since Google started an initiative called Knowledge Graph, a substantial amount of research has gone on using the phrase knowledge graph as a generalized term. Although there is no clear definition for the term knowledge graph, it is sometimes used as synonym for ontology. One common interpretation is that a knowledge graph represents a collection of interlinked descriptions of entities – real-world objects, events, situations or abstract concepts. Unlike ontologies, knowledge graphs, such as Google's Knowledge Graph, often contain large volumes of factual information with less formal semantics. In some contexts, the term knowledge graph is used to refer to any knowledge base that is represented as a graph.

Polymorphism (computer science)

In programming languages and type theory, polymorphism is the provision of a single interface to entities of different types or the use of a single symbol to represent multiple different types.The most commonly recognised major classes of polymorphism are:

Ad hoc polymorphism: defines a common interface for an arbitrary set of individually specified types.

Parametric polymorphism: when one or more types are not specified by name but by abstract symbols that can represent any type.

Subtyping (also called subtype polymorphism or inclusion polymorphism): when a name denotes instances of many different classes related by some common superclass.

Software engineer

A software engineer is a person who applies the principles of software engineering to the design, development, maintenance, testing, and evaluation of computer software.

Prior to the mid-1970s, software practitioners called themselves computer programmers or software developers, regardless of their actual jobs. Many people prefer to call themselves software developer and programmer, because most widely agree what these terms mean, while the exact meaning of software engineer is still being debated.

Software framework

In computer programming, a software framework is an abstraction in which software providing generic functionality can be selectively changed by additional user-written code, thus providing application-specific software. A software framework provides a standard way to build and deploy applications.

A software framework is a universal, reusable software environment that provides particular functionality as part of a larger software platform to facilitate development of software applications, products and solutions. Software frameworks may include support programs, compilers, code libraries, tool sets, and application programming interfaces (APIs) that bring together all the different components to enable development of a project or system.

Frameworks have key distinguishing features that separate them from normal libraries:

inversion of control: In a framework, unlike in libraries or in standard user applications, the overall program's flow of control is not dictated by the caller, but by the framework.

extensibility: A user can extend the framework – usually by selective overriding – or programmers can add specialized user code to provide specific functionality.

non-modifiable framework code: The framework code, in general, is not supposed to be modified, while accepting user-implemented extensions. In other words, users can extend the framework, but should not modify its code.

Theoretical computer science

Theoretical computer science (TCS) is a subset of general computer science and mathematics that focuses on more mathematical topics

of computing and includes the theory of computation.

It is difficult to circumscribe the theoretical areas precisely. The ACM's Special Interest Group on Algorithms and Computation Theory (SIGACT) provides the following description:

TCS covers a wide variety of topics including algorithms, data structures, computational complexity, parallel and distributed computation, probabilistic computation, quantum computation, automata theory, information theory, cryptography, program semantics and verification, machine learning, computational biology, computational economics, computational geometry, and computational number theory and algebra. Work in this field is often distinguished by its emphasis on mathematical technique and rigor.

Thread (computing)

In computer science, a thread of execution is the smallest sequence of programmed instructions that can be managed independently by a scheduler, which is typically a part of the operating system. The implementation of threads and processes differs between operating systems, but in most cases a thread is a component of a process. Multiple threads can exist within one process, executing concurrently and sharing resources such as memory, while different processes do not share these resources. In particular, the threads of a process share its executable code and the values of its dynamically allocated variables and non-thread-local global variables at any given time.

Tim Berners-Lee

Sir Timothy John Berners-Lee (born 8 June 1955), also known as TimBL, is an English engineer and computer scientist, best known as the inventor of the World Wide Web. He is currently a professor of computer science at the University of Oxford and the Massachusetts Institute of Technology (MIT). He made a proposal for an information management system on March 12, 1989, and he implemented the first successful communication between a Hypertext Transfer Protocol (HTTP) client and server via the internet in mid-November the same year.Berners-Lee is the director of the World Wide Web Consortium (W3C), which oversees the continued development of the Web. He is also the founder of the World Wide Web Foundation and is a senior researcher and holder of the 3Com founders chair at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He is a director of the Web Science Research Initiative (WSRI), and a member of the advisory board of the MIT Center for Collective Intelligence. In 2011, he was named as a member of the board of trustees of the Ford Foundation. He is a founder and president of the Open Data Institute, and is currently an advisor at social network MeWe.In 2004, Berners-Lee was knighted by Queen Elizabeth II for his pioneering work. In April 2009, he was elected a foreign associate of the United States National Academy of Sciences. Named in Time magazine's list of the 100 Most Important People of the 20th century, Berners-Lee has received a number of other accolades for his invention. He was honoured as the "Inventor of the World Wide Web" during the 2012 Summer Olympics opening ceremony, in which he appeared in person, working with a vintage NeXT Computer at the London Olympic Stadium. He tweeted "This is for everyone", which instantly was spelled out in LCD lights attached to the chairs of the 80,000 people in the audience. Berners-Lee received the 2016 Turing Award "for inventing the World Wide Web, the first web browser, and the fundamental protocols and algorithms allowing the Web to scale".

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