Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine).[3] 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]
Vasant Dhar writes that statistics emphasizes quantitative data and description. In contrast, data science deals with quantitative and qualitative data (e.g., from images, text, sensors, transactions, customer information, etc.) and emphasizes prediction and action.[12]Andrew Gelman ofColumbia University has described statistics as a non-essential part of data science.[13] Stanford professorDavid Donoho writes that data science 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 data science as an applied field growing out of traditional statistics.[14]
In 1962,John Tukey described a field he called "data analysis", which resembles modern data science.[14] In 1985, in a lecture given to the Chinese Academy of Sciences in Beijing,C. F. Jeff Wu used the term "data science" for the first time as an alternative name for statistics.[15] Later, attendees at a 1992 statistics symposium at theUniversity of Montpellier II acknowledged the emergence of a new discipline focused on data of various origins and forms, combining established concepts and principles of statistics and data analysis with computing.[16][17]
The term "data science" has been traced back to 1974, whenPeter Naur proposed it as an alternative name to computer science.[6] In 1996, the International Federation of Classification Societies became the first conference to specifically feature data science as a topic.[6] However, the definition was still in flux. After the 1985 lecture at the Chinese Academy of Sciences in Beijing, in 1997C. F. Jeff Wu again suggested that statistics should be renamed data science. He reasoned that a new name would help statistics shed inaccurate stereotypes, such as being synonymous with accounting or limited to describing data.[18] In 1998, Hayashi Chikio argued for data science as a new, interdisciplinary concept, with three aspects: data design, collection, and analysis.[17]
In 2012, technologistsThomas H. Davenport andDJ Patil declared "Data Scientist: The Sexiest Job of the 21st Century",[19] a catchphrase that was picked up even by major-city newspapers like theNew York Times[20] and theBoston Globe.[21] A decade later, they reaffirmed it, stating that "the job is more in demand than ever with employers".[22]
The modern conception of data science as an independent discipline is sometimes attributed toWilliam S. Cleveland.[23] In 2014, theAmerican Statistical Association's Section on Statistical Learning and Data Mining changed its name to the Section on Statistical Learning and Data Science, reflecting the ascendant popularity of data science.[24]
The professional title of "data scientist" has been attributed toDJ Patil andJeff Hammerbacher in 2008.[25] Though it was used by theNational Science Board in their 2005 report "Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century", it referred broadly to any key role in managing a digitaldata collection.[26]
Data analysis typically involves working with structured datasets to answer specific questions or solve specific problems. This can involve tasks such asdata cleaning anddata visualization to summarize data and develop hypotheses about relationships betweenvariables.Data analysts typically use statistical methods to test these hypotheses and draw conclusions from the data.[27]
A cloud-based architecture for enabling big data analytics. Data flows from various sources, such aspersonal computers,laptops, andsmart phones, through cloud services for processing and analysis, finally leading to variousbig data applications.
Cloud computing can offer access to large amounts of computational power andstorage.[30] Inbig data, where volumes of information are continually generated and processed, these platforms can be used to handle complex and resource-intensive analytical tasks.[31]
Some distributed computing frameworks are designed to handle big data workloads. These frameworks can enable data scientists to process and analyze large datasets in parallel, which can reduce processing times.[32]
Data science involves collecting, processing, and analyzing data which often includes personal and sensitive information. Ethical concerns include potential privacy violations, bias perpetuation, and negative societal impacts.[33][34]
Machine learning models can amplify existing biases present in training data, leading to discriminatory or unfair outcomes.[35][36]
^Armbrust, Michael; Xin, Reynold S.; Lian, Cheng; Huai, Yin; Liu, Davies; Bradley, Joseph K.; Meng, Xiangrui; Kaftan, Tomer; Franklin, Michael J.; Ghodsi, Ali; Zaharia, Matei (27 May 2015)."Spark SQL: Relational Data Processing in Spark".Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM. pp. 1383–1394.doi:10.1145/2723372.2742797.ISBN978-1-4503-2758-9.
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