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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]
The German Statistik, first introduced by Gottfried Achenwall (1749), originally designated the analysis of data about the state, signifying the "science of state" (then called political arithmetic in English). It acquired the meaning of the collection and classification of data generally in the early 19th century.
Cecilia R. Aragon – invented treap, human-centered data science; Bruce Arden – programming language compilers (GAT, Michigan Algorithm Decoder (MAD)), virtual memory architecture, Michigan Terminal System (MTS) Kevin Ashton – pioneered and named The Internet of Things at M.I.T. Sanjeev Arora – PCP theorem
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 ).
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.
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.
The history of scientific method considers changes in the methodology of scientific inquiry, as distinct from the history of science itself. The development of rules for scientific reasoning has not been straightforward; scientific method has been the subject of intense and recurring debate throughout the history of science, and eminent natural philosophers and scientists have argued for the ...
Work on Machine learning shifts from a knowledge-driven approach to a data-driven approach. Scientists begin creating programs for computers to analyze large amounts of data and draw conclusions – or "learn" – from the results. [2] Support-vector machines (SVMs) and recurrent neural networks (RNNs) become popular. [3]