data science

 Data science is a "concept to unify statistics , data analysis , informatics  , and their related methods" in order to "understand and analyze actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics ,computer science , statistics , information science  , and domain knowledge . However, data science is different from computer science and information science. Turning 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 . 

A data scientist is someone who creates programming code, and combines it with statistical knowledge to create insights from data.

Data 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. 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. As such, it incorporates skills from computer science, statistics, information science, mathematics, information visualization , data sonification , data integration, graphic design, complex system , communication and business. 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. In 2015, the American statistical Association  identified database management , statistics and machine learning , and distributed and parallel system  as the three emerging foundational professional communities.

Many statisticians, including Nate silver , have argued that data science is not a new field, but rather another name for statistics. Others argue that data science is distinct from statistics because it focuses on problems and techniques unique to digital data. Vasant Dhar  writes that statistics emphasizes quantitative data and description. In contrast, data science deals with quantitative and qualitative data (e.g. images) and emphasizes prediction and action. Andrew Gelman  of Columbia  University  has described statistics as a nonessential part of data science. 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. He describes data science as an applied field growing out of traditional statistics. In summary, data science can be therefore described as an applied branch of statistics.

The modern conception of data science as an independent discipline is sometimes attributed to William S. Cleveland . In a 2001 paper, he advocated an expansion of statistics beyond theory into technical areas; because this would significantly change the field, it warranted a new name. "Data science" became more widely used in the next few years: in 2002, the Committee  on data for Science and technology  launched Data Science Journal. In 2003, Columbia University launched The Journal of Data Science. In 2014, the American 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.

The professional title of "data scientist" has been attributed to Dj patil and Jeff hammerbacher  in 2008. Though it was used by the National 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 digital data collection.

There is still no consensus on the definition of data science and it is considered by some to be a buzzword. Big data  is a related marketing term. Data scientists are responsible for breaking down big data into usable information and creating software and algorithms that help companies and organizations determine optimal operations.