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Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. [4] Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5]
Statistics is the theory and application of mathematics to the scientific method including hypothesis generation, experimental design, sampling, data collection, data summarization, estimation, prediction and inference from those results to the population from which the experimental sample was drawn.
A theory of statistical inference was developed by Charles S. Peirce in "Illustrations of the Logic of Science" (1877–1878) and "A Theory of Probable Inference" (1883), two publications that emphasized the importance of randomization-based inference in statistics.
Introduced the theory of data-flow analysis in optimizing compilers (global expression optimization, Kildall's method). Worked on instruction set emulators ( INTERP ), found an innovative software relocation method ( page boundary relocation ), and laid the foundation to the concepts of binary recompilation ( XLT86 ).
Coding theory is one of the most important and direct applications of information theory. It can be subdivided into source coding theory and channel coding theory. Using a statistical description for data, information theory quantifies the number of bits needed to describe the data, which is the information entropy of the source.
The following dates are approximations. 700 BC: Pythagoras's theorem is discovered by Baudhayana in the Hindu Shulba Sutras in Upanishadic India. [18] However, Indian mathematics, especially North Indian mathematics, generally did not have a tradition of communicating proofs, and it is not fully certain that Baudhayana or Apastamba knew of a proof.
According to Neil Sloane, an AT&T Fellow who co-edited Shannon's large collection of papers in 1993, the perspective introduced by Shannon's communication theory (now called "information theory") is the foundation of the digital revolution, and every device containing a microprocessor or microcontroller is a conceptual descendant of Shannon's ...
The Fourth Paradigm: Data-intensive Scientific Discovery is a 2009 anthology of essays on the topic of data science.Editors Tony Hey, Kristin Michele Tolle, and Stewart Tansley claim in the book's description that it presents the first broad look at the way that increasing use of data is bringing a paradigm shift to the nature of science.