Data Science

Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply 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 order to "understand and analyze actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. 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.

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 integration, graphic design, complex systems, 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 systems as the three emerging foundational professional communities.

Relationship to statistics

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 and data scientist Vincent Granville have 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 very quickly becoming a vital tool for businesses and companies of all sizes. The availability and interpretation of big data has altered the business models of old industries and enabled the creation of new ones. Data-driven businesses are worth $1.2 trillion collectively in 2020, an increase from $333 billion in the year 2015. 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. As big data continues to have a major impact on the world, data science does as well due to the close relationship between the two.