![]() ![]() He reasoned that a new name would help statistics shed inaccurate stereotypes, such as being synonymous with accounting or limited to describing data. Jeff Wu again suggested that statistics should be renamed data science. After the 1985 lecture at the Chinese Academy of Sciences in Beijing, in 1997 C. F. However, the definition was still in flux. In 1996, the International Federation of Classification Societies became the first conference to specifically feature data science as a topic. The term "data science" has been traced back to 1974, when Peter Naur proposed it as an alternative name to computer science. Later, attendees at a 1992 statistics symposium at the University 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. Jeff Wu used the term "data science" for the first time as an alternative name for statistics. In 1985, in a lecture given to the Chinese Academy of Sciences in Beijing, C. F. In 1962, John Tukey described a field he called "data analysis", which resembles modern data science. He describes data science as an applied field growing out of traditional statistics. Stanford professor David 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. Andrew Gelman of Columbia University has described statistics as a non-essential part of data science. 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. Vasant Dhar writes that statistics emphasizes quantitative data and description. Others argue that data science is distinct from statistics because it focuses on problems and techniques unique to digital data. ![]() Many statisticians, including Nate Silver, have argued that data science is not a new field, but rather another name for statistics. In 2015, the American Statistical Association identified database management, statistics and machine learning, and distributed and parallel systems as the three emerging foundational professional communities. Statistician Nathan Yau, drawing on Ben Fry, also links data science to human–computer interaction: users should be able to intuitively control and explore data. As such, it incorporates skills from computer science, statistics, information science, mathematics, data visualization, information visualization, data sonification, data integration, graphic design, complex systems, communication and business. The field encompasses preparing data for analysis, formulating data science problems, analyzing data, developing data-driven solutions, and presenting findings to inform high-level decisions in a broad range of application domains. Foundations ĭata science is an interdisciplinary field focused on extracting knowledge from typically large data sets and applying the knowledge and insights from that data to solve problems in a wide range of application domains. Ī data scientist is a professional who creates programming code and combines it with statistical knowledge to create insights from data. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science ( empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge. However, data science is different from computer science and information science. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. ĭata science is a "concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. ĭata science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). ![]() The existence of Comet NEOWISE (here depicted as a series of red dots) was discovered by analyzing astronomical survey data acquired by a space telescope, the Wide-field Infrared Survey Explorer.ĭata science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. ![]()
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