The Computer Science Handbook

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The Handbook of Computer Science
Computer science or computing science 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.

Major achievements
Despite its short history as a formal academic discipline, computer science has made a number of fundamental contributions to science and society. These include:
1. The start of the "digital revolution," which includes the current Information Age and the Internet.
2. A formal definition of computation and computability, and proof that there are computationally unsolvable and intractable problems.
3. The concept of a programming language, a tool for the precise expression of methodological information at various levels of abstraction.
4. In cryptography, breaking the Enigma machine was an important factor contributing to the Allied victory in World War II.
5. Scientific computing enabled advanced study of the mind, and mapping the human genome became possible with Human Genome Project. Distributed computing projects such as Folding@home explore protein folding.
6. 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.