Data scientists use a variety of tools and techniques from computer science, statistics, and domain expertise to analyze and interpret data. They often work with large and complex data sets, and use machine learning and other advanced analytical methods to extract insights and make predictions.
Data science is interdisciplinary, and data scientists may come from diverse backgrounds, including computer science, statistics, physics, mathematics, and business. They may work in a variety of industries, including finance, healthcare, marketing, and technology, to solve real-world problems and make data-driven decisions.
Data 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.Data science is a "concept to unify statistics, data analysis, informatics, and their related methods" in order to "understand and analyse 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.[4] However, data science is different from computer science and information science. 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. A data scientist is someone who creates programming code and combines it with statistical knowledge to create insights from data.
FOUNDATION: Data 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.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, data
visualization, information visualization, data sonification,
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. from images, text, sensors, transactions or customer
information, etc) 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.