In general terms, Data Science is the extraction of knowledge from data, which is a continuation of the field data mining and predictive analytics, also known as knowledge discovery and data mining (KDD). It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information theory and information technology, including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition and learning, visualization, predictive analytics, uncertainty modeling, data warehousing, data compression, computer programming, and high performance computing. Methods that scale to Big Data are of particular interest in data science, although the discipline is not generally considered to be restricted to such data. The development of machine learning, a branch of artificial intelligence used to uncover patterns in data from which predictive models can be developed, has enhanced the growth and importance of data science.
Data scientists apply expertise in data preparation, statistics, and machine learning to investigate complex problems in many various domains, such as marketing optimization, fraud detection, setting public policy, etc. These areas represent great breadth and diversity of knowledge, and a data scientist will most likely be expert in only one or at most two of these areas and merely proficient in the other(s). Therefore a data scientist typically works as part of a team whose other members have knowledge and skills which complement their own.
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; ensure consistency of datasets; create visualizations to aid in understanding data; build mathematical models using the data; and present and communicate the data insights/findings (preferably actionable insights) to specialists and scientists in their team and if required to a non-technical 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.
 |
| Data science |
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; ensure consistency of datasets; create visualizations to aid in understanding data; build mathematical models using the data; and present and communicate the data insights/findings (preferably actionable insights) to specialists and scientists in their team and if required to a non-technical 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.
0 comments:
Post a Comment