What Is A Broad Definition Of Data

Interdisciplinary field of study focused on deriving knowledge and insights from data

Information scientific discipline is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to excerpt noesis and insights from noisy, structured and unstructured information,^[1] ^[2] and utilize knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.

Data science is a "concept to unify statistics, data analysis, informatics, and their related methods" in lodge to "understand and analyse bodily phenomena" with data.^[3] It uses techniques and theories drawn from many fields within the context of mathematics, statistics, informatics, information science, and domain noesis.^[4] However, information scientific discipline is different from computer science and informatics. Turing Honour winner Jim Gray imagined data science as a "quaternary paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the touch of information technology" and the data deluge.^[v] ^[6] A data scientist is someone who creates programming code and combines it with statistical cognition to create insights from data.^[7]

Foundations [edit]

Information science is an interdisciplinary field focused on extracting knowledge from data sets, which are typically large (see big data), and applying the knowledge and actionable insights from data to solve problems in a wide range of application domains.^[viii] The field encompasses preparing data for assay, formulating information science bug, analyzing data, developing data-driven solutions, and presenting findings to inform high-level decisions in a broad range of application domains. As such, it incorporates skills from computer science, statistics, data science, mathematics, data visualization, data visualization, information sonification, data integration, graphic design, complex systems, communication and business.^[nine] ^[10] Statistician Nathan Yau, drawing on Ben Fry, also links data science to human–computer interaction: users should be able to intuitively control and explore information.^[xi] ^[12] In 2015, the American Statistical Clan identified database direction, statistics and motorcar learning, and distributed and parallel systems as the three emerging foundational professional communities.^[13]

Relationship to statistics [edit]

Many statisticians, including Nate Silver, have argued that data science is non a new field, merely rather another name for statistics.^[14] Others argue that data science is singled-out from statistics because it focuses on problems and techniques unique to digital information.^[15] Vasant Dhar writes that statistics emphasizes quantitative data and description. In dissimilarity, data scientific discipline deals with quantitative and qualitative data (e.g. images) and emphasizes prediction and action.^[16] Andrew Gelman of Columbia University has described statistics as a nonessential function of data scientific discipline.^[17] Stanford professor David Donoho writes that data scientific discipline is not distinguished from statistics by the size of datasets or use of computing, and that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data-science program. He describes information science every bit an applied field growing out of traditional statistics.^[18] In summary, data science can be therefore described as an applied branch of statistics.

Etymology [edit]

Early usage [edit]

In 1962, John Tukey described a field he chosen "data analysis", which resembles modern data science.^[eighteen] In 1985, in a lecture given to the Chinese University of Sciences in Beijing, C. F. Jeff Wu used the term "data scientific discipline" for the first time as an alternative proper noun for statistics.^[19] Afterwards, attendees at a 1992 statistics symposium at the University of Montpellier II acknowledged the emergence of a new discipline focused on data of diverse origins and forms, combining established concepts and principles of statistics and information assay with computing.^[20] ^[21]

The term "data scientific discipline" has been traced back to 1974, when Peter Naur proposed information technology as an alternative name for information science.^[22] In 1996, the International Federation of Nomenclature Societies became the starting time conference to specifically feature information science every bit a topic.^[22] Still, the definition was nonetheless in flux. After the 1985 lecture in the Chinese University of Sciences in Beijing, in 1997 C. F. Jeff Wu again suggested that statistics should be renamed data scientific discipline. He reasoned that a new proper name would help statistics shed inaccurate stereotypes, such every bit being synonymous with accounting, or limited to describing data.^[23] In 1998, Hayashi Chikio argued for information scientific discipline as a new, interdisciplinary concept, with three aspects: information design, drove, and assay.^[21]

During the 1990s, popular terms for the procedure of finding patterns in datasets (which were increasingly large) included "cognition discovery" and "data mining".^[24] ^[22]

Modern usage [edit]

The modern conception of data science every bit an contained discipline is sometimes attributed to William S. Cleveland.^[25] In a 2001 newspaper, he advocated an expansion of statistics across theory into technical areas; because this would significantly change the field, it warranted a new proper name.^[24] "Data science" became more widely used in the next few years: in 2002, the Commission on Data for Scientific discipline and Engineering launched Data Scientific discipline Journal. In 2003, Columbia Academy launched The Periodical of Data Science.^[24] In 2014, the American Statistical Association's Section on Statistical Learning and Information Mining changed its name to the Department on Statistical Learning and Data Scientific discipline, reflecting the dominant popularity of data scientific discipline.^[26]

The professional person title of "information scientist" has been attributed to DJ Patil and Jeff Hammerbacher in 2008.^[27] Though it was used by the National Science Board in their 2005 report "Long-Lived Digital Data Collections: Enabling Inquiry and Education in the 21st Century", it referred broadly to any cardinal office in managing a digital data collection.^[28]

There is still no consensus on the definition of data science, and it is considered by some to exist a buzzword.^[29] Large information is a related marketing term.^[30] Data scientists are responsible for breaking downward big data into usable information and creating software and algorithms that aid companies and organizations determine optimal operations.^[31]

References [edit]

