Outline of computer science

The following outline is provided as an overview of and topical guide to computer science:

Computer science (also called computing science) – study of the theoretical foundations of information and computation and their implementation and application in computer systems. One well known subject classification system for computer science is the ACM Computing Classification System devised by the Association for Computing Machinery.

Contents 1 Mathematical foundations 1.1 Algorithms and data structures 1.2 Artificial intelligence 1.3 Collaborative Networks 1.4 Communications and Security 1.5 Computer architecture 1.6 Computer graphics 1.7 Concurrent, parallel, and distributed systems 1.8 Databases 1.9 Programming languages and compilers 1.10 Scientific computing 1.11 Software engineering 1.12 Theory of computation 2 History 3 Vocations 4 Basic concepts 4.1 Data and data structures 4.2 Other 5 Programming paradigms 5.1 Object oriented programming 5.2 Other 6 See also 7 External links

Mathematical foundations

• Mathematical logic – Boolean logic and other ways of modeling logical queries; the uses and limitations of formal proof methods
• Number theory – Theory of the integers. Used in cryptography as well as a test domain in artificial intelligence.
• Graph theory – Foundations for data structures and searching algorithms.
• Game theory – Useful in artificial intelligence and cybernetics.
• Coding theory – Useful in networking and other areas where computers communicate with each other.

Algorithms and data structures

• Algorithms – Sequential and parallel computational procedures for solving a wide range of problems.
• Data structures – The organization and manipulation of data.

Artificial intelligence

• Artificial intelligence – The implementation and study of systems that exhibit an autonomous intelligence or behavior of their own.
• Automated reasoning – Solving engines, such as used in Prolog, which produce steps to a result given a query on a fact and rule database, and automated theorem provers that aim to prove mathematical theorems with some assistance from a programmer.
• Robotics – Algorithms for controlling the behavior of robots.
• Computer vision – Algorithms for identifying three dimensional objects from a two dimensional picture.
• Machine learning – Automated creation of a set of rules and axioms based on input.

Collaborative Networks

• Virtual Organization – Including virtual enterprises, extended enterprises.
• VO Breeding Environment – Including industry clusters, industrial districts, digital business ecosystems.
• Professional virtual community – Including virtual teams, social networks.

Communications and Security

• Networking – Algorithms and protocols for reliably communicating data across different shared or dedicated media, often including error correction.
• Computer security – Practical aspects of securing computer systems and computer networks.
• Cryptography – Applies results from complexity, probability and number theory to invent and break codes, and analyze the security of cryptographic protocols.

Computer architecture

• Computer architecture – The design, organization, optimization and verification of a computer system, mostly about CPUs and Memory subsystem (and the bus connecting them).
• Operating systems – Systems for managing computer programs and providing the basis of a usable system.

Computer graphics

• Computer graphics – Algorithms both for generating visual images synthetically, and for integrating or altering visual and spatial information sampled from the real world.
• Image processing – Determining information from an image through computation.
• Human computer interaction – The study and design of computer interfaces that people use.

Concurrent, parallel, and distributed systems

• Concurrency – The theory and practice of simultaneous computation; data safety in any multitasking or multithreaded environment.
• Parallel computing – Computing using multiple concurrent threads of execution, devising algorithms for solving problems on multiple processors to achieve maximal speed-up compared to sequential execution.
• Distributed computing – Computing using multiple computing devices over a network to accomplish a common objective or task and thereby reducing the latency involved in single processor contributions for any task.

Databases

• Relational databases – the set theoretic and algorithmic foundation of databases.
• Data mining – Study of algorithms for searching and processing information in documents and databases; closely related to information retrieval.

Programming languages and compilers

• Compiler theory – Theory of compiler design, based on Automata theory.
• Programming language pragmatics – Taxonomy of programming languages, their strength and weaknesses. Various programming paradigms, such as object-oriented programming.
• Programming language theory
• Formal semantics – rigorous mathematical study of the meaning of programs.
• Type theory – Formal analysis of the types of data, and the use of these types to understand properties of programs — especially program safety.

Scientific computing

• Computational science – constructing mathematical models and quantitative analysis techniques and using computers to analyze and solve scientific problems.
• Numerical analysis – Approximate numerical solution of mathematical problems such as root-finding, integration, the solution of ordinary differential equations; the approximation of special functions.
• Symbolic computation – Manipulation and solution of expressions in symbolic form, also known as Computer algebra.
• Computational physics – Numerical simulations of large non-analytic systems
• Computational chemistry – Computational modelling of theoretical chemistry in order to determine chemical structures and properties
• Bioinformatics – The use of computer science to maintain, analyse, store biological data and to assist in solving biological problems such as Protein folding, function prediction and Phylogeny.
• Computational neuroscience – Computational modelling of neurophysiology.

Software engineering

• Formal methods – Mathematical approaches for describing and reasoning about software designs.
• Software engineering – The principles and practice of designing, developing, and testing programs, as well as proper engineering practices.
• Reverse engineering – The application of the scientific method to the understanding of arbitrary existing software
• Algorithm design – Using ideas from algorithm theory to creatively design solutions to real tasks
• Computer programming – The practice of using a programming language to implement algorithms

Theory of computation

• Automata theory – Different logical structures for solving problems.
• Computability theory – What is calculable with the current models of computers. Proofs developed by Alan Turing and others provide insight into the possibilities of what may be computed and what may not.
• Computational complexity theory – Fundamental bounds (especially time and storage space) on classes of computations.
• Quantum computing theory – Explores computational models involving quantum superposition of bits.

History

Vocations

• Programmer
• Software architect
• Software developer
• Software tester
• Software engineer

Basic concepts

Data and data structures

• Associative array
• Array
• Data structure
• Data type
• Database
• Hash table
• List
• Matrix (computer science)
• String
• Tree

Other

• Abstraction
• Big O notation
• Closure
• Compiler

Programming paradigms

Object oriented programming

• Object oriented programming
• Class
• Inheritance
• Object

Other

• Functional programming
• Procedural programming
• Logic programming

See also

• Cognitive science

External links

• Outline of computer science at the Open Directory Project
• Directory of free university lectures in Computer Science
• Collection of Computer Science Bibliographies
• Photographs of computer scientists (Bertrand Meyer's gallery)

Webcasts

• UCLA Computer Science 1 Freshman Computer Science Seminar Section 1
• Berkeley Introduction to Computers

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