Data science is the study of the generalizable extraction of knowledge from data, yet the keyword is science.
It incorporates varying elements and builds on techniques and theories from many fields, including signal processing, mathematics, probability models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and learning, visualization, uncertainty modeling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products.
The subject is not restricted to only big data, although the fact that data is scaling up makes big data an important aspect of data science. Another key ingredient that boosted the practice and applicability of data science is the development of machine learning - a branch of artificial intelligence - which is used to uncover patterns from data and develop practical and usable predictive models. A practitioner of data science is called a data scientist.
Data scientists solve complex data problems by employing deep expertise in some scientific discipline. It is generally expected that data scientists be able to work with various elements of mathematics, statistics and computer science, although expertise in these subjects is not required. However, a data scientist is most likely to be an expert in only one or two of these disciplines and proficient in another two or three. Therefore, data science is practiced as a team, where the members of the team have a variety of expertise.
Data scientists use the ability to find and interpret rich data sources, manage large amounts of data despite hardware, software and bandwidth constraints, merge data sources together, ensure consistency of data-sets, create visualizations to aid in understanding data, build mathematical models using the data, present and communicate the data insights/findings to specialists and scientists in their team and if required to a non-expert audience.
Data science techniques affect research in many domains, including the biological sciences, medical informatics, health care, social sciences, and the humanities. It heavily influences economics, business, and finance. From the business perspective, data science is an integral part of competitive intelligence, a newly emerging field that encompasses a number of activities, such as data mining and data analysis.
As an interdisciplinary subject, data science draws scientific inquiry from a broad range of academic subject areas, mostly related to the hard sciences.
It incorporates varying elements and builds on techniques and theories from many fields, including signal processing, mathematics, probability models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and learning, visualization, uncertainty modeling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products.
The subject is not restricted to only big data, although the fact that data is scaling up makes big data an important aspect of data science. Another key ingredient that boosted the practice and applicability of data science is the development of machine learning - a branch of artificial intelligence - which is used to uncover patterns from data and develop practical and usable predictive models. A practitioner of data science is called a data scientist.
Data scientists solve complex data problems by employing deep expertise in some scientific discipline. It is generally expected that data scientists be able to work with various elements of mathematics, statistics and computer science, although expertise in these subjects is not required. However, a data scientist is most likely to be an expert in only one or two of these disciplines and proficient in another two or three. Therefore, data science is practiced as a team, where the members of the team have a variety of expertise.
Data scientists use the ability to find and interpret rich data sources, manage large amounts of data despite hardware, software and bandwidth constraints, merge data sources together, ensure consistency of data-sets, create visualizations to aid in understanding data, build mathematical models using the data, present and communicate the data insights/findings to specialists and scientists in their team and if required to a non-expert audience.
Data science techniques affect research in many domains, including the biological sciences, medical informatics, health care, social sciences, and the humanities. It heavily influences economics, business, and finance. From the business perspective, data science is an integral part of competitive intelligence, a newly emerging field that encompasses a number of activities, such as data mining and data analysis.
As an interdisciplinary subject, data science draws scientific inquiry from a broad range of academic subject areas, mostly related to the hard sciences.
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