^ Dhar, V. (2013). "Data science and prediction". Communications of the ACM. 56 (12): 64–73. doi:x.1145/2500499. S2CID 6107147. Archived from the original on 9 November 2014. Retrieved ii September 2015.
^ Jeff Leek (12 December 2013). "The central word in "Information Scientific discipline" is not Information, it is Science". Simply Statistics. Archived from the original on 2 January 2014. Retrieved i January 2014.
^ Hayashi, Chikio (ane Jan 1998). "What is Data Science? Fundamental Concepts and a Heuristic Example". In Hayashi, Chikio; Yajima, Keiji; Bock, Hans-Hermann; Ohsumi, Noboru; Tanaka, Yutaka; Baba, Yasumasa (eds.). Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organisation. Springer Nihon. pp. xl–51. doi:ten.1007/978-4-431-65950-1_3. ISBN9784431702085.
^ Cao, Longbing (29 June 2017). "Information Science: A Comprehensive Overview". ACM Calculating Surveys. l (iii): 43:1–43:42. doi:ten.1145/3076253. ISSN 0360-0300.
^ Tony Hey; Stewart Tansley; Kristin Michele Tolle (2009). The Quaternary Paradigm: Data-intensive Scientific Discovery. Microsoft Inquiry. ISBN978-0-9825442-0-four. Archived from the original on 20 March 2017.
^ Bong, G.; Hey, T.; Szalay, A. (2009). "Computer Science: Beyond the Information Deluge". Science. 323 (5919): 1297–1298. doi:x.1126/science.1170411. ISSN 0036-8075. PMID 19265007. S2CID 9743327.
^ Davenport, Thomas H.; Patil, D. J. (October 2012). "Data Scientist: The Sexiest Chore of the 21st Century". Harvard Business Review. 90 (ten): 70–76, 128. PMID 23074866. Retrieved 18 January 2016.
^ "About Data Scientific discipline". Data Scientific discipline Association . Retrieved 3 April 2020.
^ "1. Introduction: What Is Data Science?". Doing Data Science [Book]. O'Reilly. Retrieved 3 April 2020.
^ "the three sexy skills of data geeks". k.eastward.driscoll: data utopian. 27 May 2009. Retrieved 3 Apr 2020.
^ Yau, Nathan (four June 2009). "Rise of the Data Scientist". FlowingData . Retrieved 3 April 2020.
^ "Basic Example". benfry.com . Retrieved 3 April 2020.
^ "ASA Statement on the Part of Statistics in Data Science". AMSTATNEWS. American Statistical Association. 1 Oct 2015. Archived from the original on 20 June 2019. Retrieved 29 May 2019.
^ "Nate Silvery: What I need from statisticians". Statistics Views . Retrieved 3 Apr 2020.
^ "What's the Difference Between Information Science and Statistics?". Priceonomics . Retrieved 3 April 2020.
^ Vasant Dhar (1 December 2013). "Information science and prediction". Communications of the ACM. 56 (12): 64–73. doi:10.1145/2500499. S2CID 6107147.
^ "Statistics is the least important office of data science « Statistical Modeling, Causal Inference, and Social Science". statmodeling.stat.columbia.edu . Retrieved 3 April 2020.
^ ^a ^b Donoho, David (eighteen September 2015). "50 years of Data Science" (PDF) . Retrieved 2 April 2020.
^ Wu, C. F. Jeff (1986). "Futurity directions of statistical research in Cathay: a historical perspective" (PDF). Application of Statistics and Management. 1: 1–7. Retrieved 29 November 2020.
^ Escoufier, Yves; Hayashi, Chikio; Fichet, Bernard, eds. (1995). Information science and its applications. Tokyo: Academic Printing/Harcourt Brace. ISBN0-12-241770-iv. OCLC 489990740.
^ ^a ^b Murtagh, Fionn; Devlin, Keith (2018). "The Evolution of Information Science: Implications for Pedagogy, Employment, Research, and the Data Revolution for Sustainable Development". Large Data and Cognitive Computing. ii (two): 14. doi:10.3390/bdcc2020014.
^ ^a ^b ^c CaoLongbing (29 June 2017). "Data Science". ACM Calculating Surveys. 50 (three): 1–42. arXiv:2007.03606. doi:ten.1145/3076253.
^ Wu, C. F. Jeff. "Statistics=Data Science?" (PDF) . Retrieved 2 April 2020.
^ ^a ^b ^c Press, Gil. "A Very Short History of Data Science". Forbes . Retrieved 3 April 2020.
^ Gupta, Shanti (11 December 2015). "William S. Cleveland". Retrieved two Apr 2020.
^ Talley, Jill (1 June 2016). "ASA Expands Telescopic, Outreach to Foster Growth, Collaboration in Data Science". Amstat News. American Statistical Association.
^ Davenport, Thomas H.; Patil, D. J. (1 October 2012). "Data Scientist: The Sexiest Task of the 21st Century". Harvard Concern Review. No. October 2012. ISSN 0017-8012. Retrieved 3 April 2020.
^ "US NSF – NSB-05-40, Long-Lived Digital Data Collections Enabling Research and Didactics in the 21st Century". www.nsf.gov . Retrieved 3 Apr 2020.
^ Press, Gil. "Data Science: What's The One-half-Life of a Buzzword?". Forbes . Retrieved three April 2020.
^ Pham, Peter. "The Impacts of Big Information That You lot May Not Accept Heard Of". Forbes . Retrieved iii Apr 2020.
^ Martin, Sophia (20 September 2019). "How Data Scientific discipline will Impact Future of Businesses?". Medium . Retrieved 3 April 2020.