Outline of computer science

What is computer science?

• Academic discipline
• Science
• * Applied science

  Subfields

Mathematical foundations

• Coding theory – Useful in networking and other areas where computers communicate with each other.
• Game theory – Useful in artificial intelligence and cybernetics.
• Discrete Mathematics
• Graph theory – Foundations for data structures and searching algorithms.
• 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.

  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.
• Computer vision – Algorithms for identifying three-dimensional objects from a two-dimensional picture.
• Soft computing, the use of inexact solutions for otherwise extremely difficult problems:
• * Machine learning - Automated creation of a set of rules and axioms based on input.
• * Evolutionary computing - Biologically inspired algorithms.
• Natural language processing - Building systems and algorithms that analyze, understand, and generate natural languages.
• Robotics – Algorithms for controlling the behaviour of robots.

  Communication 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, algebra 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.
• 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.
• Information visualization – Methods for representing and displaying abstract data to facilitate human interaction for exploration and understanding.

  Concurrent, parallel, and distributed systems

• Parallel computing - The theory and practice of simultaneous computation; data safety in any multitasking or multithreaded environment.
• Concurrency – 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.
• Structured Storage - non-relational databases such as NoSQL 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 and Computational biology – 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 design.
• Software engineering – The principles and practice of designing, developing, and testing programs, as well as proper engineering practices.
• 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.
• Human–computer interaction – The study and design of computer interfaces that people use.
• Reverse engineering – The application of the scientific method to the understanding of arbitrary existing software.

  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.
• * List of unsolved problems in computer science
• Computational complexity theory – Fundamental bounds on classes of computations.
• Quantum computing theory – Explores computational models involving quantum superposition of bits.

  History

• History of computer science
• List of pioneers in computer science

  Professions

• Programmer
• Teacher/Professor
• Software engineer
• Software architect
• Software developer
• Software tester
• Hardware engineer
• Data analyst
• Interaction designer
• Network administrator

  Data and data structures

• Data structure
• Data type
• Associative array and Hash table
• Array
• List
• Tree
• String
• Matrix
• Database

  Programming paradigms

• Imperative programming/Procedural programming
• Functional programming
• Logic programming
• Object oriented programming
• * Class
• * Inheritance
• * Object